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REVIEW article

Front. Aging Neurosci., 13 November 2018
Sec. Alzheimer's Disease and Related Dementias
This article is part of the Research Topic Risk Factors for Alzheimer's Disease View all 14 articles

The Early Events That Initiate β-Amyloid Aggregation in Alzheimer’s Disease

  • Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China

Alzheimer’s disease (AD) is characterized by the development of amyloid plaques and neurofibrillary tangles (NFTs) consisting of aggregated β-amyloid (Aβ) and tau, respectively. The amyloid hypothesis has been the predominant framework for research in AD for over two decades. According to this hypothesis, the accumulation of Aβ in the brain is the primary factor initiating the pathogenesis of AD. However, it remains elusive what factors initiate Aβ aggregation. Studies demonstrate that AD has multiple causes, including genetic and environmental factors. Furthermore, genetic factors, many age-related events and pathological conditions such as diabetes, traumatic brain injury (TBI) and aberrant microbiota also affect the aggregation of Aβ. Here we provide an overview of the age-related early events and other pathological processes that precede Aβ aggregation.

Introduction

Alzheimer’s disease (AD) is the most frequent cause of dementia in the elderly. By 2030, the world’s AD population will reach more than 70 million (McDade and Bateman, 2017). But no effective treatments can prevent, halt, or reverse AD so far. Pathologically, AD is characterized by the assemblies of extracellular β-amyloid (Aβ) plaques and cytoplasmic neurofibrillary tangles (NFTs) consisting of the microtubule-associated protein tau (Braak and Braak, 1991).

Aβ is generated by the sequential proteolytic cleavage of the much larger amyloid precursor protein (APP) by β-secretase and γ-secretase. In contrast, cleavage of APP by α-secretase precludes Aβ formation. The exact physiological function of Aβ remains unknown. In AD brain, Aβ adopts a highly ordered structure known as cross-β spine, or amyloid (Lührs et al., 2005). Many studies have shown a causal relationship between Aβ and the pathogenesis of AD. The NFTs mainly consist of aggregated tau that bears abnormal posttranslational modifications, including hyperphosphorylation, acetylation, ubiquitylation, truncation and so on. Compared to Aβ, tau deposits correlates better with the degree of cognitive impairment (Goedert and Spillantini, 2006). It is believed that tau functions primarily to stabilize microtubes, and its aggregation in AD causes deficits through a loss-of-function mechanism (Morris et al., 2011). However, recent studies have shown that tau may promote or enhance excitatory neurotransmission by modulating the distribution of synaptic activity-related signaling molecules (Morris et al., 2011).

Currently, the predominant framework of AD research is the amyloid hypothesis. According to the amyloid hypothesis (Hardy and Selkoe, 2002), Aβ is the pathological factor that initiates the onset and progression of AD. Thus, Aβ is proposed to be the target of primary prevention trials (McDade and Bateman, 2017). However, what initially triggers the aggregation and accumulation of Aβ in AD is unclear. To stop the disease before it starts, we should find the earlier events that precede Aβ aggregation. This review highlights the relationship between risk factors of AD and Aβ aggregation to bring us closer to a comprehensive understanding of the pathogenesis of AD and prevention potential of early events in AD.

Assembly of Aβ

Aβ is 40–42 amino acids in length and is formed by proteolytic cleavage of the much larger APP. APP is a transmembrane protein with a single membrane-spanning domain (Glenner and Wong, 1984a,b; Masters et al., 1985), which may have a trophic function (Thornton et al., 2006; Weyer et al., 2011). APP can be cleaved by β-secretase and γ-secretase generating the N terminus and the C terminus of Aβ respectively. During the amyloidogenic process, APP is first cleaved by β-secretase to release the C-terminal fragment (C99), and then C99 is further cleaved by γ-secretase to generate Aβ. In contrast, cleaved by α-secretase precluding Aβ formation. C99 is cleaved at different sites by γ-secretase, resulting in different Aβ profiles (Acx et al., 2014). The major species of Aβ profiles are 40 or 42 amino acids long, and Aβ42 is more aggregation-prone and believed to be the toxic building block of Aβ assemblies.

Aβ adopts a highly ordered structure known as cross-β spine or amyloid (Lührs et al., 2005). The formation of Aβ fibrils can be divided into three phase including nucleation phase, elongation phase and stationary phase (Iadanza et al., 2018). In nucleation phase, oligomeric Aβ forms a nucleus, which can recruit other monomers. As fibrils grow, they can shatter, producing new aggregation-prone species to elongate the fibril. Until nearly all free monomer is converted into a fibrillar form, a variety of insoluble fibrils, oligomers and soluble monomer achieve dynamic balance in the stationary phase. Oligomers are considered to be more pathogenic than mature fiber. However, which Aβ assemblies are most pathogenic is unresolved (Benilova et al., 2012). The fibrils also associate with each other, with other proteins, and with non-proteinaceous factors to form the plaques (Stewart et al., 2017).

Aβ plaques first develop in one or more parts of the basal temporal and orbitofrontal neocortex (Braak and Braak, 1991; Thal et al., 2002; Braak and Del Trecidi, 2015). They were then observed throughout the neocortex, in the hippocampal formation, amygdala, diencephalon and basal ganglia. In severe AD cases, Aβ brain plaque also appears in the mesencephalon, lower brainstem and cerebellar cortex.

Multiple lines of evidence indicate that APP and Aβ contribute causally to the pathogenesis of AD. However, the function of Aβ remains confused. There is evidence that Aβ regulates neuronal and synaptic activity, and that Aβ accumulation in the brain leads to a combination of abnormal network activity and synaptic depression, which can result in excitotoxicity (Palop and Mucke, 2010). Recent studies suggest that Aβ is an antimicrobial peptide, which may play a protective role in innate immunity, and infectious or sterile inflammatory stimuli may drive amyloidosis (Kumar et al., 2016).

The Amyloid Hypothesis and the Prion Hypothesis

The Amyloid Hypothesis

In 1992, Hardy and Higgins (1992) postulated that “Aβ … is the causative agent in AD pathology and that NFTs, cell loss, vascular damage and dementia follow as a direct result of this deposition.” This hypothesis has dominated the AD field for more than two decades. A variety of clinical and laboratory evidence supports the hypothesis. The most reliable data supporting the initiator role of Aβ come from genetic studies. The mutations of APP, presenilin-1 (PS1), and PS2, which are involved in Aβ production, cause the autosomal dominant familial AD (fAD; Bettens et al., 2013). Besides, duplication of the APP locus on chromosome 21 in Down syndrome cause age-related dementia with brain parenchymal Aβ deposits (Prasher et al., 1998; Rovelet-Lecrux et al., 2006). Moreover, a rare APP mutation is protective against dementia because it inhibits the production of Aβ and the development of plaques in the brain (Jonsson et al., 2012).

There are also some observations that do not fit easily with the amyloid hypothesis. The main objections can be summed up as the anatomical and temporal discord between Aβ plaque deposition, neuronal death and clinical symptoms in AD (Musiek and Holtzman, 2015). Early neuronal loss regions (entorhinal cortex and hippocampus) are consistent more closely with tau pathology regions than Aβ deposition site (precuneus and frontal lobes), both spatially and temporally (Arriagada et al., 1992; Musiek and Holtzman, 2015). This anatomic disconnection is still not fully explained. However, some studies suggest that the appearance of high-grade cortical tau pathology requires the presence of Aβ aggregation (Price and Morris, 1999; Knopman et al., 2003; Petersen et al., 2006) and tau-mediated toxicity requires trigger from Aβ (West et al., 1994; Gómez-Isla et al., 1996). As for the temporal discrepancy, the neuropathology occurring before symptom onset can be explained as a preclinical AD (Bateman et al., 2012). It is well-accepted that Aβ plays a key role in AD, but it does not exert its effects in a vacuum. The Aβ toxicity involves a complicated network (Musiek and Holtzman, 2015).

The Prion Hypothesis

The amyloid hypothesis cannot adequately explain the progression of Aβ pathology over a long distance. The recent surge of studies shows the misfolded proteins, such as Aβ and tau, have prion-like properties. Therefore, the prion hypothesis was proposed to explain how amyloid aggregates propagate through anatomically connected brain areas. According to the prion hypothesis, Aβ and tau are similar to prion in the cross-β quaternary structure, the mechanism of self-propagation and cell-to-cell transmission, and the ability to form structurally diverse fibrils (strains; Guo and Lee, 2014). The amyloid formation can be divided into two processes, a slow nucleation phase (the aggregation of the protein into seeds) and a growth phase (the growing fibril break to generate and spread new amyloid seeds; Jucker and Walker, 2013). Also, this seeding process could be homologous or heterologous (Morales et al., 2009), which means oligomers composed of one misfolded protein can promote the polymerization of another protein. This process is termed as “cross-seeding,” which may play an essential and yet uncovered role in the origin of AD.

What Initiates Aβ Aggregation?

Genotype of Protein

fAD: APP, PS1, Down Syndrome

Although the cases of fAD account for only less than 1% of total AD, the research of fAD helps us to discover the causative gene defects, including APP and PS1. APP gene is located on chromosome 21. It is well-known that APP gene mutations, duplication of its gene or trisomy of chromosome 21 (Down’s syndrome) cause fAD (Prasher et al., 1998). Thirty-nine missense mutations in the APP gene have been described in individuals from Early-onset fAD (Wang Q. et al., 2015), most of which are inside or surrounds the Aβ area. APP mutations either increase total Aβ production or lead to an increased proportion of Aβ42 (Citron et al., 1992; Suzuki et al., 1994). PS1 or PS2 is the catalytic subunit of the γ-secretase protein complex. Mutations in PS1 are the most frequent cause of fAD. The mutations increase the ratio of Aβ42 to Aβ40, which may result from reduced γ-secretase activity (Citron et al., 1997).

sAD: ApoE, BIN1 and TREM2

Most cases of AD are sporadic. Inherited forms of the ε4 allele of Apolipoprotein E (ApoE4) was identified as a major genetic risk factor for sAD. Furthermore, the bridging integrator 1 (BIN1 or amphiphysin2) is the second most important genetic susceptibility locus in late-onset AD after ApoE4 (Tan et al., 2013). Recently, rare mutations in triggering receptor expressed on myeloid cells (TREM2) has received much attention, because one of its variants, R47H, is reported to increase the risk for LOAD by 2–3 folds (Guerreiro et al., 2013).

ApoE

ApoE is a glycoprotein with a molecular weight of 34.2 kDa (Mahley, 1988), which has three isoforms, ApoE2, ApoE3 and ApoE4, in humans (Mahley and Huang, 2006). ApoE is mainly expressed in brain and liver. Astrocytes and neurons have long been recognized as the primary source of ApoE in the brain (Huang, 2006). The primary role of ApoE is to transport lipids and cholesterol in the body. Besides, ApoE also plays a role in mediating synaptogenesis, synaptic plasticity and neuroinflammation (Holtzman et al., 2012). Corder et al. (1993) reported that subjects with an ApoE ε4 allele had an earlier onset clinical dementia in families with AD. Poirier et al. (1993) further confirmed the association in a case-control study of sporadic AD. This conclusion was supported by a series of other reports (Amouyel et al., 1993; Noguchi et al., 1993; Myers et al., 1996), making the ApoE ε4 allele the most important genetic risk factor for AD. In contrast to APP, PS1 and PS2, the presence of ApoE ε4 is not sufficient to cause the disease. Indeed, despite decades of research, the pathophysiological pathway linking ApoE4 to AD remains unclear. To date, the studies suggest that ApoE4 may promote the pathogenesis of AD via Aβ-dependent and Aβ-independent mechanisms.

Aβ-Dependent Mechanisms. ApoE is associated with the formation of amyloid plaques. Lipid-free ApoE3 and ApoE4 can form stable complexes with Aβ peptides. ApoE4 forms complexes with Aβ more efficiently and rapidly than ApoE3 (Huang and Mahley, 2006). Further studies have shown that ApoE binds to residues 12–28 of Aβ and this binding modulates Aβ accumulation, hence affecting disease progression. Peptides that interrupt ApoE/Aβ binding reduced Aβ-related pathology and cognitive improvements in an APP/PS1 transgenic AD mouse model (Liu et al., 2017). On the other hand, lipidated ApoE3 binds Aβ with higher affinity than ApoE4 (Huang and Mahley, 2006), and further studies (Kim et al., 2009) demonstrate that altering ApoE lipidation changes its ability to mediate Aβ clearance or deposition in the brain. Furthermore, recent data describe a novel signal transduction pathway in neurons whereby ApoE activates a non-canonical MAP kinase cascade that enhances APP transcription and amyloid-β synthesis (Huang et al., 2017).

