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Rabaneda Bueno, R. Targets for Therapeutic Treatment of Alzheimer’s Disease. Encyclopedia. Available online: https://encyclopedia.pub/entry/17524 (accessed on 27 July 2024).
Rabaneda Bueno R. Targets for Therapeutic Treatment of Alzheimer’s Disease. Encyclopedia. Available at: https://encyclopedia.pub/entry/17524. Accessed July 27, 2024.
Rabaneda Bueno, Rubén. "Targets for Therapeutic Treatment of Alzheimer’s Disease" Encyclopedia, https://encyclopedia.pub/entry/17524 (accessed July 27, 2024).
Rabaneda Bueno, R. (2021, December 23). Targets for Therapeutic Treatment of Alzheimer’s Disease. In Encyclopedia. https://encyclopedia.pub/entry/17524
Rabaneda Bueno, Rubén. "Targets for Therapeutic Treatment of Alzheimer’s Disease." Encyclopedia. Web. 23 December, 2021.
Targets for Therapeutic Treatment of Alzheimer’s Disease
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This entry is adapted from the peer-reviewed paper 10.3390/genes12121959

Alzheimer's disease (AD) is an adult-onset dementia characterised by progressive neurodegeneration and widespread brain damage, leading to long-term functional and cognitive impairment and greatly reduced life expectancy. While early genetic studies uncovered several polymorphisms associated with AD, more recently genome-wide association analyses and massive sequencing techniques have revealed numerous novel susceptibility genes, differentially expressed genes, and disease traits. Nevertheless, the mechanisms underlying disease onset are still not fully understood and, as with other complex human diseases, the causes of low heritability are unclear. Epigenetic mechanisms, in which changes in gene expression do not depend on changes in genotype, have been postulated as key factors in understanding the development of AD and the processes that influence age-related changes and various neurological diseases. Research on specific mutations in risk genes and epigenetic markers is increasing, enabling the development of therapeutic treatments that target the neuropathological changes associated with AD and, in many cases, are expected to reverse at least some of the cognitive impairment associated with the disease. The application of effective therapies therefore requires a growing understanding of the genetic risk factors and underlying epigenetic mechanisms involved in AD. 

Alzheimer’s disease Aβ immunotherapy Epistasis Genetic risk factors Epigenome Genome-wide association analysis (GWAS)

1. Alzheimer's Disease and the Genome: Epistasis and Key Susceptibility Genes-Targeted Therapies.  

Alzheimer's disease (AD) is characterized by the accumulation of Aβ-peptide and the formation of NFTs, preceded by the expression of a number of genetic factors that interact through complex biochemical pathways [1]. The rare familial form of AD occurs in less than 6% of patients, of whom approximately 60% have a family history of AD and 13% have an autosomal dominant inheritance. This form of AD occurs at less than 65 years of age as Early Onset AD (EOAD), usually in the thirties or forties. The prevalence of neurodegenerative dementia associated with EOAD is characterized by rapid disease onset and shorter estimated survival [2], which worsens as patients age and reach the age group of 65 years [3]. This has been associated with the deposition of phosphorylated tau protein (p-Tau) in the brain at a very early stage [4]. In contrast, in the multifactorial sporadic form, susceptibility is determined by multiple genetic factors interacting with environmental factors. This form usually occurs as a late onset AD (LOAD) around the age of 65 and is genetically more complex, both in terms of inheritance and etiopathology [5][6][7].

