Citation: Khue Vu Nguyen. Special Issue: Alzheimer’s disease[J]. AIMS Neuroscience, 2018, 5(1): 74-80. doi: 10.3934/Neuroscience.2018.1.74
[1] | Khue Vu Nguyen . Epigenetic modulation of human neurobiological disorders: Lesch-Nyhan disease as a model disorder. AIMS Neuroscience, 2025, 12(2): 58-74. doi: 10.3934/Neuroscience.2025005 |
[2] | Danton H. O’Day . Alzheimer’s Disease: A short introduction to the calmodulin hypothesis. AIMS Neuroscience, 2019, 6(4): 231-239. doi: 10.3934/Neuroscience.2019.4.231 |
[3] | Craig T. Vollert, Jason L. Eriksen . Microglia in the Alzheimers brain: a help or a hindrance?. AIMS Neuroscience, 2014, 1(3): 210-224. doi: 10.3934/Neuroscience.2014.3.210 |
[4] | Khue Vu Nguyen . Encephalomalacia/gliosis, deep venous thrombosis, and cancer in Arg393His antithrombin Hanoi and the potential impact of the β-amyloid precursor protein (APP) on thrombosis and cancer. AIMS Neuroscience, 2022, 9(2): 175-215. doi: 10.3934/Neuroscience.2022010 |
[5] | Khue Vu Nguyen . β-Amyloid precursor protein (APP) and the human diseases. AIMS Neuroscience, 2019, 6(4): 273-281. doi: 10.3934/Neuroscience.2019.4.273 |
[6] | Jean A. Rondal . Down syndrome: A curative prospect?. AIMS Neuroscience, 2020, 7(2): 168-193. doi: 10.3934/Neuroscience.2020012 |
[7] |
Frank O. Bastian .
Is Alzheimers Disease Infectious? Relative to the CJD Bacterial Infection Model of Neurodegeneration. AIMS Neuroscience, 2015, 2(4): 240-258. doi: 10.3934/Neuroscience.2015.4.240 |
[8] | Khue Vu Nguyen . Potential molecular link between the β-amyloid precursor protein (APP) and hypoxanthine-guanine phosphoribosyltransferase (HGprt) enzyme in Lesch-Nyhan disease and cancer. AIMS Neuroscience, 2021, 8(4): 548-557. doi: 10.3934/Neuroscience.2021030 |
[9] | Kiran Kumar Siddappaji, Shubha Gopal . Molecular mechanisms in Alzheimer's disease and the impact of physical exercise with advancements in therapeutic approaches. AIMS Neuroscience, 2021, 8(3): 357-389. doi: 10.3934/Neuroscience.2021020 |
[10] | Dimitra Anatolou, Marios G. Krokidis . Computational analysis of peripheral blood RNA sequencing data unravels disrupted immune patterns in Alzheimer's disease. AIMS Neuroscience, 2024, 11(2): 103-117. doi: 10.3934/Neuroscience.2024007 |
It is with great pleasure to introduce a special issue, namely "Alzheimer's disease", which is scheduled to appear this year in AIMS Neuroscience. I cordially invite authors to contribute their excellent works to this exciting forum. Submissions are now open and will be fully considered for publication.
Alzheimer's disease (AD) is renowned as the most prevalent multifactorial neurodegenerative disorder in today's world. It is the most common cause of dementia in the elderly, affecting about 46.8 million persons worldwide, and will reach a height of 74.7 million in 2030 and 131.5 million in 2050 [1]. In AD, a deterioration of memory and other cognitive domains that leads to death within 3 to 9 years after diagnosis. Owing to the dramatic increase in the population as the year and age progresses, AD is not only considered to be life threatening but also an economic and social burden to the health-care system. Since 1992, the amyloid cascade hypothesis has played a prominent role in explaining the etiology AD. It proposes that the deposition of amyloid-β (Aβ) peptides, resulting from a chronic imbalance between Aβ production and Aβ clearance, is the initial pathological event in AD leading to the formation of extracellular senile plaques (SPs) and then to intracellular neurofibrillary tangles (NFTs) of hyperphosphorylated tau proteins, neuronal cell death and, ultimately, dementia [2]. Aβ peptides are natural products of metabolism consisting of 36 to 43 amino acids. Monomers of Aβ40 are much more prevalent than the aggregation-prone and damaging Ab42 species. Aβ peptides originate from proteolysis of the β-amyloid precursor protein (APP) by sequential enzymatic action of beta-site amyloid precursor protein-cleaving enzyme 1 (BACE-1), a b-secretase, and γ-secretase, a protein complex with presenilin 1 at its catalytic core [3]. AD is currently classified by age at onset and genetic status [4]. Sporadic AD (SAD) is characterized by late age at onset (onset age > 65 years) and accounts for ~99% of AD cases. Familial AD (FAD) accounting for ~1% of cases, is characterized by early age at onset (onset age < 65 years) and a genetic component [4]. In these cases, mutations in the APP gene, and the presenilin (PS) genes PS1 and PS2 are known to be associated with FAD.