It has been reported that human ApoE regulates Aβ clearance. ApoE2 and ApoE3 clear Aβ more efficiently than ApoE4 (Bales et al., 1999). Also, a C-terminally truncated ApoE4 was found in AD brain, which inefficiently removes Aβ and acts in concert with Aβ to elicit neuronal and behavioral deficits in transgenic mice (Bien-Ly et al., 2011). Overall, ApoE4 may initiate Aβ accumulation through binding with Aβ and decreasing its clearance. Interesting, Wisniewski et al. (1995) isolated Aβ from senile plaques and found that a carboxyl-terminal fragment of ApoE was co-purified. In vitro, this fragment could form amyloid-like fibrils. The amyloid-like property of ApoE fragment is reminiscent of the cross-seeding hypothesis. Whether the ApoE fragment initiates Aβ aggregation though cross-seeding needs further investigation.

Aβ-Independent Mechanisms. ApoE4 also impairs synaptogenesis and decreases dendritic spine density. This effect is independent of Aβ accumulation (Dumanis et al., 2009; Brodbeck et al., 2011). Besides, the Aβ-independent roles of ApoE4 also include its detrimental effects on neuronal plasticity, aberrant proteolysis that generates neurotoxic fragments, stimulation of Tau phosphorylation and disruption of the cytoskeleton and impairment of mitochondrial function (Huang, 2010).

BIN1

Except for ApoE, some studies sought associations between biologically plausible candidate genes and risk of sAD. Among them, BIN1 gene has been identified as the most important genetic risk locus in LOAD after ApoE. Interestingly, although BIN1 mRNA level was found to be increased in AD brains, the protein levels of the longest isoform of BIN1 was decreased, whereas the levels of the shorter BIN1 isoforms were increased (Chapuis et al., 2013; Holler et al., 2014). BIN1 affects AD risk through various pathways, mainly including tau pathology, APP endocytosis/intracellular trafficking, immune/inflammation of the brain, and calcium transients (Tan et al., 2013). Of those, tau pathology is the most studied aspect. BIN1 can interact with tau (Chapuis et al., 2013), and the decline of BIN1 isoform1 promotes the propagation of tau pathology (Calafate et al., 2016). BIN1 is important in the intercellular trafficking of APP, Aβ, ApoE and BACE1 (Tan et al., 2013; Miyagawa et al., 2016; Ubelmann et al., 2017). Miyagawa et al. (2016) found that depletion of BIN1 impaired endosomal trafficking and lysosomal degradation of BACE1, leading to elevated Aβ production. Ubelmann et al. (2017) also found that BACE1 was trapped in tubules of early endosomes and failed to recycle in axons after BIN1 depletion, eventually resulted in increased Aβ production. However, the precise role of BIN1 in the BACE1 recycling remains speculative.

TREM2

TREM2 is a transmembrane protein of the immunoglobulin superfamily that is expressed in mononuclear phagocytes, including microglial in brain (Colonna and Wang, 2016). The main function of TREM2 is regulating the microglial phagocytosis and response to inflammatory stimulation. And the individuals carrying the TREM2 variant R47H have an increased risk for AD by 2–3 folds (Guerreiro et al., 2013).

TREM2 binds to anionic ligands including phospholipids, bacterial LPS, sulfatides and DNA (Daws et al., 2003; Cannon et al., 2012; Wang Y. et al., 2015). Recently, Aβ (Zhao et al., 2018), clusterin (CLU; Yeh et al., 2016) and ApoE (Atagi et al., 2015) are also reported to bind the extracellular region of TREM2. The binding with Aβ oligomers mediates Aβ degradation and downstream signaling (Zhao et al., 2018). Additionally, R47H variant impairs Aβ binding (Zhao et al., 2018). The binding with CLU and ApoE also mediate uptake of lipoprotein-Aβ complexes by microglia. Uptake of lipoprotein-Aβ complexes was reduced in individuals carrying a TREM2 AD variant, R62H (Yeh et al., 2016).

TREM2 associated with the adaptor proteins DNAX-activation protein 10 (DAP10) and DAP12. TREM2-DAP10-DAP12 signaling modulates the energetic cellular metabolism by activating the mechanistic target of rapamycin (mTOR; Xing et al., 2015). TREM2-deficient microglia showed a metabolic defect (Ulland et al., 2017), which may result in the microglia ineffectively responding to stressful events, such as Aβ toxicity. Furthermore, some study crossed the TREM2-deficient mice with developed Aβ-plaque-driven mice, and they found microglia of TREM2-deficient mice failed to cluster around Aβ plaque (Wang Y. et al., 2015; Wang et al., 2016; Yuan et al., 2016; Mazaheri et al., 2017; Ulland et al., 2017). The clustering of microglia around Aβ plaque was of significance to limit the Aβ plaque spreading and protect surrounding neurons (Condello et al., 2015; Wang et al., 2016; Yuan et al., 2016). The study further found that the lack of TREM2 increased the Aβ plaque burden in the 5XFAD model of AD (Wang Y. et al., 2015). And the areas not covered by microglia had a high degree of neural dystrophy (Condello et al., 2015). Instead, elevated expression of TREM2 reduced neural dystrophy in the 5XFAD model of AD (Ulland et al., 2017; Lee et al., 2018). However, a TREM2 deficiency in APP/PS1 mice led to a dramatic reduction in Aβ plaque burden (Jay et al., 2015). The different outcomes may due to the use of different mouse models.

TREM2 also modulates the expression of activation markers in disease-associated microglia. TREM2-deficiency failed to upregulate some activation genes (Keren-Shaul et al., 2017). The partial defect of microglial activation may contribute to the development of AD. Recently, TREM2 was found to alter the degradative process in microglia. Zhao et al. (2018) show that TREM2 KO microglia cause defective clearance of Aβ by disrupting proteasome function. Conversely, Lee et al. (2018) reported that lysosomal degradation was involved in Aβ clearance.

Except for the gene mentioned above, there are several genes related to LOAD, such as PLD3, ABCA7, CASS4, CD33, CD2AP, CELF1, CLU, CR1, DSG2, EPHA1, FERMT2, HLA-DRB5-DBR1, INPP5D, MS4A, MEF2C, NME8, PICALM, PTK2B, SLC24H4 RIN3, SORL1, ZCWPW1 (Karch and Goate, 2015). But the relationship with Aβ aggregation is not fully clarified.

Age-Related Process

Aging is the primary non-genetic risk for sporadic AD, but little is known about how aging affects Aβ generation. Recent summit addressed seven main hallmarks of the basic aging process (Kennedy et al., 2014), including decreased adaptation to stress, loss of proteostasis, stem cell exhaustion, metabolism, macromolecular damage, unfavorable epigenetic and impaired inflammaging. In this review article, we will focus on the mechanism of age-related pathologic process initiating Aβ aggregation.

Age-Related Neuronal Stress

Several stress-related signaling pathways are related to AD, such as oxidative stress (Arimon et al., 2015) and nitrosative stress (Guix et al., 2012).

Aging is usually accompanied by accumulation of reactive oxygen species (ROS; Finkel and Holbrook, 2000). Generally, ROS function as messenger and are kept at low level. Excessive amounts of ROS accumulation is defined as oxidative stress, which will damage various cell components. In the brain of AD, ROS production and the level of oxidative stress markers are elevated (Krstic and Knuesel, 2013). Furthermore, lipid peroxidation precedes Aβ deposition (Pratico et al., 2001), suggesting the oxidative stress may be an initiator of Aβ pathology. Further study found that lipid peroxidation product 4-hydroxynonenal (4-HNE) elevated γ-secretase activity and Aβ production, resulting in Aβ and neurodegenerative pathologies in AD (Gwon et al., 2012). Another study also found the β-secretase activity was affected by 4-HNE (Arimon et al., 2015). Oxidative stress was also reported to cause pathogenic PS1 conformational change (Wahlster et al., 2013) and induce Aβ aggregation (Siegel et al., 2007). During the oxidative stress process, superoxide anions react with nitric oxide generating peroxynitrite, which causes nitrosative stress. Peroxynitrite-triggered nitrotyrosination is especially relevant in AD (Smith et al., 1997). Guix et al. (2012) found that the secretion of Aβ is enhanced in an in vitro model of neuronal aging. This is associated with an increase in γ-secretase complex formation. Moreover, the age-related nitrative stress promoted the nitrotyrosination of PS1, which is associated with an increased association of the two PS1 fragments, PS1-CTF and PS1-NTF. Further, it raised the Aβ42/Aβ40 ratio.

Repressor element 1-silencing transcription (REST) is also involved in the neuronal stress response. Lu T. et al. (2014) showed that elevated REST levels are associated with the preservation of the ordinary aged people from AD. REST is induced in the aging human brain and regulates a network of genes that mediate cell death, stress resistance and AD pathology. At early stages of AD, REST is lost from the nucleus, resulting in dysregulation of the gene network, including the γ-secretase complex members PS-2 and pen-2, which are implicated in Aβ generation. Interesting, the study also found that aging individuals who harbor substantial AD pathology do not appear to progress to dementia when neuronal REST levels are high.

Age-Related Inflammation

Aging is characterized by chronic, systemic, low-grade inflammation (Franceschi et al., 2017). Inflammation is considered to contribute to and exacerbate AD pathology (Gandy and Heppner, 2013; Sudduth et al., 2013; Sarlus and Heneka, 2017). The inflammation in AD includes microglia and astrocytes dysfunction (Xia et al., 2018). One study using viral mimic to stimulated the systemic immune system found the deposition of APP and its proteolytic fragments (Krstic et al., 2012). Some AD-related chronic disease, such as obesity and type 2 diabetes, will lead to the systemic inflammatory condition, which further increases the risk of AD (Takeda et al., 2010; Thaler et al., 2012).

Microglia play the most important role in inflammatory responses in AD. In physiological condition, microglia remove the apoptotic neurons and prune the synaptic connections to keep the normal development of CNS (Paolicelli et al., 2011; Hong et al., 2016). In response to neuropathological insults, including Aβ, microglia alter its morphology and proliferate, express inflammatory markers, phagocytose dead cells and myelin debris, secrete cytokines and neurotrophic factors (Lue et al., 2010). This process is termed as microglial activation. Furthermore, the activation plays a dual role in AD pathogenesis. On the one hand, microglia increase phagocytosis or clearance of Aβ. On the other hand, the persistent production of Aβ leads to the chronic activation of microglia and drive further amyloid deposition (Hickman et al., 2018). Microglia-Aβ interactions lead to NACHT-, LRR- and pyrin (PYD)-domain-containing protein 3 (NLRP3) inflammasome activation (Heneka et al., 2013). After activation, NLRP3 recruits the adaptor protein apoptosis-associated speck-like protein containing a CARD (ASC), triggering ASC helical fibrillar assembly (Lu A. et al., 2014). ASC rapidly bind to Aβ and increase the formation of amyloid-β oligomers and aggregates by a cross-seeding mechanism (Venegas et al., 2017). Under what conditions the microglia play a positive function, and how can we keep the microglia inflammatory response in check and promote clearance of Aβ without accelerating Aβ aggregation remain to be investigated.

Astrocytes also participate in neuroinflammation in AD. Aβ deposition might be a potent trigger of astroglia activation in AD because the cells surround Aβ plaques (Medeiros and LaFerla, 2013). One study found that reducing astrocyte activation in APP/PS1 mice decreased the amyloid levels and ameliorated cognitive and synaptic function (Furman et al., 2012). The result suggested that astrocyte activation may play a deleterious role in AD. However, it has been confirmed that astrocytes can bind and degrade Aβ (Wyss-Coray et al., 2003). But in some mouse models of AD astrocytes show atrophy (Olabarria et al., 2010), which might result in reduced clearance of Aβ. Overall, inflammation plays a complex but important role in AD.

Age-Related Disturbances in Proteostasis

Aging is related to a functional decline in protein homeostasis (proteostasis) machinery, contributing to the development of protein misfolding in AD. The proteostasis network (PN) maintains protein homeostasis by controlling the levels of functional proteins and preventing their aggregation. The process is achieved by three branches of the PN, including protein synthesis, the chaperone pathways for the remodeling of misfolded proteins and protein disaggregates, and the protein degradation pathways (Hipp et al., 2014). mTOR pathway acts as a central pathway regulating protein synthesis (Saxton and Sabatini, 2017). Numerous studies have shown that inhibition of mTOR activity could extend lifespan among mammalian species, suggesting mTOR may regulate aging (Antikainen et al., 2017). Activation of mTOR has been recognized as a major event causing the onset of AD (Yates et al., 2013). The activation of mTOR downregulates the autophagy, resulting in reduced clearance of Aβ and elevated Aβ accumulation (Nixon, 2013), and elevates the expressions of β-secretase and γ-secretase by AMPK and IGF-1 pathway (Cai et al., 2015). Aβ aggregates are believed to be detoxified by DAF-16 regulated active aggregation activity that assembles small oligomers into large, less toxic structures and HSF-1 mediated disaggregation and degradation activity (Taylor and Dillin, 2011). Also, DAF-16 and HSF-1 and effector molecules such as kinase mTOR can regulate aging.

Diabetes

Numerous epidemiological studies suggest that diabetic patients have a significant risk of developing AD (Arvanitakis et al., 2004; Luchsinger et al., 2005; Fukazawa et al., 2013). T2DM increases the risk of dementia by 50%–150% (Strachan et al., 1997; Biessels et al., 2006). However, the underlying mechanisms with clinical relevance remain to be elucidated. Several mechanisms have been proposed, including insulin and insulin-like growth factor (IGF) resistance, glucose toxicity, oxidative stress and mitochondrial dysfunction. Here we focus on the potential mechanisms by which diabetes affects the initiation of Aβ aggregation.