1.1. Major Susceptibility Genes

Candidate gene studies have compared the frequency of genetic variants and identified the protein-coding genes of the amyloid-β precursor protein (APP) and the PS1 and PS2 subunits of presenilins (PSEN1, PSEN2) and apolipoprotein E (ApoE) [8][9][10] as important susceptibility factors for AD. The most common allelic form of ApoE is ε3 (ApoE3), followed by the ε4 allele (ApoE4), which is the predominant risk factor for sporadic AD, and the ε2 allele (ApoE2) is the rarest form. In the European population, the observed allele frequencies are 74.98% ApoE3, 8.62% ApoE2 and 16.40% ApoE4 in individuals under or equal to 65 years of age and 78.74%, 7.74% and 13.52%, respectively, in individuals above this age [11]. Substitution of several amino acids leads to a structural change that promotes binding to very low density lipoproteins (VLDL) in ApoE4 and to high density lipoproteins (HDL) in ApoE3 and ApoE2. In the case of ApoE2, this structural affinity for HDL determines its neuroprotective properties, especially in women, who have higher levels of HDL [12], insulin-like growth factor I (IGF-I) and glucose transporter type 4 (GLUT4) compared to the other two isoforms [13]. The genetic risk of ApoE is also age-dependent, with the ApoE4/ApoE4 genotype generally occurring in individuals aged 65 years and the ApoE3/ApoE4 genotype occurring in individuals aged 85 years, with no significant differences between the sexes [10][14]. ApoE4 has an estimated heritability of 0.13-0.33 and about 25% of overall heritability (LOAD), but these estimates tend to increase significantly in twin studies [15][16][17].Autosomal dominant variants of PSEN1 and PSEN2 genes and Aβ precursor genes are responsible for 5-10% of EOAD cases, while the remaining cases of AD are explained by the effects of different polygenic variants in an additive model [18]. These genes are expressed through common biological pathways with Aβ metabolism, so mutations in them have great potential to influence amyloid pathogenicity and early onset [19]. A missense variant of APP at codon 673 increases the risk of AD [20], and another in PSEN1 alters the processing of APP and promotes the accumulation of Aβ plaques [17]. Mutations in the Aβ sequence of APP can promote fibrillation and early cognitive impairment or cause inhibition of Aβ-peptide that prevents neuronal dysfunction [21].

Bridging integrator 1 (BIN1) is the second major risk locus of AD, and increased expression has been associated with cognitive impairment and accelerated disease onset due to tauopathy [22][23][24]. CLU is considered the third most important genome-wide risk locus for LOAD, which influences the disease progression of MCI [25]. Along with variants of CD33, MS4A4, and CD2AP [26], they have been found to increase the risk of LOAD in Caucasian populations but not in Asian populations, or association studies in this population have been inconsistent. CLU is particularly regulated by interactions with other risk loci involved in various regulatory processes of tau pathology, such as Aβ clearance, Aβ binding and deposition [27][28] and cerebral neuroinflammatory stress responses [29][30][31]. Alone or in interaction with other loci, increased expression of CLU leads to neuronal dysfunction and amyloid plaque formation [32][33][34]. There are other important protein-coding genes that increase the risk of LOAD, such as PICALM, APOC1, SORL1, CR1, ABCA7 and ESR1 [17][35][36][37][38][39], as well as differentially expressed genes in brain regions associated with amyloid pathologies, such as the gene SERPINA3. In particular, this gene encodes the α1-antichymotrypsin protein, which is upregulated in the brain of AD patients [40][41] and exerts an inhibitory effect on serine protease enzymes associated with dementia risk [42]. SERPINA3 induces neuronal death by promoting increased p-Tau levels, Aβ deposition and the formation of NFTs [43][44][45].

NGS sequencing studies documented several associations between TREM2 and EOAD and LOAD forms in Caucasian populations, with a number of variants increasing the risk of LOAD two- to fourfold, comparable to that of an ApoE4 carrier [46][47]. R47H is the best known variant of TREM2, which significantly increases the risk of LOAD and promotes the rapid onset of symptoms and cognitive impairment. The effects of this variant appear to be associated with amyloid pathology and NFTS formation, particularly in Caucasian populations. Increased levels of total tau and p-Tau have been found in the cerebrospinal fluid (CSF) of R47H carriers compared to non-carriers [48].

There are mutations in other genes such as nonsense variants in the LOAD susceptibility genes PSEN1, PSEN2, MAPT, PRNP, CSF1R, and GRN that have been associated with various neurodegenerative diseases in Asian populations. These mutations associated with neurodegenerative diseases should also be investigated in addition to the risk loci in the clinical diagnosis of [49]. Variants of the gene CD2AP have been associated with EOAD and are also thought to play a critical role in the development of AD [50][51]. Recent studies on gene haplotypes have shown a strong influence of the regulated expression of the MAPT gene in the brain [52] and differences between haplotypes of TOMM40 encoding ApoE4 and ApoE3 on the association probability for AD [53].