Advances in research have been translated into several drug candidates with disease-modifying potential, many of which are now being evaluated in clinical trials [5]. Studies in transgenic mouse models of AD suggest that the majority of these types of disease-modifying drugs may be most effective early on in the process of Aβ aggregation, and be less effective in later stages when there is severe plaque pathology and neurodegeneration [6]. However, the current diagnostic criteria from the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) that are used to identify patients with AD who have overt dementia correspond to neuropathologically advanced disease. Consequently, early recognition of the disease needs to be improved. Indeed, it is estimated that interventions that could delay the clinical onset of dementia by 1 year would reduce the prevalence in 2050 by 9 million cases [6]. Several multimodal core biomarkers candidates derived from structural, functional and metabolic neuroimaging, and from neurochemistry and genetic studies of AD have been investigated but their values in pivotal efficacy studies for AD are still limited [6]. Examples of genetic analysis in biomarker research include testing for PS1, PS2, and APP mutations; however, as PS1, PS2, or APP mutations are rare, screening for these mutations currently has to be organized on an ad hoc basis. In addition, over the last decades, the apolipoprotein E gene (APOE) with three variants referred as APOE ε2, ε3 and ε4 alleles, has been irrefutably recognized as a major risk factor for SAD. Unlike APP, PS1, and PS2 mutations that are fully penetrant (causal), APOE ε4 allele is the most important genetic risk factor for SAD. Only about 20–25% of the general population carries one or more ε4 alleles, whereas 50–65% of people with AD are ε4 carriers. The presence of at least one ε4 allele has ben associated with reduced age at onset rather than increases in the lifetime risk of developing AD. Homozygous ε4 allele carriers develop AD up to 10 years earlier than individuals who do not have this allele. Nonetheless, APOE ε4 does not account for all genetic variation in AD. The use of APOE as a diagnostic or predictive factor in clinical practice is therefore not warranted. Recent works have demonstrated a rare functional variant (R47H) in triggering receptor expressed on myeloid cells 2 gene (TREM2), encoding TREM2 transmembrane glycoprotein, increase susceptibility to SAD, with an odds ratio similar to that of the APOE ε4 allele [7,8]. Emerging evidence has demonstrated that TREM2 could suppress inflammatory response by repression of microglia-mediated cytokine production and secretion, which may prevent inflammation-induced bystander damage of neurons. Proteolytic processing of TREM2 results in release of a soluble, ectodomain-containing TREM2 fragment (sTREM2) that is detectable in cerebrospinal fluid (CSF) and serum. Changes in the levels of AD-related proteins in CSF, such as Aβ42 and tau, reflect the presence and progression of AD pathology in the brain. Studies of SAD and autosomal-dominant AD populations found that sTREM2 levels were increased in the CSF of AD patients and that sTREM2 levels were positively correlated with levels of tau and phosphorylated tau (p-tau); however, no robust correlation between sTREM2 and Aβ42. This suggests that sTREM2 levels increase in association with neuronal damage resulting from an inflammatory process in AD rather than with the appearance of Aβ plaques per se. Then, much work remains to both validate the relationship between sTREM2 and AD and understand the mechanism underlying a potential relationship between TREM2 and neuronal toxicity. Although several studies indicate that elevated sTREM2 levels may mark the transition from preclinical AD to symptomatic AD, some studies have reported contrary findings of either decreased or unchanged CSF sTREM2 levels in AD patient populations. In addition, although most of studies support the hypothesis that inflammation is present as a pathogenetic force in the process of AD, it still remains unclear whether inflammation is a causative factor or just a consequence of this disease. Understanding the role of TREM2 in regulating the response of the innate immune system to Aβ and/or tau pathology may lead to the discovery of novel biomarkers and therapeutic strategies. Finally, up to present, none of the imaging, genetic or biochemical markers has been sufficiently qualified as a surrogate for the actual NINCDS-ADRDA. There is a growing need for biomarkers of AD pathology to improve drug development related to the disorder. Indeed, biomarkers are needed to monitor drug safety, to identify individuals who are most likely to respond to specific treatments, to stratify pre-symptomatic patients and to quantify the benefits of treatments [6].