Insulin and IGF Resistance

Insulin and IGF signaling is involved in synaptic plasticity, and the organization and function of the brain, playing neuromodulatory and neurotrophic roles (Baglietto-Vargas et al., 2016), and hence may play an essential role in learning and memory. Several studies suggest that insulin and IGF resistance participate in AD pathogenesis (Correia et al., 2011; Cholerton et al., 2013). AD patients have lower brain levels of insulin and insulin receptor (IR), and insulin signaling impairments have been documented in postmortem brain and animal models of AD (Steen et al., 2005; Lester-Coll et al., 2006; Moloney et al., 2010). These abnormalities were associated with increased APP mRNA expression (Steen et al., 2005). A novel study reveals that insulin deficiency alters APP processing by increasing the expression of BACE-1 and accompanied by increased translational upregulation of APP through the PERK-eIF2α phosphorylation pathway (Devi et al., 2012). Another study found that insulin resistance might alter APP processing through autophagy activation (Son et al., 2012). Besides, insulin signaling provides a physiological defense mechanism against Aβ oligomer-induced synapse loss through downregulating oligomer binding sites in neurons (De Felice et al., 2009). Insulin also has multiple anti-amyloidogenic effects on human neuronal cells, including preventing the translocation of the APP intracellular domain fragment into the nucleus, increasing the transcription of anti-amyloidogenic proteins, and increasing the α-secretase-dependent APP-processing pathway (Pandini et al., 2013). On other hand, Aβ oligomers can inhibit insulin signaling via the JNK/TNFα pathway (Bomfim et al., 2012), suggesting a positive feed-forward mechanism.

Hyperglycemia

Chronic hyperglycemia characterizes diabetes, and several lines of evidence suggest that hyperglycemia has toxic effects on the brain (Gispen and Biessels, 2000; Kerti et al., 2013). Epidemiological studies show that hyperglycemia individuals had a higher risk of AD and exhibited higher conversion rate from mild cognitive impairment (MCI) to AD, indicating that hyperglycemia might be responsible for AD onset and progression (Crane et al., 2013; Morris et al., 2014). Hyperglycemia may have toxic effects on neurons through several mechanisms, including the direct impact on Aβ, the formation of advanced glycation end products (AGEs; Umegaki, 2014), osmotic insult and oxidative stress.

Hyperglycemia directly raises interstitial fluid (ISF) Aβ levels via altering neuronal activity, which increases Aβ production. KATP channel impairments mediate hyperglycemia-induced neuronal excitability and increased ISF Aβ (Macauley et al., 2015). Hyperglycemia could also directly inhibit APP protein degradation and enhance Aβ production (Yang et al., 2013). Moreover, hyperglycemia accelerates Aβ aggregation through the formation of AGEs. AGEs are generated by a non-enzymatic reaction of glucose, free amino groups, lipids, and nucleic acids (Singh et al., 2001; Sims-Robinson et al., 2010). The receptors for AGEs (RAGE) are highly expressed in both microglia and neurons and are responsible for the pathological consequences (Lue et al., 2001). Aβ is a RAGE ligand, and Aβ-RAGE interaction exaggerates neuronal stress, accumulation of Aβ, impaired learning and memory and neuroinflammation (Chen et al., 2007). Additionally, AD patients with diabetes (ADD) have higher levels of AGEs than non-diabetic AD individuals (Valente et al., 2010). And RAGE is also demonstrated as a cofactor for Aβ-induced neuronal perturbation in an AD model (Arancio et al., 2004). Oxidative stress is also a key player in diabetes and AD (Moreira, 2012). Oxidative stress stimulates APP gene expression and modulates its processing via modulating γ- and β-secretases (Jolivalt et al., 2010; Oda et al., 2010), which contributes to Aβ aggregation.

Cross-Seeding

Islet amyloid polypeptide (IAPP) form β-sheet aggregates in the pancreas in type 2 diabetes (Pillay and Govender, 2013). Interestingly, IAPP deposits are also found in the brain tissue of patients with AD (Jackson et al., 2013). IAPP and Aβ share similar β-sheet secondary structures, and they are 25% identical in amino acid sequence and have a high binding affinity to each other (Andreetto et al., 2010). Emerging evidence indicates that cross-amyloid interactions may play a key role in Aβ aggregation, including the interaction of Aβ-tau (Guo et al., 2006), the Aβ-α-synuclein (Westermark et al., 1996), and the Aβ-IAPP interaction (Andreetto et al., 2010). The potential mechanisms of IAPP-induced AD development include the independently toxic effects, lose the physiological function of soluble IAPP in the brain, and interacting with Aβ (Zhang and Song, 2017). In this part, we will focus on the mechanism of cross-interaction of Aβ and IAPP.

In vitro Evidence. O’Nuallain et al. (2004) first studied the seeding efficiency between Aβ(1–40) and amyloid fibrils produced from IAPP. They found the IAPP fibrils are poor seeds for Aβ(1–40) elongation. But it is difficult to gauge whether low cross-seeding efficiency might be biologically significant. Later, Yang et al. (2013) found both nucleation and fibrillization of Aβ/hIAPP mixtures (1:1) were slower than pure Aβ or pure hIAPP. And they suggest that the cross-seeding of Aβ and hIAPP was less efficient than homologous seeding of pure Aβ or IAPP. However, a study investigating lipid membranes got different results (Seeliger et al., 2012). They found the aggregation kinetics of Aβ/IAPP mixtures was slower than that of hIAPP, but faster than that of Aβ. Hu et al. (2015) found the cross-seeding of Aβ/IAPP led to the retard of peptide aggregation at the nucleation stage due to structural incompatibility between different amyloid aggregates, and the acceleration at final fibrillation stage due to the formation of similar seed structures as templates for promoting cross-seeding. Another in vitro study found that the fibrils of amyloidogenic proteins, including IAPP, functioned as seeds in the Aβ aggregation, and the seeds accelerate the Aβ aggregation pathway. Also, E3, R5, H13, H14 and Q15 of Aβ are common binding regions between the Aβ monomer and the fibrillar seeds including IAPP (Ono et al., 2014). Andreetto et al. (2010) studied the cross- and self-interaction interface of Aβ and IAPP by using membrane-bound peptide arrays and fluorescence titration assays. They identified five short peptide segments of Aβ and IAPP as hot regions of the Aβ-IAPP cross-interaction interface, including Aβ(27–32), Aβ(35–40), Aβ(19–22), IAPP(8–18) and IAPP(22–28), and these peptides also mediate the self-interaction of Aβ and IAPP. They suggested that hetero- and self-association of Aβ and IAPP most likely occur competitively. Overall, the in vitro studies investigated the seeding efficiency and the interaction regions of hetero- and homo-seeding, though some results are contradictory. However, amyloid fibrils in vivo are functionally different from fibrils grown in vitro. So, it is hard to draw biological conclusions from studies only in vitro.

In vivo Evidence. Moreno-Gonzalez et al. (2017) investigated whether IAPP could accelerate Aβ aggregation in vitro and in vivo. They found that the addition of pre-formed IAPP aggregates to Aβ40 monomers can accelerate the misfolding of Aβ compared with unseeded Aβ monomers in vitro, which is consistent with the former studies. Their in vivo study found that the transgenic animals expressing human IAPP and mutant APP showed increased Aβ burden in the hippocampus and cortex compared to AD transgenic mice or AD transgenic animals with type 1 diabetes. Additionally, IAPP colocalizes with amyloid plaques in the transgenic mice express hIAPP and mutant APP. Furthermore, injection of pancreatic IAPP aggregates into the APP-mutant mice resulted in more Aβ burden in the cortex and hippocampus and greater memory impairments than untreated animals. Based on these results, the team provided a hypothesis that IAPP aggregates can accelerate the transformation process of Aβ by recruiting the normal soluble protein into the growing aggregates, thereby accelerating or exacerbating the pathological features of AD.

Microbes

The microbiota is a newly discovered human organ that weighs about 1.5 kg, and contains approximately 90% of the cells of the human body, with a genetic repertoire that exceeds 100%–200% of the remaining organisms (Qin et al., 2010). The microbes inhabit different locations, including gut, skin, nose and vagina. The gut harbors the highest concentrations of microbiota so far and is the best-studied habitat (Scheperjans, 2016). In recent years, many studies found the association between gut microbes disorders and other disorders including diabetes, obesity, arthritis, allergy, cardiovascular and neurodegeneration diseases. The mechanisms through which gut bacteria influence central process include the neurotransmitters synthesized by gut bacteria, the activation of immune system, the metabolites, such as short-chain fatty acids and amyloid (Sherwin et al., 2018). Except for the microbiota, specific microbes including herpes simplex virus type1 (HSV1), Chlamydia pneumoniae and several types of spirochaete which were found in the aging human brain also play a role in the etiology of AD (De Chiara et al., 2012; Balin and Hudson, 2014; Itzhaki, 2014; Miklossy, 2015). In this review article, we will focus on the part of the activation of immune system in the initiation of Aβ.

The microbes of human microbiome can release large amounts of lipopolysaccharides (LPS), which might play a role in the production of proinflammatory cytokines related to the pathogenesis of AD (Pistollato et al., 2016). LPSs may modify gut homeostasis, gut inflammation, and gut permeability (Hufnagel et al., 2013). Additionally, the presence of bacterial LPS or endotoxin-mediated inflammation actively contributes to the potentiated fibrillogenesis of Aβ by stimulating fibril elongation (Asti and Gioglio, 2014) and attenuated amyloid clearance by down-regulating TREM2 (Zhao and Lukiw, 2015). Bacterial amyloid is considered as a pathogen-associated molecular pattern (PAMP) and induces activation of toll-like receptor-2 (TLR2) and other inflammatory mediators including NF-κB, as well as TLR1 and CD14 (Tukel et al., 2010; Nishimori et al., 2012). Besides, the activated inflammatory reaction caused by microbiome species, and their secretory products have shown to intensify the aggregation of amyloids into senile plaque lesions (Smith et al., 1996; Zhao et al., 2018).

Allen (2016) provided a novel hypothesis about the production of Aβ induced by spirochetes. They found spirochetes and innate immune system activity in the brains of AD patients. Additionally, they suggested that the innate immune system first responder TLR2 and its major pathway (MyD88) activates the secretases which generate Aβ. Interestingly, Aβ has been shown to be antimicrobial (Soscia et al., 2010). Therefore, they suggested that Aβ is generated for the purpose to rid the body of the spirochetal parasites, and the damages of tissue, as well as the neuronal circuits, are the adverse reactions. Kumar et al. (2016) gave a more detailed explanation of the antimicrobial role of Aβ. They found a rapid seeding and accelerated Aβ deposition after Salmonella Typhimurium bacteria infections in 5XFAD mice. And they showed that Aβ fibrils mediated adhesion inhibition and agglutination activities against Candida. This novel perspective deepens our understanding of the enigmatic role of Aβ in the etiology of AD.

Traumatic Brain Injury

Traumatic brain injury (TBI) is an universal health and socioeconomic problem. The risk of AD is increased in moderate and severe head injury for 2.3 and 4.5 times, respectively (Plassman et al., 2000). Besides, a growing number of epidemiological studies have considered TBI as one of the most potent risk factors for AD (Molgaard et al., 1990; O’Meara et al., 1997; Guo et al., 2000; Fleminger et al., 2003). However, two recent studies found that a history of TBI was not associated with AD or the Aβ deposition (Crane et al., 2016; Weiner et al., 2017). The conflicting result may due to the difference in severity and frequency of TBI, which may result in different neuropathologic outcomes.

Numerous studies have shown that TBI can trigger rapid and insidiously progressive AD-like pathological process, such as the production and accumulation of Aβ (Johnson et al., 2010). Up to 30% of patients who die from TBI have the Aβ plaques in their brain (Roberts et al., 1991, 1994). But the mechanism by which TBI induce Aβ accumulation is still obscure. The most common pathologies of TBI is diffuse axonal injury, which causes an accumulation of proteins in the axon, including APP (Gentleman et al., 1993; Gorrie et al., 2002). Also, PS-1 and BACE1 were found in injured axons after TBI (Uryu et al., 2007; Chen et al., 2009). Furthermore, high APP production following TBI may increase β-secretase processing and Aβ genesis (Lou et al., 2017), due to the saturation of normal α-secretase processing pathway (Gentleman et al., 1993; Graham et al., 1996). TBI also induces Aβ genesis via oxidative-stress-mediated upregulation of BACE1 (Tamagno et al., 2005; Guglielmotto et al., 2009). TBI is accompanied by hypoperfusion, vascular dysfunction and ischemia (Ramos-Cejudo et al., 2018), which may play an important role in Aβ deposition. It has been shown that transient hypoxia elevated plasma Aβ42 levels (Gren et al., 2016); reduced blood flow activated β-secretase and γ-secretase (Pluta et al., 2013); metabolic acidosis after TBI could potentially contribute to Aβ accumulation due to the fact that Aβ is prone to aggregation in a pH-dependent manner (Acharya et al., 2016). S100A9-driven amyloid-neuroinflammatory cascade may be also involved in the accumulation of Aβ. S100A9 is an amyloidogenic protein associated with inflammation, which was found to form amyloid plaques itself in TBI (Wang et al., 2018) and AD (Shepherd et al., 2006). Recently, S100A9 was found abundant in TBI human brain tissue compared to Aβ and contributed to Aβ plaque formation (Wang et al., 2018). Another study also found S100A9 also co-aggregated with both Aβ40 and Aβ42 and promoted their amyloid deposition (Wang et al., 2014). How S100A9 interact with Aβ and whether aggregation of S100A9 could serve as seeds to accelerate aggregation of Aβ need a deep investigation.