1.2. Epistatic interactions between Risk Loci

As with many complex human traits, only a minimal portion of the heritability of AD [54] appears to be explained by genetic risk factors. One explanation for this "missing heritability" could be the occurrence of rare and undetected variants such as mutations in ADAM10 [55], which contribute greatly to the amplification of the disease phenotype. As in other complex diseases, susceptibility to AD is determined by additive effects and epistatic interactions of multiple genetic variants. It has also been proposed that TREM2 and ApoE jointly influence the pathogenesis of LOAD, although the mechanisms of interaction between the two risk loci are still unknown [29][56] BIN1 interacts with coding products of similar functional pathways affected in AD, such as PICALM, MAPT, ABCA7 or SORL1; CLU and ApoE have additive effects on lipid trafficking and affect Aβ deposition, and ABCA7 interacts with the major risk candidate genes BIN1, CLU and PICALM [6] (Figure 1). Genetic interactions between the rs670139 and rs11136000 polymorphisms of the MS4A4E and CLU genes predict up to 8% increased risk for the occurrence of AD [57]. Indeed, the epistatic dominance effect of MS4A4E-CLU, together with ApoE, APP and TREM2, is among the most important risk loci identified to date.

Updated multivariate GWAS (MGAS) opens new possibilities in the study of complex traits and can contribute to the discovery of new susceptibility genes and to a better understanding of the interactions between known risk genes. This type of analysis uses information on biological relationships in combination with bioinformatics tools such as a network analysis of protein interactions (PPI) [58][59] (see Figure 1 for an example of a PPI network analysis in AD). For example, the risk genes ApoE and APOC1 were found to be related to eight subcortical measures of the AD neuroimaging phenotype after applying a multivariate screen based on the extended Simes method (GATES) together with a PPI network analysis, in addition to novel genetic variants such as LAMA1, XYLB or NPEPL1 [60], while candidate genes such as ITGB5, RPH3A, GNAS, THY1, NEK6, JUN, GDI1, GNAI2, ERCC3 and CDC42EP4 were identified as potential biomarkers for early diagnosis in another study [61]. Gene-based multivariate tests, such as Versatile gene-based assay (VEGAS) or Multiphenotypic association analysis (MultiPhen), can also provide additional data on susceptible brain regions of AD -related functional areas [62].

Genes 12 01959 g001 550
Figure 1. Epistatic interactions between the major susceptibility loci of Alzheimer’s disease (AD). (A) Cluster analysis of the protein–protein interaction network of the major susceptibility loci of AD, generated with STRINGdb (http://string-db.org) (accessed on 15 November 2021). The ATP-binding cassette member 7 gene (ABCA7) (in bold) was used as the reference protein for the query to identify potential epistatic interactions between all the protein-coding genes. Nodes represent proteins, and edges represent functional and physical protein–protein associations with a significant contribution of the proteins to a common function, regardless of their physical binding to each other. The color of the lines indicates the type of interaction, and the line thickness indicates the strength of the data support. The PPI network analysis was performed with greater than 70% confidence (required minimum interaction score of 0.7). The analysis used a k-means clustering approach with an average local clustering coefficient of 0.729. Four clusters were identified for the network, highlighted in different colors, with dashed lines indicating edges between clusters. The network contains 29 nodes with an average node degree of 7.86; the confidence threshold was set to 0.7 (high). The enrichment of the connectivity in the network was significant (p < 0.001), suggesting that the proteins as a group are at least partially biologically connected and have significantly more interactions with each other than expected (114 edges compared to 31 expected edges) from a random group of proteins of the same size and degree distribution from the genome. (B) Heatmap of predicted functional associations between search protein ABCA7 and the other risk loci for AD. Scores refer to the strength of evidence found in a series of experiments for correlated expression between two coding protein genes based on RNA expression patterns and protein coregulation data from ProteomeHD (https://www.proteomehd.net/proteomehd) (accessed on 15 November 2021). Legend: The color intensity indicates confidence in the predicted functional association between ABCA7 and a given protein (adapted from STRINGdb). Created with BioRender.com.