Up to now, six broad therapeutic strategies based on Aβ biology have been proposed [5,9]. First, one could attempt to partially inhibit either of the two proteases, β-and γ-secretase, that generate Aβ from APP. Second, one could attempt to prevent the oligomerization of Aβ or enhance its clearance from the cerebral cortex. The third broad approach is an anti-inflammatory strategy based on the observation that a cellular inflammatory response in the cerebral cortex is elicited by the progressive accumulation of Aβ. The fourth approach is based on modulating cholesterol homeostasis. The fifth strategy is based on the observation that Aβ aggregation is, in part, dependent on the metal ions Cu2+ and Zn2+. The sixth broad amyloid-based strategy is to prevent the synaptotoxic and neurodegenerative effects putatively triggered by Ab accumulation. Numerous approaches have been contemplated, including the use of compounds with antioxidant, neuroprotective, and/or neurotrophic properties, but again, no slowing of cognitive decline has been documented in human to date. Thus, for now, clinically validated treatments for AD remain confined to symptomatic interventions such as treatment with acetylcholinesterase inhibitors such as donepezil, rivastigmine, galantamine, huperzine-A, and memantine (a partial antagonist of N-methyl-D-aspartate receptor, NMDAR), and drugs that ameliorate behavioral disturbances such as antidepressants, sleeping tablets, tranquilizers [5,9].
Although the amyloid cascade hypothesis has been dominated the field for more than 20 years, and offered a broad framework to explain AD pathogenesis, it is currently lacking in detail, and certain observations do not fit easily with the simplest version of the hypothesis [9,10]. The most frequently voiced objection is that the number of amyloid deposits in the brain does not correlate well with the degree of cognitive impairment that the patient experienced in life. Furthermore, over recent years, data have illustrated that reciprocal interactions between APP and its various metabolites, including Aβ, can powerfully regulate key neuronal functions including cell excitability, synaptic transmission and neural plasticity [11]. As a consequence, perturbation of some of these activities may contribute to AD pathogenesis and neurodegeneration in an Aβ-dependent or Aβ-independent manner, and therefore (a) SPs and NFTs may be developed independently and able to interact with each other, and thereby promoting the AD neuropathological cascade, and (b) SPs and NFTs may be the products rather than causes of neurodegeneration in AD [11]. We are entering an era in which the unitary view of AD as a single sequential pathological pathway with Ab considered as the only initial and causal event is likely to be progressively replaced by a more complex picture in which AD is considered as a multi-parameter pathology that is subtended by several partly independent pathology processes. In this disease, neuronal injury could be caused by different factors, with various possible sequences of pathological events. In contrast to monogenic disease, SAD exhibits numerous non-Mendelian anomalies that suggest epigenetic modifications, gene-gene and/or gene-environment interactions in the disease etiology. Compared to genetic causes, epigenetic factors are probably much more suited to explain the observed anomalies in SAD as aberrant epigenetic patterns may be acquired during many developmental stages. The epigenome is particularly susceptible to deregulation during early embryonal and neonatal development, puberty and especially old age, which is the most important known risk factor for AD [11,12,13]. Indeed, mutations in FAD represent a very small percentage (~1%), and ~99% of cases are SAD [4]. Multiple studies conducted to determine disease-causative loci have revealed that AD is highly complex and heterogeneous in nature. Therefore, non-genetic factors, such as epigenetic modifications, gene-gene and/or gene-environment interactions may also be causative and currently the subject of intense research [11,12,13]. As such, it is important to continue to investigate the normal function of APP. Understanding its physiological function will not only provide insights into the pathogenesis of AD but may also prove vital in the development of an effective therapy. Recently, reports on epigenetic regulation of alternative splicing of APP gene resulting in the presence of various APP-mRNA isoforms and their quantification, especially for identifying the most abundant one that may decisive for the normal status or disease risk, have been described [14,15,16,17]. These findings may provide new directions for the research in neurodevelopmental and neurodegenerative disorders in which the APP gene is involved in the pathogenesis of diseases such as autism [18,19], fragile X syndrome [19], amyotrophic lateral sclerosis [20], multiple sclerosis [21], and AD [10,11,19], and may pave the way for new strategies applicable to rational antisense drugs design [22].