Others

Beside processes mentioned above, many factors may influence the production and accumulation of Aβ. For example, dietary fats may affect cerebrovascular integrity and alter Aβ kinetics across the blood-brain barrier (Takechi et al., 2010). Sex hormones also have effects on Aβ pathology (Grimm et al., 2016). Women exhibit a greater vulnerability to AD (Mielke et al., 2014) and a more striking Aβ deposition compared to men (Corder et al., 2004). Additionally, olfactory impairment subjects have more Aβ accumulation than normal people (Vassilaki et al., 2017).

Conclusions

A wealth of studies supports the amyloid hypothesis that Aβ is the initiator of a complex network of pathologic changes in the brain. And many earlier events precede Aβ aggregation. The best way to eliminate the Aβ pathology is to stop it from taking hold in the first place. Although much has been learned, many important questions remain. How do the early events initiate Aβ aggregation? How can we prevent it? What is the target point? When should measures be taken? How to explain the pathogenesis of AD-like dementias without Aβ, and how to avoid it? The answers to these questions might bring us to find safe and effective treatments for AD.

Author Contributions

XZ, ZF, LM, MH and ZZ prepared the manuscript.

Funding

This work was supported by National Natural Science Foundation of China (No.81571249, 81771382 and 81822016).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Acharya, S., Srivastava, K. R., Nagarajan, S., and Lapidus, L. J. (2016). Monomer dynamics of alzheimer peptides and kinetic control of early aggregation in Alzheimer’s disease. Chemphyschem 17, 3470–3479. doi: 10.1002/cphc.201600706

PubMed Abstract | CrossRef Full Text | Google Scholar

Acx, H., Chávez-Gutiérrez, L., Serneels, L., Lismont, S., Benurwar, M., Elad, N., et al. (2014). Signature amyloid β profiles are produced by different γ-secretase complexes. J. Biol. Chem. 289, 4346–4355. doi: 10.1074/jbc.m113.530907

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen, H. B. (2016). Alzheimer’s disease: a novel hypothesis for the development and the subsequent role of β amyloid. J. Neuroinfect. Dis. 7:211. doi: 10.4172/2314-7326.1000211

CrossRef Full Text | Google Scholar

Amouyel, P., Brousseau, T., Fruchart, J. C., and Dallongeville, J. (1993). Apolipoprotein E-epsilon 4 allele and Alzheimer’s disease. Lancet 342:1309.

PubMed Abstract | Google Scholar

Andreetto, E., Yan, L. M., Tatarek-Nossol, M., Velkova, A., Frank, R., and Kapurniotu, A. (2010). Identification of hot regions of the Aβ-IAPP interaction interface as high-affinity binding sites in both cross- and self-association. Angew. Chem. Int. Ed Engl. 49, 3081–3085. doi: 10.1002/anie.200904902

PubMed Abstract | CrossRef Full Text | Google Scholar

Antikainen, H., Driscoll, M., Haspel, G., and Dobrowolski, R. (2017). TOR-mediated regulation of metabolism in aging. Aging Cell 16, 1219–1233. doi: 10.1111/acel.12689

PubMed Abstract | CrossRef Full Text | Google Scholar

Arancio, O., Zhang, H. P., Chen, X., Lin, C., Trinchese, F., Puzzo, D., et al. (2004). RAGE potentiates Aβ-induced perturbation of neuronal function in transgenic mice. EMBO J. 23, 4096–4105. doi: 10.1038/sj.emboj.7600415

PubMed Abstract | CrossRef Full Text | Google Scholar

Arimon, M., Takeda, S., Post, K. L., Svirsky, S., Hyman, B. T., and Berezovska, O. (2015). Oxidative stress and lipid peroxidation are upstream of amyloid pathology. Neurobiol. Dis. 84, 109–119. doi: 10.1016/j.nbd.2015.06.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Arriagada, P. V., Growdon, J. H., Hedley-Whyte, E. T., and Hyman, B. T. (1992). Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology 42, 631–639. doi: 10.1212/wnl.42.3.631

PubMed Abstract | CrossRef Full Text | Google Scholar

Arvanitakis, Z., Wilson, R. S., Bienias, J. L., Evans, D. A., and Bennett, D. A. (2004). Diabetes mellitus and risk of Alzheimer disease and decline in cognitive function. Arch. Neurol. 61, 661–666. doi: 10.1001/archneur.61.5.661

PubMed Abstract | CrossRef Full Text | Google Scholar

Asti, A., and Gioglio, L. (2014). Can a bacterial endotoxin be a key factor in the kinetics of amyloid fibril formation? J. Alzheimers Dis. 39, 169–179. doi: 10.3233/jad-131394

PubMed Abstract | CrossRef Full Text | Google Scholar

Atagi, Y., Liu, C. C., Painter, M. M., Chen, X. F., Verbeeck, C., Zheng, H., et al. (2015). Apolipoprotein E is a ligand for triggering receptor expressed on myeloid cells 2 (TREM2). J. Biol. Chem. 290, 26043–26050. doi: 10.1074/jbc.M115.679043

PubMed Abstract | CrossRef Full Text | Google Scholar

Baglietto-Vargas, D., Shi, J., Yaeger, D. M., Ager, R., and LaFerla, F. M. (2016). Diabetes and Alzheimer’s disease crosstalk. Neurosci. Biobehav. Rev. 64, 272–287. doi: 10.1016/j.neubiorev.2016.03.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Bales, K. R., Verina, T., Cummins, D. J., Du, Y., Dodel, R. C., Saura, J., et al. (1999). Apolipoprotein E is essential for amyloid deposition in the APPV717F transgenic mouse model of Alzheimer’s disease. Proc. Natl. Acad. Sci. U S A 96, 15233–15238. doi: 10.1073/pnas.96.26.15233

PubMed Abstract | CrossRef Full Text | Google Scholar

Balin, B. J., and Hudson, A. P. (2014). Etiology and pathogenesis of late-onset Alzheimer’s disease. Curr. Allergy Asthma Rep. 14:417. doi: 10.1007/s11882-013-0417-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Bateman, R. J., Xiong, C., Benzinger, T. L., Fagan, A. M., Goate, A., Fox, N. C., et al. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 367, 795–804. doi: 10.1056/NEJMoa1202753

PubMed Abstract | CrossRef Full Text | Google Scholar

Benilova, I., Karran, E., and De Strooper, B. (2012). The toxic Aβ oligomer and Alzheimer’s disease: an emperor in need of clothes. Nat. Neurosci. 15, 349–357. doi: 10.1038/nn.3028

PubMed Abstract | CrossRef Full Text | Google Scholar

Bettens, K., Sleegers, K., and Van Broeckhoven, C. (2013). Genetic insights in Alzheimer’s disease. Lancet Neurol. 12, 92–104. doi: 10.1016/s1474-4422(12)70259-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Bien-Ly, N., Andrews-Zwilling, Y., Xu, Q., Bernardo, A., Wang, C., and Huang, Y. (2011). C-terminal-truncated apolipoprotein (apo) E4 inefficiently clears amyloid-β (Aβ) and acts in concert with Aβ to elicit neuronal and behavioral deficits in mice. Proc. Natl. Acad. Sci. U S A 108, 4236–4241. doi: 10.1073/pnas.1018381108

PubMed Abstract | CrossRef Full Text | Google Scholar

Biessels, G. J., Staekenborg, S., Brunner, E., Brayne, C., and Scheltens, P. (2006). Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 5, 64–74. doi: 10.1016/S1474-4422(05)70284-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Bomfim, T. R., Forny-Germano, L., Sathler, L. B., Brito-Moreira, J., Houzel, J. C., Decker, H., et al. (2012). An anti-diabetes agent protects the mouse brain from defective insulin signaling caused by Alzheimer’s disease- associated Aβ oligomers. J. Clin. Invest. 122, 1339–1353. doi: 10.1172/jci57256

PubMed Abstract | CrossRef Full Text | Google Scholar

Braak, H., and Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259. doi: 10.1007/bf00308809

PubMed Abstract | CrossRef Full Text | Google Scholar

Braak, H., and Del Trecidi, K. (2015). Neuroanatomy and pathology of sporadic Alzheimer’s disease. Adv. Anat. Embryol. Cell Biol. 215, 1–162. doi: 10.1007/978-3-319-12679-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Brodbeck, J., McGuire, J., Liu, Z., Meyer-Franke, A., Balestra, M. E., Jeong, D. E., et al. (2011). Structure-dependent impairment of intracellular apolipoprotein E4 trafficking and its detrimental effects are rescued by small-molecule structure correctors. J. Biol. Chem. 286, 17217–17226. doi: 10.1074/jbc.m110.217380

PubMed Abstract | CrossRef Full Text | Google Scholar

Cai, Z., Chen, G., He, W., Xiao, M., and Yan, L. J. (2015). Activation of mTOR: a culprit of Alzheimer’s disease? Neuropsychiatr. Dis. Treat. 11, 1015–1030. doi: 10.2147/NDT.S75717

PubMed Abstract | CrossRef Full Text | Google Scholar

Calafate, S., Flavin, W., Verstreken, P., and Moechars, D. (2016). Loss of Bin1 promotes the propagation of tau pathology. Cell Rep. 17, 931–940. doi: 10.1016/j.celrep.2016.09.063

PubMed Abstract | CrossRef Full Text | Google Scholar

Cannon, J. P., O’Driscoll, M., and Litman, G. W. (2012). Specific lipid recognition is a general feature of CD300 and TREM molecules. Immunogenetics 64, 39–47. doi: 10.1007/s00251-011-0562-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Chapuis, J., Hansmannel, F., Gistelinck, M., Mounier, A., Van Cauwenberghe, C., Kolen, K. V., et al. (2013). Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol. Psychiatry 18, 1225–1234. doi: 10.1038/mp.2013.1

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, X. H., Johnson, V. E., Uryu, K., Trojanowski, J. Q., and Smith, D. H. (2009). A lack of amyloid β plaques despite persistent accumulation of amyloid β in axons of long-term survivors of traumatic brain injury. Brain Pathol. 19, 214–223. doi: 10.1111/j.1750-3639.2008.00176.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, X., Walker, D. G., Schmidt, A. M., Arancio, O., Lue, L. F., and Yan, S. D. (2007). RAGE: a potential target for Aβ-mediated cellular perturbation in Alzheimer’s disease. Curr. Mol. Med. 7, 735–742. doi: 10.2174/156652407783220741

PubMed Abstract | CrossRef Full Text | Google Scholar

Cholerton, B., Baker, L. D., and Craft, S. (2013). Insulin, cognition, and dementia. Eur. J. Pharmacol. 719, 170–179. doi: 10.1016/j.ejphar.2013.08.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Citron, M., Westaway, D., Xia, W., Carlson, G., Diehl, T., Levesque, G., et al. (1997). Mutant presenilins of Alzheimer’s disease increase production of 42-residue amyloid β-protein in both transfected cells and transgenic mice. Nat. Med. 3, 67–72. doi: 10.1038/nm0197-67

PubMed Abstract | CrossRef Full Text | Google Scholar

Citron, M., Oltersdorf, T., Haass, C., McConlogue, L., Hung, A. Y., Seubert, P., et al. (1992). Mutation of the β-amyloid precursor protein in familial Alzheimer’s disease increases β-protein production. Nature 360, 672–674. doi: 10.1038/360672a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Colonna, M., and Wang, Y. (2016). TREM2 variants: new keys to decipher Alzheimer disease pathogenesis. Nat. Rev. Neurosci. 17, 201–207. doi: 10.1038/nrn.2016.7

PubMed Abstract | CrossRef Full Text | Google Scholar

Condello, C., Yuan, P., Schain, A., and Grutzendler, J. (2015). Microglia constitute a barrier that prevents neurotoxic protofibrillar Aβ42 hotspots around plaques. Nat. Commun. 6:6176. doi: 10.1038/ncomms7176

PubMed Abstract | CrossRef Full Text | Google Scholar

Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., et al. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921–923. doi: 10.1126/science.8346443