1.3. Key genes as targets for AD therepies

A number of mutant variants in genes that affect the processing of APP and the formation of Aβ-peptides are considered potential targets for the development of AD therapies. One target that has been the focus of recent studies is BACE2, which plays an important role in APP-Aβ processing and cleavage. Both mutant variants at this locus and CLU expression affecting the juxta-membrane helix of APP can lead to early cleavage of this precursor protein by suppressing β-secretase activity [63]. Therapies against these targets would include prevention of CLU expression. Tauopathy associated with MAPT can be treated with drugs that stimulate microtubule stabilization by mutations at this locus or excessive phosphorylation in the brain [64]. Of note, processing and cleavage of APP into extracellular vesicles in the brain occurs with the progression of AD and contributes to both APP -dependent neurotoxicity and disease prognosis. At disease onset, vesicles are released outside brain cells along with APP and derived deleterious metabolites such as AICD and Aβ, preventing the accumulation of neurotoxic peptides in the brain [65]. This mechanism represents a potential target against AD pathogenesis, with extracellular vesicles acting protectively on brain cells.

In the sporadic form of AD, the β-secretase-derived C99 fragment is involved in the Aβ-independent aggregation of APP-CTFs in mitochondria, resulting in morphological and functional changes that can trigger pathogenic pathways in the brain. These mitochondrial abnormalities would be a target to counteract the early accumulation of APP-CTFs and slow disease progression [66]. Therapeutic treatments based on the localization of tau and Aβ proteins and APP processing must consider factors that alter their functionality, as well as changes within the cell or their expression in specific cells or body parts. TREM2 is also becoming an important target in the development of AD therapies that focus on stimulating TREM2 signaling at an early stage, before tau neuropathogenesis or deposits of β-amyloid form in the brain. However, the role of TREM2 needs to be further explored as its effect seems to change with the disease model used and the stage of tauopathy [67].

2. The Epigenome of Alzheimer's Disease as a Target for Therapeutic Treatment

Epigenetic changes can be understood as any mechanism by which the environment can alter the phenotype without altering the genotype, and they are a crucial factor that can explain the non-genetic component associated with the lack of heritability of complex traits. Epigenetic mechanisms may act as mediators of environmental factors and genetic risk components throughout an individual's life [68][69] and may play a particularly important role in the etiology of sporadic AD. Age and other factors indicative of healthy or unhealthy lifestyle habits, such as diet, smoking, and educational attainment, have been associated with the occurrence of AD [70][71][72]. Epigenetic mechanisms most commonly associated with AD include DNA methylation, post-translational modifications of histones (PTM-Hs), and gene silencing of non-coding RNAs (ncRNAs), which usually do not occur in isolation but in a complex interplay in which environmental factors may also play a role that may ultimately influence important cognitive processes [73][74]. Despite the association between many of these factors and the occurrence of epigenetic changes during the development of AD, the process that drives the interaction between each factor and leads to different rates of disease progression is not well understood. See Table 1 for recent studies of the epigenome in AD.

Table 1. Key findings from studies on the epigenetic mechanisms of Alzheimer's disease found in the NCBI PubMed database.

Epigenomic signal

AD model

Main finding

Ref

Histone acetylation

Post-mortem human prefrontal cortices

Tau pathology, but not amyloid-β pathology, affects histone acetylation.

In neurons, altered transcription occurs due to tau-induced chromatin remodeling. Tau-related chromatin changes exhibit spatial patterns.

[75]

DNA methylation

Post-mortem human brain tissue (AD Braak stage progression).

Identification of genes with cell type-specific methylation signatures and documentation of age-related differential methylation dynamics in neurons (CLU, NCOR2 and SYNJ2) or glia (CXXC5, RAI1 and INPP5A).

Several DNA methylation signals of neuronal (HOXA3) or glial origin (ANK1) associated with AD were validated.

[76]

DNA methylation

 

AβPPswe/PS-1 mice

Dlg4/PSD95, a protein involved in neuronal plasticity and memory, exhibits increased expression during development regulated by histone marks that have a major impact on several processes of hippocampal plasticity and neurons.

[77]

DNA methylation

 

Postmortem human brain tissue (middle temporal gyrus) and blood samples in AD patients.

Gene regulatory network (GRN) analysis predicts altered expression of IL6 and SIAH1 genes in brain tissues influenced by methylation and hydroxymethylation.

In blood, WNT3A is the leading gene in the reference network.

In both tissues, a common DMR is identified near the transcriptional starting point of the gene OXT, which encodes the neuropeptide oxytocin involved in neuromodulation of social behavior.