In conclusion, it is clear that APP undergoes complex regulation and is important for neuronal and synaptic function in both central and peripheral nervous systems. This may involve the APP extracellular domain, the APP intracellular domain, the Aβ sequence or, indeed, cross communication among these motifs [11]. Then, APP, a house keeping gene [23], and endogenous ligand (http://www.genenames.org/genefamilies/EnDOLIG), is an important molecular hub at the center of interacting pathways and acts as a permissive factor for various neurodevelopmental and neural circuit processes [24], altered APP processing may affect brain function through a host of altered cellular and molecular events occurring in neurodevelopmental and neurodegenerative disorders such as autism, fragile X syndrome, and AD. As such, a more complete understanding of AD pathogenesis will likely require greater insights into the physiological function of APP.
[1] | World Alzheimer Report 2015. |
[2] |
Hardy JA, Higgin GA (1992) Alzheimer's disease: the amyloid cascade hypothesis. Science 256: 184–185. doi: 10.1126/science.1566067
![]() |
[3] | Haass C, Selkoe DJ (2007) Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid beta-peptide. Nat Rev Mol Cell Biol 8: 101–112. |
[4] |
Bettens K, Sleegers K, Van Broeckhoven C (2010) Current status on Alzheimer's disease molecular genetics: from past, to present, to future. Hum Mol Genet 19: R4–R11. doi: 10.1093/hmg/ddq142
![]() |
[5] |
Klafki HW (2006) Therapeutic approaches to Alzheimer's disease. Brain 129: 2840–2855. doi: 10.1093/brain/awl280
![]() |
[6] | Hampel H, Frank R, Broich K, et al. (2010) Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev 9: 560–574. |
[7] |
Jiang T, Yu JT, Zhu XC, et al. (2013) TREM2 in Alzheimer's disease. Mol Neurobiol 48: 180–185. doi: 10.1007/s12035-013-8424-8
![]() |
[8] |
Ulrich JD, Ulland TK, Colonna M, et al. (2017) Elucidating the role of TREM2 in Alzheimer's disease. Neuron 94: 237-248. doi: 10.1016/j.neuron.2017.02.042
![]() |
[9] |
Hardy J, Selkoe DJ (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
![]() |
[10] |
Chetelat G (2013) Ab-independent processes-rethinking preclinical AD. Nat Rev Neurol 9: 123–124. doi: 10.1038/nrneurol.2013.21
![]() |
[11] | Nguyen KV (2015) The human b-amyloid precursor protein: biomolecular and epigenetic aspects. BioMol Concepts 6: 11–32. |
[12] |
Wang SC, Oelze B, Schumacher A (2008) Age-specific epigenetic drift in late-onset Alzheimer's disease. PLoS One 3: e2698. doi: 10.1371/journal.pone.0002698
![]() |
[13] |
Combarros O, Cortina-Borja M, Smith AD, et al. (2009) Epistasis in sporadic Alzheimer's disease. Neurobiol Aging 30: 1333–1349. doi: 10.1016/j.neurobiolaging.2007.11.027
![]() |
[14] |
Nguyen KV (2014) Epigenetic regulation in amyloid precursor protein and the Lesch-Nyhan syndrome. Biochem Biophys Res Commun 446: 1091–1095. doi: 10.1016/j.bbrc.2014.03.062
![]() |
[15] |
Nguyen KV (2015) Epigenetic regulation in amyloid precursor protein with genomic rearrangements and the Lesch-Nyhan syndrome. Nucleosides Nucleotides Nucleic Acids 34: 674–690. doi: 10.1080/15257770.2015.1071844
![]() |
[16] |
Nguyen KV, Nyhan WL (2017) Quantification of various APP-mRNA isoforms and epistasis in Lesch-Nyhan disease. Neurosci Lett 643: 52–58. doi: 10.1016/j.neulet.2017.02.016
![]() |