PubMed Abstract | CrossRef Full Text | Google Scholar

Corder, E. H., Ghebremedhin, E., Taylor, M. G., Thal, D. R., Ohm, T. G., and Braak, H. (2004). The biphasic relationship between regional brain senile plaque and neurofibrillary tangle distributions: modification by age, sex and APOE polymorphism. Ann. N Y Acad. Sci. 1019, 24–28. doi: 10.1196/annals.1297.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Correia, S. C., Santos, R. X., Perry, G., Zhu, X., Moreira, P. I., and Smith, M. A. (2011). Insulin-resistant brain state: the culprit in sporadic Alzheimer’s disease? Ageing Res. Rev. 10, 264–273. doi: 10.1016/j.arr.2011.01.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Crane, P. K., Gibbons, L. E., Dams-O’Connor, K., Trittschuh, E., Leverenz, J. B., Keene, C. D., et al. (2016). Association of traumatic brain injury with late-life neurodegenerative conditions and neuropathologic findings. JAMA Neurol. 73, 1062–1069. doi: 10.1001/jamaneurol.2016.1948

PubMed Abstract | CrossRef Full Text | Google Scholar

Crane, P. K., Walker, R., Hubbard, R. A., Li, G., Nathan, D. M., Zheng, H., et al. (2013). Glucose levels and risk of dementia. N. Engl. J. Med. 369, 540–548. doi: 10.1056/NEJMoa1215740

PubMed Abstract | CrossRef Full Text | Google Scholar

Daws, M. R., Sullam, P. M., Niemi, E. C., Chen, T. T., Tchao, N. K., and Seaman, W. E. (2003). Pattern recognition by TREM-2: binding of anionic ligands. J. Immunol. 171, 594–599. doi: 10.4049/jimmunol.171.2.594

PubMed Abstract | CrossRef Full Text | Google Scholar

De Chiara, G., Marcocci, M. E., Sgarbanti, R., Civitelli, L., Ripoli, C., Piacentini, R., et al. (2012). Infectious agents and neurodegeneration. Mol. Neurobiol. 46, 614–638. doi: 10.1007/s12035-012-8320-7

PubMed Abstract | CrossRef Full Text | Google Scholar

De Felice, F. G., Vieira, M. N., Bomfim, T. R., Decker, H., Velasco, P. T., Lambert, M. P., et al. (2009). Protection of synapses against Alzheimer’s-linked toxins: insulin signaling prevents the pathogenic binding of Aβ oligomers. Proc. Natl. Acad. Sci. U S A 106, 1971–1976. doi: 10.1073/pnas.0809158106

PubMed Abstract | CrossRef Full Text | Google Scholar

Devi, L., Alldred, M. J., Ginsberg, S. D., and Ohno, M. (2012). Mechanisms underlying insulin deficiency-induced acceleration of β-amyloidosis in a mouse model of Alzheimer’s disease. PLoS One 7:e32792. doi: 10.1371/journal.pone.0032792

PubMed Abstract | CrossRef Full Text | Google Scholar

Dumanis, S. B., Tesoriero, J. A., Babus, L. W., Nguyen, M. T., Trotter, J. H., Ladu, M. J., et al. (2009). ApoE4 decreases spine density and dendritic complexity in cortical neurons in vivo. J. Neurosci. 29, 15317–15322. doi: 10.1523/jneurosci.4026-09.2009

PubMed Abstract | CrossRef Full Text | Google Scholar

Finkel, T., and Holbrook, N. J. (2000). Oxidants, oxidative stress and the biology of ageing. Nature 408, 239–247. doi: 10.1038/35041687

PubMed Abstract | CrossRef Full Text | Google Scholar

Fleminger, S., Oliver, D. L., Lovestone, S., Rabe-Hesketh, S., and Giora, A. (2003). Head injury as a risk factor for Alzheimer’s disease: the evidence 10 years on; a partial replication. J. Neurol. Neurosurg. Psychiatry 74, 857–862. doi: 10.1136/jnnp.74.7.857

PubMed Abstract | CrossRef Full Text | Google Scholar

Franceschi, C., Garagnani, P., Vitale, G., Capri, M., and Salvioli, S. (2017). Inflammaging and ‘Garb-aging’. Trends Endocrinol. Metab. 28, 199–212. doi: 10.1016/j.tem.2016.09.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Fukazawa, R., Hanyu, H., Sato, T., Shimizu, S., Koyama, S., Kanetaka, H., et al. (2013). Subgroups of Alzheimer’s disease associated with diabetes mellitus based on brain imaging. Dement. Geriatr. Cogn. Disord. 35, 280–290. doi: 10.1159/000348407

PubMed Abstract | CrossRef Full Text | Google Scholar

Furman, J. L., Sama, D. M., Gant, J. C., Beckett, T. L., Murphy, M. P., Bachstetter, A. D., et al. (2012). Targeting astrocytes ameliorates neurologic changes in a mouse model of Alzheimer’s disease. J. Neurosci. 32, 16129–16140. doi: 10.1523/jneurosci.2323-12.2012

PubMed Abstract | CrossRef Full Text | Google Scholar

Gandy, S., and Heppner, F. L. (2013). Microglia as dynamic and essential components of the amyloid hypothesis. Neuron 78, 575–577. doi: 10.1016/j.neuron.2013.05.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Gentleman, S. M., Nash, M. J., Sweeting, C. J., Graham, D. I., and Roberts, G. W. (1993). β-amyloid precursor protein (β APP) as a marker for axonal injury after head injury. Neurosci. Lett. 160, 139–144. doi: 10.1016/0304-3940(93)90398-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Gispen, W. H., and Biessels, G. J. (2000). Cognition and synaptic plasticity in diabetes mellitus. Trends Neurosci. 23, 542–549. doi: 10.1016/s0166-2236(00)01656-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenner, G. G., and Wong, C. W. (1984a). Alzheimer’s disease and down’s syndrome: sharing of a unique cerebrovascular amyloid fibril protein. Biochem. Biophys. Res. Commun. 122, 1131–1135. doi: 10.1016/0006-291x(84)91209-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Glenner, G. G., and Wong, C. W. (1984b). Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem. Biophys. Res. Commun. 120, 885–890. doi: 10.1016/s0006-291x(84)80190-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Goedert, M., and Spillantini, M. G. (2006). A century of Alzheimer’s disease. Science 314, 777–781. doi: 10.1126/science.1132814

PubMed Abstract | CrossRef Full Text | Google Scholar

Gómez-Isla, T., Price, J. L., McKeel, D. W. Jr., Morris, J. C., Growdon, J. H., and Hyman, B. T. (1996). Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J. Neurosci. 16, 4491–4500. doi: 10.1523/jneurosci.16-14-04491.1996

PubMed Abstract | CrossRef Full Text | Google Scholar

Gorrie, C., Oakes, S., Duflou, J., Blumbergs, P., and Waite, P. M. (2002). Axonal injury in children after motor vehicle crashes: extent, distribution and size of axonal swellings using β-APP immunohistochemistry. J. Neurotrauma 19, 1171–1182. doi: 10.1089/08977150260337976

PubMed Abstract | CrossRef Full Text | Google Scholar

Graham, D. I., Gentleman, S. M., Nicoll, J. A., Royston, M. C., McKenzie, J. E., Roberts, G. W., et al. (1996). Altered β-APP metabolism after head injury and its relationship to the aetiology of Alzheimer’s disease. Acta Neurochir. Suppl. 66, 96–102. doi: 10.1007/978-3-7091-9465-2_17

PubMed Abstract | CrossRef Full Text | Google Scholar

Gren, M., Shahim, P., Lautner, R., Wilson, D. H., Andreasson, U., Norgren, N., et al. (2016). Blood biomarkers indicate mild neuroaxonal injury and increased amyloid β production after transient hypoxia during breath-hold diving. Brain Inj. 30, 1226–1230. doi: 10.1080/02699052.2016.1179792

PubMed Abstract | CrossRef Full Text | Google Scholar

Grimm, A., Mensah-Nyagan, A. G., and Eckert, A. (2016). Alzheimer, mitochondria and gender. Neurosci. Biobehav. Rev. 67, 89–101. doi: 10.1016/j.neubiorev.2016.04.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., et al. (2013). TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 368, 117–127. doi: 10.1056/NEJMoa1211851

PubMed Abstract | CrossRef Full Text | Google Scholar

Guglielmotto, M., Aragno, M., Autelli, R., Giliberto, L., Novo, E., Colombatto, S., et al. (2009). The up-regulation of BACE1 mediated by hypoxia and ischemic injury: role of oxidative stress and HIF1α. J. Neurochem. 108, 1045–1056. doi: 10.1111/j.1471-4159.2008.05858.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Guix, F. X., Wahle, T., Vennekens, K., Snellinx, A., Chavez-Gutierrez, L., Ill-Raga, G., et al. (2012). Modification of γ-secretase by nitrosative stress links neuronal ageing to sporadic Alzheimer’s disease. EMBO Mol. Med. 4, 660–673. doi: 10.1002/emmm.201200243

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, J. P., Arai, T., Miklossy, J., and McGeer, P. L. (2006). Aβ and tau form soluble complexes that may promote self aggregation of both into the insoluble forms observed in Alzheimer’s disease. Proc. Natl. Acad. Sci. U S A 103, 1953–1958. doi: 10.1073/pnas.0509386103

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, Z., Cupples, L. A., Kurz, A., Auerbach, S. H., Volicer, L., Chui, H., et al. (2000). Head injury and the risk of AD in the MIRAGE study. Neurology 54, 1316–1323. doi: 10.1212/wnl.54.6.1316

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, J. L., and Lee, V. M. (2014). Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat. Med. 20, 130–138. doi: 10.1038/nm.3457

PubMed Abstract | CrossRef Full Text | Google Scholar

Gwon, A. R., Park, J. S., Arumugam, T. V., Kwon, Y. K., Chan, S. L., Kim, S. H., et al. (2012). Oxidative lipid modification of nicastrin enhances amyloidogenic γ-secretase activity in Alzheimer’s disease. Aging Cell 11, 559–568. doi: 10.1111/j.1474-9726.2012.00817.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Hardy, J. A., and Higgins, G. A. (1992). Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185. doi: 10.1126/science.1566067

PubMed Abstract | CrossRef Full Text | Google Scholar

Hardy, J. A., and Selkoe, D. J. (2002). The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353–356. doi: 10.1126/science.1072994

PubMed Abstract | CrossRef Full Text | Google Scholar

Heneka, M. T., Kummer, M. P., Stutz, A., Delekate, A., Schwartz, S., Vieira-Saecker, A., et al. (2013). NLRP3 is activated in Alzheimer’s disease and contributes to pathology in APP/PS1 mice. Nature 493, 674–678. doi: 10.1038/nature11729

PubMed Abstract | CrossRef Full Text | Google Scholar

Hickman, S., Izzy, S., Sen, P., Morsett, L., and El Khoury, J. (2018). Microglia in neurodegeneration. Nat. Neurosci. 21, 1359–1369. doi: 10.1038/s41593-018-0242-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Hipp, M. S., Park, S. H., and Hartl, F. U. (2014). Proteostasis impairment in protein-misfolding and -aggregation diseases. Trends Cell. Biol. 24, 506–514. doi: 10.1016/j.tcb.2014.05.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Holler, C. J., Davis, P. R., Beckett, T. L., Platt, T. L., Webb, R. L., Head, E., et al. (2014). Bridging integrator 1 (BIN1) protein expression increases in the Alzheimer’s disease brain and correlates with neurofibrillary tangle pathology. J. Alzheimers Dis. 42, 1221–1227. doi: 10.3233/JAD-132450

PubMed Abstract | CrossRef Full Text | Google Scholar

Holtzman, D. M., Herz, J., and Bu, G. (2012). Apolipoprotein E and apolipoprotein E receptors: normal biology and roles in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2:a006312. doi: 10.1101/cshperspect.a006312

PubMed Abstract | CrossRef Full Text | Google Scholar

Hong, S., Beja-Glasser, V. F., Nfonoyim, B. M., Frouin, A., Li, S., Ramakrishnan, S., et al. (2016). Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716. doi: 10.1126/science.aad8373

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, R., Zhang, M., Chen, H., Jiang, B., and Zheng, J. (2015). Cross-seeding interaction between β-amyloid and human Islet amyloid polypeptide. ACS Chem. Neurosci. 6, 1759–1768. doi: 10.1021/acschemneuro.5b00192

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, Y. (2006). Molecular and cellular mechanisms of apolipoprotein E4 neurotoxicity and potential therapeutic strategies. Curr. Opin. Drug Discov. Devel. 9, 627–641.