[78]

Histone acetylation and  methylation 

Human cortical brain tissue (microglia,  neuronal and  oligodendrocyte)

A subset of the variants identified by GWAS could act on super-enhancers that would affect gene expression. Knockdown of a targeted microglial enhancer carrying AD risk variants suppresses BIN1 gene expression in microglia but not in neurons or astrocytes.

[79]

DNA methylation

DNA methylation of induced pluripotent stem cells (iPSCs) from AD patients and healthy controls. One normal cell line, one LOAD line (APOE4) and at least two EOAD cell lines (PSEN1, PSEN2) are included.

Different DMRs of 5mC, 5hmC, and 5fC/caC are identified during differentiation of iPSCs into neurons in both normal cells and PSEN2.

[80]

DNA methylation

Whole blood DNA from trisomy 21 (T21) patients, non-dementia patients and AD dementia patients.

In both T21 and AD patients, at least 6 hypermethylated sites occur compared to healthy individuals. One of them is located in the ADAM10 promoter region.

[81]

DNA methylation

Blood DNA from an international population of eleven cohorts totaling 3337 individuals.

It has been observed that genetic factors contribute to differential DNA methylation in the hippocampus. Methylation at these sites alters the expression of genes required for hippocampal function and metabolic regulation.

[82]

mtDNA methylation

 

Postmortem PFC samples and Human cell lines HEK293T and A549.

mtDNA methylation is negatively correlated with mitochondrial gene expression and is modulated by methyltransferase (DNMT3A), which is required for the maintenance of methylation in neurons.

[83]

DNA methylation

 

E4 and E3 mice with high fat diet (HFD)

E4 HFD mice exhibit a unique DNA methylation profile in the hippocampus. They find that HFD-induced deficits in learning and spatial memory, but not object recognition, are more pronounced in E4 mice.

[84]

DNA methylation

DNA methylation in the entorhinal cortex of the brain (EC) Samples from the MRC London Neurodegenerative Diseases Brain Bank.

The ANK1 gene exhibits differential DNA methylation at AD.

Abnormal DNA methylation changes in WNT5B are associated with AD neuropathology.

[85]

DNA methylation

Peripheral blood samples from patients with MCI and normal control subjects.

Discovery of DMRs: two within SEPT8 and TMEM232 on chromosome 5, one within SLC17A8 on chromosome 12, and another within ALOX12 on chromosome 17.

Functional methylation analysis identifies four groups of genes (modules) that are significantly hypomethylated in affected individuals compared to controls: RIN3, CTSG, SPEG and UBE2L3.

[86]

Histone acetylation and methylation

Female triple transgenic (3xTg-AD) mice

The DNA methylation level at the promoter of the Txnip gene in the brain is significantly lower in 3xTg- AD compared to wild type.

The level of DNA methylation at the CTCF region of the Txnip gene promoter is significantly lower in the cerebellum and significantly higher in the spleen of 3xTg- AD mice compared to wild-type controls.

[87]

DNA methylation

Postmortem brains of age-matched normal controls and AD patients.

Methylation levels in the promoter regions of the BRCA1 and AURKC genes are upregulated in AD brains.

Dysfunction of BRCA1 results in impaired DNA integrity.

[88]

DNA methylation

Four brain regions: Hippocampus, cerebellum, EC and dorsolateral PFC of donor controls and patients with late stages AD.

Identify 858 robust DMRs in up to 772 putative genes.

Identify CpGs near ANK1 and MYO1C genes with AD.

The region-dependent and most significant effect is observed for a DNA methylation site near ANK1, which is more methylated in subjects with AD in the dorsolateral PFC, EC and hippocampus, but less methylated in the cerebellum.

[89]

DNA methylation

APP/PS1 mouse

PM20D1 is involved in lipid metabolism and is an important methylation and expression locus located within a AD -risk associated haplotype.

[90]

Histone acetylation

THY‐Tau22 mouse

The histone acetyltransferase (HAT)-activating molecule CBP/p300 (CSP -TTK21) is capable of acetylating nuclear chromatin in mouse brain.

It shows a specific decrease in acetylation levels in the hippocampus of THY -Tau22 mice, and CSP -TTK21 significantly restores this signature by enhancing long-term spatial memory storage.

[91]

DNA methylation

Postmortem brain tissue from patients with AD; dementia with Lewy bodies (DLB); Huntington's disease (HD); Parkinson's disease vascular dementia and non-demented control subjects.