[17] | Nguyen KV, Leydiker K, Wang R, et al. (2017) A neurodevelopmental disorder with a nonsense mutation in the Ox-2 antigen domain of the amyloid precursor protein (APP) gene. Nucleosides Nucleotides Nucleic Acids 36: 317–327. |
[18] |
Ray B, Long JM, Sokol DK, et al. (2011) Increased secreted amyloid precursor protein-a (sAPPa) in severe autism: proposal of a specific, anabolic pathway and putative biomarker. PLoS One 6: e20405. doi: 10.1371/journal.pone.0020405
![]() |
[19] |
Sokol DK, Maloney B, Long JM, et al. (2011) Autism, Alzheimer's disease, and fragile X, APP, FMRP, and mGluR5 are molecular links. Neurology 76: 1344–1352. doi: 10.1212/WNL.0b013e3182166dc7
![]() |
[20] |
Bryson JB, Hobbs C, Parsons MJ, et al. (2012) Amyloid precursor protein (APP) contributes to pathology in the SODG93A mouse model of amyotrophic lateral sclerosis. Hum Mol Genet 21: 3871–3882. doi: 10.1093/hmg/dds215
![]() |
[21] | Matias-Guiu JA, Oreja-Guevara C, Cabrera-Martin MN, et al. (2016) Amyloid proteins and their role in multiple sclerosis. Considerations in the use of amyloid-PET imaging. Front Neurol 7: 53. |
[22] | Saonere JA (2011) Antisense therapy, a magic bullet for the treatment of various diseases: present and future prospects. J Med Genet Genomics 3: 77–83. |
[23] | Sabaum JM, Weidermann A, Lemaire HG, et al. (1988) The promoter of Alzheimer's disease amyloid A4 precursor gene. EMBO J 7: 2807–2813. |
[24] |
Nicolas M, Hassan BA (2014) Amyloid precursor protein and neural development. Development 141: 2543–2548. doi: 10.1242/dev.108712
![]() |
1. | Sinan Bilginer, Halise I. Gul, Baris Anil, Yeliz Demir, Ilhami Gulcin, Synthesis and in silico studies of triazene‐substituted sulfamerazine derivatives as acetylcholinesterase and carbonic anhydrases inhibitors, 2021, 354, 0365-6233, 2000243, 10.1002/ardp.202000243 | |
2. | Keynes Masayoshi Kanno, Edgard Afonso Lamounier, Alexandre Cardoso, Ederaldo José Lopes, Saadallah Azor Fakhouri Filho, Assisting individuals with Alzheimer’s disease using mobile augmented reality with voice interaction: an acceptance experiment with individuals in the early stages, 2019, 35, 2446-4732, 223, 10.1007/s42600-019-00025-y | |
3. | Khue Vu Nguyen, β-Amyloid precursor protein (APP) and the human diseases, 2019, 6, 2373-7972, 273, 10.3934/Neuroscience.2019.4.273 | |
4. | Laura Trujillo-Estrada, Elisabeth Sanchez-Mejias, Raquel Sanchez-Varo, Juan Antonio Garcia-Leon, Cristina Nuñez-Diaz, Jose Carlos Davila, Javier Vitorica, Frank M. LaFerla, Ines Moreno-Gonzalez, Antonia Gutierrez, David Baglietto-Vargas, Animal and Cellular Models of Alzheimer’s Disease: Progress, Promise, and Future Approaches, 2021, 1073-8584, 107385842110017, 10.1177/10738584211001753 | |
5. | Ruolin Li, Ming Ren, Yingxin Yu, Anti‐PrP monoclonal antibody as a novel treatment for neurogenesis in mouse model of Alzheimer's disease, 2021, 11, 2162-3279, 10.1002/brb3.2365 | |
6. | John Bufalini, Paul Eslinger, Erik Lehman, Daniel R. George, Effects of a Personalized Music Intervention for Persons with Dementia and their Caregivers, 2022, 6, 25424823, 43, 10.3233/ADR-210043 | |
7. | Zeng-Ling Liu, Fei-Fei Hua, Lei Qu, Na Yan, Hui-Fang Zhang, Evaluating serum CXCL12, sCD22, Lp-PLA2 levels and ratios as biomarkers for diagnosis of Alzheimer's disease, 2024, 14, 2220-3206, 380, 10.5498/wjp.v14.i3.380 |