PubMed Abstract | Google Scholar

Huang, Y. (2010). Aβ-independent roles of apolipoprotein E4 in the pathogenesis of Alzheimer’s disease. Trends Mol. Med. 16, 287–294. doi: 10.1016/j.molmed.2010.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, Y., and Mahley, R. W. (2006). Commentary on “Perspective on a pathogenesis and treatment of Alzheimer’s disease”. Apolipoprotein E and the mitochondrial metabolic hypothesis. Alzheimers Dement. 2, 71–73. doi: 10.1016/j.jalz.2005.12.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, Y. A., Zhou, B., Wernig, M., and Sudhof, T. C. (2017). ApoE2, ApoE3 and ApoE4 differentially stimulate APP transcription and Aβ secretion. Cell 168, 427.e21–441.e21. doi: 10.1016/j.cell.2016.12.044

PubMed Abstract | CrossRef Full Text | Google Scholar

Hufnagel, D. A., Tükel, C., and Chapman, M. R. (2013). Disease to dirt: the biology of microbial amyloids. PLoS Pathog. 9:e1003740. doi: 10.1371/journal.ppat.1003740

PubMed Abstract | CrossRef Full Text | Google Scholar

Iadanza, M. G., Jackson, M. P., Hewitt, E. W., Ranson, N. A., and Radford, S. E. (2018). A new era for understanding amyloid structures and disease. Nat. Rev. Mol. Cell Biol. doi: 10.1038/s41580-018-0060-8 [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

Itzhaki, R. F. (2014). Herpes simplex virus type 1 and Alzheimer’s disease: increasing evidence for a major role of the virus. Front. Aging Neurosci. 6:202. doi: 10.3389/fnagi.2014.00202

PubMed Abstract | CrossRef Full Text | Google Scholar

Jackson, K., Barisone, G. A., Diaz, E., Jin, L. W., DeCarli, C., and Despa, F. (2013). Amylin deposition in the brain: a second amyloid in Alzheimer disease? Ann. Neurol. 74, 517–526. doi: 10.1002/ana.23956

PubMed Abstract | CrossRef Full Text | Google Scholar

Jay, T. R., Miller, C. M., Cheng, P. J., Graham, L. C., Bemiller, S., Broihier, M. L., et al. (2015). TREM2 deficiency eliminates TREM2+ inflammatory macrophages and ameliorates pathology in Alzheimer’s disease mouse models. J. Exp. Med. 212, 287–295. doi: 10.1084/jem.20142322

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnson, V. E., Stewart, W., and Smith, D. H. (2010). Traumatic brain injury and amyloid-β pathology: a link to Alzheimer’s disease? Nat. Rev. Neurosci. 11, 361–370. doi: 10.1038/nrn2808

PubMed Abstract | CrossRef Full Text | Google Scholar

Jolivalt, C. G., Hurford, R., Lee, C. A., Dumaop, W., Rockenstein, E., and Masliah, E. (2010). Type 1 diabetes exaggerates features of Alzheimer’s disease in APP transgenic mice. Exp. Neurol. 223, 422–431. doi: 10.1016/j.expneurol.2009.11.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., et al. (2012). A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 488, 96–99. doi: 10.1038/nature11283

PubMed Abstract | CrossRef Full Text | Google Scholar

Jucker, M., and Walker, L. C. (2013). Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501, 45–51. doi: 10.1038/nature12481

PubMed Abstract | CrossRef Full Text | Google Scholar

Karch, C. M., and Goate, A. M. (2015). Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol. Psychiatry 77, 43–51. doi: 10.1016/j.biopsych.2014.05.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., et al. (2014). Geroscience: linking aging to chronic disease. Cell 159, 709–713. doi: 10.1016/j.cell.2014.10.039

PubMed Abstract | CrossRef Full Text | Google Scholar

Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., et al. (2017). A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276.e17–1290.e17. doi: 10.1016/j.cell.2017.05.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Kerti, L., Witte, A. V., Winkler, A., Grittner, U., Rujescu, D., and Floel, A. (2013). Higher glucose levels associated with lower memory and reduced hippocampal microstructure. Neurology 81, 1746–1752. doi: 10.1212/01.wnl.0000435561.00234.ee

PubMed Abstract | CrossRef Full Text | Google Scholar

Kim, J., Basak, J. M., and Holtzman, D. M. (2009). The role of apolipoprotein E in Alzheimer’s disease. Neuron 63, 287–303. doi: 10.1016/j.neuron.2009.06.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Knopman, D. S., Parisi, J. E., Salviati, A., Floriach-Robert, M., Boeve, B. F., Ivnik, R. J., et al. (2003). Neuropathology of cognitively normal elderly. J. Neuropathol. Exp. Neurol. 62, 1087–1095. doi: 10.1093/jnen/62.11.1087

PubMed Abstract | CrossRef Full Text | Google Scholar

Krstic, D., and Knuesel, I. (2013). Deciphering the mechanism underlying late-onset Alzheimer disease. Nat. Rev. Neurol. 9, 25–34. doi: 10.1038/nrneurol.2012.236

PubMed Abstract | CrossRef Full Text | Google Scholar

Krstic, D., Madhusudan, A., Doehner, J., Vogel, P., Notter, T., Imhof, C., et al. (2012). Systemic immune challenges trigger and drive Alzheimer-like neuropathology in mice. J. Neuroinflammation 9:151. doi: 10.1186/1742-2094-9-151

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, D. K., Choi, S. H., Washicosky, K. J., Eimer, W. A., Tucker, S., Ghofrani, J., et al. (2016). Amyloid-β peptide protects against microbial infection in mouse and worm models of Alzheimer’s disease. Sci. Transl. Med. 8:340ra372. doi: 10.1126/scitranslmed.aaf1059

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, C. Y. D., Daggett, A., Gu, X., Jiang, L. L., Langfelder, P., Li, X., et al. (2018). Elevated TREM2 gene dosage reprograms microglia responsivity and ameliorates pathological phenotypes in Alzheimer’s disease models. Neuron 97, 1032.e5–1048.e5. doi: 10.1016/j.neuron.2018.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Lester-Coll, N., Rivera, E. J., Soscia, S. J., Doiron, K., Wands, J. R., and de la Monte, S. M. (2006). Intracerebral streptozotocin model of type 3 diabetes: relevance to sporadic Alzheimer’s disease. J. Alzheimers Dis. 9, 13–33. doi: 10.3233/jad-2006-9102

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, S., Park, S., Allington, G., Prelli, F., Sun, Y., Martá-Ariza, M., et al. (2017). Targeting apolipoprotein E/amyloid β binding by peptoid CPO_Aβ17–21 P ameliorates Alzheimer’s disease related pathology and cognitive decline. Sci. Rep. 7:8009. doi: 10.1038/s41598-017-08604-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Lou, D., Du, Y., Huang, D., Cai, F., Zhang, Y., Li, T., et al. (2017). Traumatic brain injury alters the metabolism and facilitates Alzheimer’s disease in a murine model. Mol. Neurobiol. 55, 4928–4939. doi: 10.1007/s12035-017-0687-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, T., Aron, L., Zullo, J., Pan, Y., Kim, H., Chen, Y., et al. (2014). REST and stress resistance in ageing and Alzheimer’s disease. Nature 507, 448–454. doi: 10.1038/nature13163

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, A., Magupalli, V. G., Ruan, J., Yin, Q., Atianand, M. K., Vos, M. R., et al. (2014). Unified polymerization mechanism for the assembly of ASC-dependent inflammasomes. Cell 156, 1193–1206. doi: 10.1016/j.cell.2014.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Luchsinger, J. A., Reitz, C., Honig, L. S., Tang, M. X., Shea, S., and Mayeux, R. (2005). Aggregation of vascular risk factors and risk of incident Alzheimer disease. Neurology 65, 545–551. doi: 10.1212/01.wnl.0000172914.08967.dc

PubMed Abstract | CrossRef Full Text | Google Scholar

Lue, L. F., Kuo, Y. M., Beach, T., and Walker, D. G. (2010). Microglia activation and anti-inflammatory regulation in Alzheimer’s disease. Mol. Neurobiol. 41, 115–128. doi: 10.1007/s12035-010-8106-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Lue, L. F., Walker, D. G., Brachova, L., Beach, T. G., Rogers, J., Schmidt, A. M., et al. (2001). Involvement of microglial receptor for advanced glycation endproducts (RAGE) in Alzheimer’s disease: identification of a cellular activation mechanism. Exp. Neurol. 171, 29–45. doi: 10.1006/exnr.2001.7732

PubMed Abstract | CrossRef Full Text | Google Scholar

Lührs, T., Ritter, C., Adrian, M., Riek-Loher, D., Bohrmann, B., Döbeli, H., et al. (2005). 3D structure of Alzheimer’s amyloid-β(1–42) fibrils. Proc. Natl. Acad. Sci. U S A 102, 17342–17347. doi: 10.1073/pnas.0506723102

PubMed Abstract | CrossRef Full Text | Google Scholar

Macauley, S. L., Stanley, M., Caesar, E. E., Yamada, S. A., Raichle, M. E., Perez, R., et al. (2015). Hyperglycemia modulates extracellular amyloid-β concentrations and neuronal activity in vivo. J. Clin. Invest. 125, 2463–2467. doi: 10.1172/JCI79742

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahley, R. W. (1988). Apolipoprotein E: cholesterol transport protein with expanding role in cell biology. Science 240, 622–630. doi: 10.1126/science.3283935

PubMed Abstract | CrossRef Full Text | Google Scholar

Mahley, R. W., and Huang, Y. (2006). Apolipoprotein (apo) E4 and Alzheimer’s disease: unique conformational and biophysical properties of apoE4 can modulate neuropathology. Acta Neurol. Scand. Suppl. 185, 8–14. doi: 10.1111/j.1600-0404.2006.00679.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Masters, C. L., Simms, G., Weinman, N. A., Multhaup, G., McDonald, B. L., and Beyreuther, K. (1985). Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc. Natl. Acad. Sci. U S A 82, 4245–4249. doi: 10.1073/pnas.82.12.4245

PubMed Abstract | CrossRef Full Text | Google Scholar

Mazaheri, F., Snaidero, N., Kleinberger, G., Madore, C., Daria, A., Werner, G., et al. (2017). TREM2 deficiency impairs chemotaxis and microglial responses to neuronal injury. EMBO Rep. 18, 1186–1198. doi: 10.15252/embr.201743922

PubMed Abstract | CrossRef Full Text | Google Scholar

McDade, E., and Bateman, R. J. (2017). Stop Alzheimer’s before it starts. Nature 547, 153–155. doi: 10.1038/547153a

PubMed Abstract | CrossRef Full Text | Google Scholar

Medeiros, R., and LaFerla, F. M. (2013). Astrocytes: conductors of the Alzheimer disease neuroinflammatory symphony. Exp. Neurol. 239, 133–138. doi: 10.1016/j.expneurol.2012.10.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Mielke, M. M., Vemuri, P., and Rocca, W. A. (2014). Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences. Clin. Epidemiol. 6, 37–48. doi: 10.2147/CLEP.s37929

PubMed Abstract | CrossRef Full Text | Google Scholar

Miklossy, J. (2015). Historic evidence to support a causal relationship between spirochetal infections and Alzheimer’s disease. Front. Aging Neurosci. 7:46. doi: 10.3389/fnagi.2015.00046

PubMed Abstract | CrossRef Full Text | Google Scholar

Miyagawa, T., Ebinuma, I., Morohashi, Y., Hori, Y., Young Chang, M., Hattori, H., et al. (2016). BIN1 regulates BACE1 intracellular trafficking and amyloid-β production. Hum. Mol. Genet. 25, 2948–2958. doi: 10.1093/hmg/ddw146

PubMed Abstract | CrossRef Full Text | Google Scholar

Molgaard, C. A., Stanford, E. P., Morton, D. J., Ryden, L. A., Schubert, K. R., and Golbeck, A. L. (1990). Epidemiology of head trauma and neurocognitive impairment in a multi-ethnic population. Neuroepidemiology 9, 233–242. doi: 10.1159/000110778

PubMed Abstract | CrossRef Full Text | Google Scholar

Moloney, A. M., Griffin, R. J., Timmons, S., O’Connor, R., Ravid, R., and O’Neill, C. (2010). Defects in IGF-1 receptor, insulin receptor and IRS-1/2 in Alzheimer’s disease indicate possible resistance to IGF-1 and insulin signalling. Neurobiol. Aging 31, 224–243. doi: 10.1016/j.neurobiolaging.2008.04.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Morales, R., Green, K. M., and Soto, C. (2009). Cross currents in protein misfolding disorders: interactions and therapy. CNS Neurol. Disord. Drug Targets 8, 363–371. doi: 10.2174/187152709789541998

PubMed Abstract | CrossRef Full Text | Google Scholar

Moreira, P. I. (2012). Alzheimer’s disease and diabetes: an integrative view of the role of mitochondria, oxidative stress, and insulin. J. Alzheimers Dis. 30, S199–S215. doi: 10.3233/jad-2011-111127

PubMed Abstract | CrossRef Full Text | Google Scholar

Moreno-Gonzalez, I., Edwards, G. III., Salvadores, N., Shahnawaz, M., Diaz-Espinoza, R., and Soto, C. (2017). Molecular interaction between type 2 diabetes and Alzheimer’s disease through cross-seeding of protein misfolding. Mol. Psychiatry 22, 1327–1334. doi: 10.1038/mp.2016.230

PubMed Abstract | CrossRef Full Text | Google Scholar

Morris, J. K., Vidoni, E. D., Honea, R. A., Burns, J. M., and Alzheimer’s Disease Neuroimaging Initiative. (2014). Impaired glycemia increases disease progression in mild cognitive impairment. Neurobiol. Aging 35, 585–589. doi: 10.1016/j.neurobiolaging.2013.09.033

PubMed Abstract | CrossRef Full Text | Google Scholar

Morris, M., Maeda, S., Vossel, K., and Mucke, L. (2011). The many faces of tau. Neuron 70, 410–426. doi: 10.1016/j.neuron.2011.04.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Musiek, E. S., and Holtzman, D. M. (2015). Three dimensions of the amyloid hypothesis: time, space and ‘wingmen’. Nat. Neurosci. 18, 800–806. doi: 10.1038/nn.4018

PubMed Abstract | CrossRef Full Text | Google Scholar

Myers, R. H., Schaefer, E. J., Wilson, P. W., D’Agostino, R., Ordovas, J. M., Espino, A., et al. (1996). Apolipoprotein E epsilon4 association with dementia in a population-based study: the Framingham study. Neurology 46, 673–677. doi: 10.1212/wnl.46.3.673

PubMed Abstract | CrossRef Full Text | Google Scholar

Nishimori, J. H., Newman, T. N., Oppong, G. O., Rapsinski, G. J., Yen, J. H., Biesecker, S. G., et al. (2012). Microbial amyloids induce interleukin 17A (IL-17A) and IL-22 responses via Toll-like receptor 2 activation in the intestinal mucosa. Infect. Immun. 80, 4398–4408. doi: 10.1128/iai.00911-12

PubMed Abstract | CrossRef Full Text | Google Scholar

Nixon, R. A. (2013). The role of autophagy in neurodegenerative disease. Nat. Med. 19, 983–997. doi: 10.1038/nm.3232

PubMed Abstract | CrossRef Full Text | Google Scholar

Noguchi, S., Murakami, K., and Yamada, N. (1993). Apolipoprotein E genotype and Alzheimer’s disease. Lancet 342:737. doi: 10.1016/0140-6736(93)91728-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Oda, A., Tamaoka, A., and Araki, W. (2010). Oxidative stress up-regulates presenilin 1 in lipid rafts in neuronal cells. J. Neurosci. Res. 88, 1137–1145. doi: 10.1002/jnr.22271

PubMed Abstract | CrossRef Full Text | Google Scholar

Olabarria, M., Noristani, H. N., Verkhratsky, A., and Rodriguez, J. J. (2010). Concomitant astroglial atrophy and astrogliosis in a triple transgenic animal model of Alzheimer’s disease. Glia 58, 831–838. doi: 10.1002/glia.20967

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Meara, E. S., Kukull, W. A., Sheppard, L., Bowen, J. D., McCormick, W. C., Teri, L., et al. (1997). Head injury and risk of Alzheimer’s disease by apolipoprotein E genotype. Am. J. Epidemiol. 146, 373–384. doi: 10.1093/oxfordjournals.aje.a009290

PubMed Abstract | CrossRef Full Text | Google Scholar

Ono, K., Takahashi, R., Ikeda, T., Mizuguchi, M., Hamaguchi, T., and Yamada, M. (2014). Exogenous amyloidogenic proteins function as seeds in amyloid β-protein aggregation. Biochim. Biophys. Acta 1842, 646–653. doi: 10.1016/j.bbadis.2014.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

O’Nuallain, B., Williams, A. D., Westermark, P., and Wetzel, R. (2004). Seeding specificity in amyloid growth induced by heterologous fibrils. J. Biol. Chem. 279, 17490–17499. doi: 10.1074/jbc.M311300200

PubMed Abstract | CrossRef Full Text | Google Scholar

Palop, J. J., and Mucke, L. (2010). Amyloid-β-induced neuronal dysfunction in Alzheimer’s disease: from synapses toward neural networks. Nat. Neurosci. 13, 812–818. doi: 10.1038/nn.2583

PubMed Abstract | CrossRef Full Text | Google Scholar

Pandini, G., Pace, V., Copani, A., Squatrito, S., Milardi, D., and Vigneri, R. (2013). Insulin has multiple antiamyloidogenic effects on human neuronal cells. Endocrinology 154, 375–387. doi: 10.1210/en.2012-1661

PubMed Abstract | CrossRef Full Text | Google Scholar

Paolicelli, R. C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., Panzanelli, P., et al. (2011). Synaptic pruning by microglia is necessary for normal brain development. Science 333, 1456–1458. doi: 10.1126/science.1202529

PubMed Abstract | CrossRef Full Text | Google Scholar

Petersen, R. C., Parisi, J. E., Dickson, D. W., Johnson, K. A., Knopman, D. S., Boeve, B. F., et al. (2006). Neuropathologic features of amnestic mild cognitive impairment. Arch. Neurol. 63, 665–672. doi: 10.1001/archneur.63.5.665

PubMed Abstract | CrossRef Full Text | Google Scholar

Pillay, K., and Govender, P. (2013). Amylin uncovered: a review on the polypeptide responsible for type II diabetes. Biomed Res. Int. 2013:826706. doi: 10.1155/2013/826706

PubMed Abstract | CrossRef Full Text | Google Scholar

Pistollato, F., Sumalla Cano, S., Elio, I., Masias Vergara, M., Giampieri, F., and Battino, M. (2016). Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease. Nutr. Rev. 74, 624–634. doi: 10.1093/nutrit/nuw023

PubMed Abstract | CrossRef Full Text | Google Scholar

Plassman, B. L., Havlik, R. J., Steffens, D. C., Helms, M. J., Newman, T. N., Drosdick, D., et al. (2000). Documented head injury in early adulthood and risk of Alzheimer’s disease and other dementias. Neurology 55, 1158–1166. doi: 10.1212/wnl.55.8.1158

PubMed Abstract | CrossRef Full Text | Google Scholar

Pluta, R., Furmaga-Jabońska, W., Maciejewski, R., Ułamek-Kozioł, M., and Jaboński, M. (2013). Brain ischemia activates β- and γ-secretase cleavage of amyloid precursor protein: significance in sporadic Alzheimer’s disease. Mol. Neurobiol. 47, 425–434. doi: 10.1007/s12035-012-8360-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Poirier, J., Davignon, J., Bouthillier, D., Kogan, S., Bertrand, P., and Gauthier, S. (1993). Apolipoprotein E polymorphism and Alzheimer’s disease. Lancet 342, 697–699. doi: 10.1016/0140-6736(93)91705-Q

PubMed Abstract | CrossRef Full Text | Google Scholar

Prasher, V. P., Farrer, M. J., Kessling, A. M., Fisher, E. M., West, R. J., Barber, P. C., et al. (1998). Molecular mapping of Alzheimer-type dementia in Down’s syndrome. Ann. Neurol. 43, 380–383. doi: 10.1002/ana.410430316

PubMed Abstract | CrossRef Full Text | Google Scholar

Pratico, D., Uryu, K., Leight, S., Trojanoswki, J. Q., and Lee, V. M. (2001). Increased lipid peroxidation precedes amyloid plaque formation in an animal model of Alzheimer amyloidosis. J. Neurosci. 21, 4183–4187. doi: 10.1523/JNEUROSCI.21-12-04183.2001

PubMed Abstract | CrossRef Full Text | Google Scholar

Price, J. L., and Morris, J. C. (1999). Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann. Neurol. 45, 358–368. doi: 10.1002/1531-8249(199903)45:3<358::aid-ana12>3.0.co;2-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65. doi: 10.1038/nature08821

PubMed Abstract | CrossRef Full Text | Google Scholar

Ramos-Cejudo, J., Wisniewski, T., Marmar, C., Zetterberg, H., Blennow, K., de Leon, M. J., et al. (2018). Traumatic brain injury and Alzheimer’s disease: the cerebrovascular link. EBioMedicine 28, 21–30. doi: 10.1016/j.ebiom.2018.01.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Roberts, G. W., Gentleman, S. M., Lynch, A., and Graham, D. I. (1991). βA4 amyloid protein deposition in brain after head trauma. Lancet 338, 1422–1423. doi: 10.1016/0140-6736(91)92724-g

PubMed Abstract | CrossRef Full Text | Google Scholar

Roberts, G. W., Gentleman, S. M., Lynch, A., Murray, L., Landon, M., and Graham, D. I. (1994). β amyloid protein deposition in the brain after severe head injury: implications for the pathogenesis of Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 57, 419–425. doi: 10.1136/jnnp.57.4.419

PubMed Abstract | CrossRef Full Text | Google Scholar

Rovelet-Lecrux, A., Hannequin, D., Raux, G., Le Meur, N., Laquerrière, A., Vital, A., et al. (2006). APP locus duplication causes autosomal dominant early-onset alzheimer disease with cerebral amyloid angiopathy. Nat. Genet. 38, 24–26. doi: 10.1038/ng1718

PubMed Abstract | CrossRef Full Text | Google Scholar

Sarlus, H., and Heneka, M. T. (2017). Microglia in Alzheimer’s disease. J. Clin. Invest. 127, 3240–3249. doi: 10.1172/JCI90606

PubMed Abstract | CrossRef Full Text | Google Scholar

Saxton, R. A., and Sabatini, D. M. (2017). mTOR signaling in growth, metabolism, and disease. Cell 168, 960–976. doi: 10.1016/j.cell.2017.03.035

PubMed Abstract | CrossRef Full Text | Google Scholar

Scheperjans, F. (2016). Can microbiota research change our understanding of neurodegenerative diseases? Neurodegener. Dis. Manag. 6, 81–85. doi: 10.2217/nmt-2015-0012

PubMed Abstract | CrossRef Full Text | Google Scholar

Seeliger, J., Evers, F., Jeworrek, C., Kapoor, S., Weise, K., Andreetto, E., et al. (2012). Cross-amyloid interaction of Aβ and IAPP at lipid membranes. Angew. Chem. Int. Ed Engl. 51, 679–683. doi: 10.1002/anie.201105877

PubMed Abstract | CrossRef Full Text | Google Scholar

Shepherd, C. E., Goyette, J., Utter, V., Rahimi, F., Yang, Z., Geczy, C. L., et al. (2006). Inflammatory S100A9 and S100A12 proteins in Alzheimer’s disease. Neurobiol. Aging 27, 1554–1563. doi: 10.1016/j.neurobiolaging.2005.09.033

PubMed Abstract | CrossRef Full Text | Google Scholar

Sherwin, E., Dinan, T. G., and Cryan, J. F. (2018). Recent developments in understanding the role of the gut microbiota in brain health and disease. Ann. N Y Acad. Sci. 1420, 5–25. doi: 10.1111/nyas.13416

PubMed Abstract | CrossRef Full Text | Google Scholar

Siegel, S. J., Bieschke, J., Powers, E. T., and Kelly, J. W. (2007). The oxidative stress metabolite 4-hydroxynonenal promotes Alzheimer protofibril formation. Biochemistry 46, 1503–1510. doi: 10.1021/bi061853s

PubMed Abstract | CrossRef Full Text | Google Scholar

Sims-Robinson, C., Kim, B., Rosko, A., and Feldman, E. L. (2010). How does diabetes accelerate Alzheimer disease pathology? Nat. Rev. Neurol. 6, 551–559. doi: 10.1038/nrneurol.2010.130

PubMed Abstract | CrossRef Full Text | Google Scholar

Singh, R., Barden, A., Mori, T., and Beilin, L. (2001). Advanced glycation end-products: a review. Diabetologia 44, 129–146. doi: 10.1007/s001250051591

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, M. A., Perry, G., Richey, P. L., Sayre, L. M., Anderson, V. E., Beal, M. F., et al. (1996). Oxidative damage in Alzheimer’s. Nature 382, 120–121. doi: 10.1038/382120b0

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, M. A., Richey Harris, P. L., Sayre, L. M., Beckman, J. S., and Perry, G. (1997). Widespread peroxynitrite-mediated damage in Alzheimer’s disease. J. Neurosci. 17, 2653–2657. doi: 10.1523/JNEUROSCI.17-08-02653.1997

PubMed Abstract | CrossRef Full Text | Google Scholar

Son, S. M., Song, H., Byun, J., Park, K. S., Jang, H. C., Park, Y. J., et al. (2012). Accumulation of autophagosomes contributes to enhanced amyloidogenic APP processing under insulin-resistant conditions. Autophagy 8, 1842–1844. doi: 10.4161/auto.21861

PubMed Abstract | CrossRef Full Text | Google Scholar

Soscia, S. J., Kirby, J. E., Washicosky, K. J., Tucker, S. M., Ingelsson, M., Hyman, B., et al. (2010). The Alzheimer’s disease-associated amyloid β-protein is an antimicrobial peptide. PLoS One 5:e9505. doi: 10.1371/journal.pone.0009505

PubMed Abstract | CrossRef Full Text | Google Scholar

Steen, E., Terry, B. M., Rivera, E. J., Cannon, J. L., Neely, T. R., Tavares, R., et al. (2005). Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease—is this type 3 diabetes? J. Alzheimers Dis. 7, 63–80. doi: 10.3233/jad-2005-7107

PubMed Abstract | CrossRef Full Text | Google Scholar

Stewart, K. L., Hughes, E., Yates, E. A., Middleton, D. A., and Radford, S. E. (2017). Molecular origins of the compatibility between glycosaminoglycans and Aβ40 amyloid fibrils. J. Mol. Biol. 429, 2449–2462. doi: 10.1016/j.jmb.2017.07.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Strachan, M. W., Deary, I. J., Ewing, F. M., and Frier, B. M. (1997). Is type II diabetes associated with an increased risk of cognitive dysfunction? A critical review of published studies. Diabetes Care 20, 438–445. doi: 10.2337/diacare.20.3.438

PubMed Abstract | CrossRef Full Text | Google Scholar

Sudduth, T. L., Schmitt, F. A., Nelson, P. T., and Wilcock, D. M. (2013). Neuroinflammatory phenotype in early Alzheimer’s disease. Neurobiol. Aging 34, 1051–1059. doi: 10.1016/j.neurobiolaging.2012.09.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Suzuki, N., Cheung, T. T., Cai, X. D., Odaka, A., Otvos, L. Jr., Eckman, C., et al. (1994). An increased percentage of long amyloid β protein secreted by familial amyloid β protein precursor (β APP717) mutants. Science 264, 1336–1340. doi: 10.1126/science.8191290

PubMed Abstract | CrossRef Full Text | Google Scholar

Takechi, R., Galloway, S., Pallebage-Gamarallage, M. M., Lam, V., and Mamo, J. C. (2010). Dietary fats, cerebrovasculature integrity and Alzheimer’s disease risk. Prog. Lipid Res. 49, 159–170. doi: 10.1016/j.plipres.2009.10.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Takeda, S., Sato, N., Uchio-Yamada, K., Sawada, K., Kunieda, T., Takeuchi, D., et al. (2010). Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Aβ deposition in an Alzheimer mouse model with diabetes. Proc. Natl. Acad. Sci. U S A 107, 7036–7041. doi: 10.1073/pnas.1000645107

PubMed Abstract | CrossRef Full Text | Google Scholar

Tamagno, E., Parola, M., Bardini, P., Piccini, A., Borghi, R., Guglielmotto, M., et al. (2005). β-site APP cleaving enzyme up-regulation induced by 4-hydroxynonenal is mediated by stress-activated protein kinases pathways. J. Neurochem. 92, 628–636. doi: 10.1111/j.1471-4159.2004.02895.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Tan, M. S., Yu, J. T., and Tan, L. (2013). Bridging integrator 1 (BIN1): form, function, and Alzheimer’s disease. Trends Mol. Med. 19, 594–603. doi: 10.1016/j.molmed.2013.06.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Taylor, R. C., and Dillin, A. (2011). Aging as an event of proteostasis collapse. Cold Spring Harb. Perspect. Biol. 3:a004440. doi: 10.1101/cshperspect.a004440

PubMed Abstract | CrossRef Full Text | Google Scholar

Thal, D. R., Rüb, U., Orantes, M., and Braak, H. (2002). Phases of A β-deposition in the human brain and its relevance for the development of AD. Neurology 58, 1791–1800. doi: 10.1212/wnl.58.12.1791

PubMed Abstract | CrossRef Full Text | Google Scholar

Thaler, J. P., Yi, C. X., Schur, E. A., Guyenet, S. J., Hwang, B. H., Dietrich, M. O., et al. (2012). Obesity is associated with hypothalamic injury in rodents and humans. J. Clin. Invest. 122, 153–162. doi: 10.1172/JCI59660

PubMed Abstract | CrossRef Full Text | Google Scholar

Thornton, E., Vink, R., Blumbergs, P. C., and Van Den Heuvel, C. (2006). Soluble amyloid precursor protein α reduces neuronal injury and improves functional outcome following diffuse traumatic brain injury in rats. Brain Res. 1094, 38–46. doi: 10.1016/j.brainres.2006.03.107

PubMed Abstract | CrossRef Full Text | Google Scholar

Tukel, C., Nishimori, J. H., Wilson, R. P., Winter, M. G., Keestra, A. M., van Putten, J. P., et al. (2010). Toll-like receptors 1 and 2 cooperatively mediate immune responses to curli, a common amyloid from enterobacterial biofilms. Cell. Microbiol. 12, 1495–1505. doi: 10.1111/j.1462-5822.2010.01485.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Ubelmann, F., Burrinha, T., Salavessa, L., Gomes, R., Ferreira, C., Moreno, N., et al. (2017). Bin1 and CD2AP polarise the endocytic generation of β-amyloid. EMBO Rep. 18, 102–122. doi: 10.15252/embr.201642738

PubMed Abstract | CrossRef Full Text | Google Scholar

Ulland, T. K., Song, W. M., Huang, S. C., Ulrich, J. D., Sergushichev, A., Beatty, W. L., et al. (2017). TREM2 maintains microglial metabolic fitness in Alzheimer’s disease. Cell 170, 649.e13–663.e13. doi: 10.1016/j.cell.2017.07.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Umegaki, H. (2014). Type 2 diabetes as a risk factor for cognitive impairment: current insights. Clin. Interv. Aging 9, 1011–1019. doi: 10.2147/cia.s48926

PubMed Abstract | CrossRef Full Text | Google Scholar

Uryu, K., Chen, X. H., Martinez, D., Browne, K. D., Johnson, V. E., Graham, D. I., et al. (2007). Multiple proteins implicated in neurodegenerative diseases accumulate in axons after brain trauma in humans. Exp. Neurol. 208, 185–192. doi: 10.1016/j.expneurol.2007.06.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Valente, T., Gella, A., Fernàndez-Busquets, X., Unzeta, M., and Durany, N. (2010). Immunohistochemical analysis of human brain suggests pathological synergism of Alzheimer’s disease and diabetes mellitus. Neurobiol. Dis. 37, 67–76. doi: 10.1016/j.nbd.2009.09.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Vassilaki, M., Christianson, T. J., Mielke, M. M., Geda, Y. E., Kremers, W. K., Machulda, M. M., et al. (2017). Neuroimaging biomarkers and impaired olfaction in cognitively normal individuals. Ann. Neurol. 81, 871–882. doi: 10.1002/ana.24960

PubMed Abstract | CrossRef Full Text | Google Scholar

Venegas, C., Kumar, S., Franklin, B. S., Dierkes, T., Brinkschulte, R., Tejera, D., et al. (2017). Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease. Nature 552, 355–361. doi: 10.1038/nature25158

PubMed Abstract | CrossRef Full Text | Google Scholar

Wahlster, L., Arimon, M., Nasser-Ghodsi, N., Post, K. L., Serrano-Pozo, A., Uemura, K., et al. (2013). Presenilin-1 adopts pathogenic conformation in normal aging and in sporadic Alzheimer’s disease. Acta Neuropathol. 125, 187–199. doi: 10.1007/s00401-012-1065-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., Klechikov, A. G., Gharibyan, A. L., Wärmländer, S. K., Jarvet, J., Zhao, L., et al. (2014). The role of pro-inflammatory S100A9 in Alzheimer’s disease amyloid-neuroinflammatory cascade. Acta Neuropathol. 127, 507–522. doi: 10.1007/s00401-013-1208-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, C., Iashchishyn, I. A., Pansieri, J., Nyström, S., Klementieva, O., Kara, J., et al. (2018). S100A9-driven amyloid-neuroinflammatory cascade in traumatic brain injury as a precursor state for Alzheimer’s disease. Sci. Rep. 8:12836. doi: 10.1038/s41598-018-31141-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., Cella, M., Mallinson, K., Ulrich, J. D., Young, K. L., Robinette, M. L., et al. (2015). TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071. doi: 10.1016/j.cell.2015.01.049

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Q., Jia, J., Qin, W., Wu, L., Li, D., Wang, Q., et al. (2015). A novel AβPP M722K mutation affects amyloid-β secretion and tau phosphorylation and may cause early-onset familial Alzheimer’s disease in chinese individuals. J. Alzheimers Dis. 47, 157–165. doi: 10.3233/jad-143231

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Y., Ulland, T. K., Ulrich, J. D., Song, W., Tzaferis, J. A., Hole, J. T., et al. (2016). TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. J. Exp. Med. 213, 667–675. doi: 10.1084/jem.20151948

PubMed Abstract | CrossRef Full Text | Google Scholar

Weiner, M. W., Harvey, D., Hayes, J., Landau, S. M., Aisen, P. S., Petersen, R. C., et al. (2017). Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer’s disease in Vietnam Veterans using the Alzheimer’s disease neuroimaging initiative: preliminary report. Alzheimers Dement. 3, 177–188. doi: 10.1016/j.trci.2017.02.005

PubMed Abstract | CrossRef Full Text | Google Scholar

West, M. J., Coleman, P. D., Flood, D. G., and Troncoso, J. C. (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet 344, 769–772. doi: 10.1016/s0140-6736(94)92338-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Westermark, P., Li, Z. C., Westermark, G. T., Leckström, A., and Steiner, D. F. (1996). Effects of β cell granule components on human islet amyloid polypeptide fibril formation. FEBS Lett. 379, 203–206. doi: 10.1016/0014-5793(95)01512-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Weyer, S. W., Klevanski, M., Delekate, A., Voikar, V., Aydin, D., Hick, M., et al. (2011). APP and APLP2 are essential at PNS and CNS synapses for transmission, spatial learning and LTP. EMBO J. 30, 2266–2280. doi: 10.1038/emboj.2011.119

PubMed Abstract | CrossRef Full Text | Google Scholar

Wisniewski, T., Lalowski, M., Golabek, A., Vogel, T., and Frangione, B. (1995). Is Alzheimer’s disease an apolipoprotein E amyloidosis? Lancet 345, 956–958. doi: 10.1016/S0140-6736(95)90701-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Wyss-Coray, T., Loike, J. D., Brionne, T. C., Lu, E., Anankov, R., Yan, F., et al. (2003). Adult mouse astrocytes degrade amyloid-β in vitro and in situ. Nat. Med. 9, 453–457. doi: 10.1038/nm838

PubMed Abstract | CrossRef Full Text | Google Scholar

Xia, X., Jiang, Q., McDermott, J., and Han, J. J. (2018). Aging and Alzheimer’s disease: comparison and associations from molecular to system level. Aging Cell 17:e12802. doi: 10.1111/acel.12802

PubMed Abstract | CrossRef Full Text | Google Scholar

Xing, J., Titus, A. R., and Humphrey, M. B. (2015). The TREM2-DAP12 signaling pathway in Nasu-Hakola disease: a molecular genetics perspective. Res. Rep. Biochem. 5, 89–100. doi: 10.2147/rrbc.s58057

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, Y., Wu, Y., Zhang, S., and Song, W. (2013). High glucose promotes Aβ production by inhibiting APP degradation. PLoS One 8:e69824. doi: 10.1371/journal.pone.0069824

PubMed Abstract | CrossRef Full Text | Google Scholar

Yates, S. C., Zafar, A., Hubbard, P., Nagy, S., Durant, S., Bicknell, R., et al. (2013). Dysfunction of the mTOR pathway is a risk factor for Alzheimer’s disease. Acta Neuropathol. Commun. 1:3. doi: 10.1186/2051-5960-1-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeh, F. L., Wang, Y., Tom, I., Gonzalez, L. C., and Sheng, M. (2016). TREM2 binds to apolipoproteins, including APOE and CLU/APOJ and thereby facilitates uptake of amyloid-β by microglia. Neuron 91, 328–340. doi: 10.1016/j.neuron.2016.06.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Yuan, P., Condello, C., Keene, C. D., Wang, Y., Bird, T. D., Paul, S. M., et al. (2016). TREM2 haplodeficiency in mice and humans impairs the microglia barrier function leading to decreased amyloid compaction and severe axonal dystrophy. Neuron 92, 252–264. doi: 10.1016/j.neuron.2016.09.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., and Song, W. (2017). Islet amyloid polypeptide: another key molecule in Alzheimer’s pathogenesis? Prog. Neurobiol. 153, 100–120. doi: 10.1016/j.pneurobio.2017.03.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, Y., and Lukiw, W. J. (2015). Microbiome-generated amyloid and potential impact on amyloidogenesis in Alzheimer’s disease (AD). J. Nat. Sci. 5:177. doi: 10.4172/2161-0460.1000177

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, Y., Wu, X., Li, X., Jiang, L. L., Gui, X., Liu, Y., et al. (2018). TREM2 is a receptor for β-amyloid that mediates microglial function. Neuron 97, 1023.e7–1031.e7. doi: 10.1016/j.neuron.2018.01.031

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: Alzheimer’s disease, β-amyloid, aggregation, age, diabetes

Citation: Zhang X, Fu Z, Meng L, He M and Zhang Z (2018) The Early Events That Initiate β-Amyloid Aggregation in Alzheimer’s Disease. Front. Aging Neurosci. 10:359. doi: 10.3389/fnagi.2018.00359

Received: 11 August 2018; Accepted: 22 October 2018;
Published: 13 November 2018.

Edited by:

David Baglietto-Vargas, University of California, Irvine, United States

Reviewed by:

Angela Gomez-Arboledas, Universidad de Málaga, Spain
Marisa Vizuete, Universidad de Sevilla, Spain

Copyright © 2018 Zhang, Fu, Meng, He and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhentao Zhang, zzt.104@163.com

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