Significantly increased levels in AD cases compared to controls AT eight ANK1 CpG sites in the ERC and seven ANK1 CpG sites in theSTG. Changes in ANK1 DNA methylation in the cerebellum are reported for the first time.

DNA hypermethylation of ANK1 in the ERC is observed only in DLB cases with coexisting AD pathology.

[92]

DNA methylation

Postmortem hippocampal samples from AD patients and control subjects.

DNA methylation levels correlate significantly with exposure to p-Tau deposition. Genomic loci that strongly overlap in regulatory regions are significantly hypermethylated in AD compared to healthy patients. DMGs are associated with neuronal development and neurogenesis.

[93]

Histone acetylation and methylation

APP/PS1 mice

Overall, no changes in histone marks over time in APP/PS1 and wild-type mice.

Age-related changes in histone marks are observed in wild-type mice.

[94]

DNA methylation

Post-mortem PFC of normal subjects and AD patients (LOAD).

They found 504 differentially methylated positions (DMPs), of which 487 positions had increased methylation levels and 17 positions had reduced methylation levels compared to controls AD.

The DMPs are mainly located in the HOXA3, GSTP1, CXXC1-3 and BIN1 genes.

[95]

DNA methylation

Post-mortem brains of AD patients and healthy controls.

DNA methylation (H3K9me3) is upregulated in the temporal cortex of patients with sporadic AD.

[96]

DNA methylation

Peripheral blood monocytes from healthy controls and AD patients. Cortex samples from 4 healthy subjects and 4 AD patients.

Hypomethylation of the TNF-α promoter region in the cerebral cortex of AD patients, while similar levels of methylation are found in control groups and in blood samples from AD patients.

[97]

DNA methylation

Brain tissue from 147 individuals drawn from the Mount Sinai Alzheimer's and Schizophrenia Brain Bank.

DNA hypermethylation in a 48 kb region of the HOXA gene is associated with neuropathology of AD in human cerebral cortex and cortical neuropathology of HOXA3.

[92]

DNA methylation

DNA methylation DNA from postmortem prefrontal cortical corx tissue from patients with AD and controls.

325 genes with differentially hydroxymethylated loci were identified.

Gene enrichment analysis in ontology reveals biological processes related to the development of neuronal projections and neurogenesis.

[98]

DNA methylation

Postmortem samples from STG patients with late onset AD and control subjects.

A total of 17,895 CpG sites were preliminarily identified as differentially methylated between AD and control subjects, including 11,822 and 6,073 hyper- and hypomethylated CpGs, respectively. Hypermethylation was mainly detected in genes regulating cell cycle progression.

[99]

DNA methylation

Peripheral blood (PB) samples from cognitively normal (CN), MCI, and AD patients.

DMPs with the strongest association with MCI compared to CN are annotated with CLIP4 and those most strongly associated with AD compared to CN are annotated with FAM8A1.

[100]

DNA methylation

LOAD patients and age- and sex-matched controls.

Significantly higher levels of D-loop methylation are observed in heterozygous MTRR 66AG carriers compared to wild-type MTRR 66AA individuals. Stratification of the population into AD and control subgroups shows that even in the AD subgroup, carriers of the DNMT3A AA genotype have significantly higher levels of D-loop methylation than GG or GA carriers. They suggest that MTRR and DNMT3A polymorphisms affect mitochondrial DNA methylation.

[101]

Blood DNA methylation

Blood samples from individuals before dementia diagnosis and cognitively healthy controls.

The biggest difference in methylation is the lower methylation in diagnosed dementia compared to controls.

DMRs are found together when blood samples are analysed before and after diagnosis. Genes affected by these DMRs include GULP1, SORCS3, PIEZO2, DNAH14, RIBC2, FOXG1, HOXC5, EPHA6, HOXA7, SYN3, IRX4, NOS1, LOC101929268, MARCH3, and ADAM12.

[102]

 

GWAS studies have primarily focused on specific phenotypes that include age of onset, differences in ethnicity, and psychotic traits in settings where the epigenome may influence the etiology of AD. However, effective treatment includes not only the prescription, dosage, and proper management of medications, but also the implementation of and adherence to a set of daily routines consistent with a healthy lifestyle that promotes socialization, nutrition, exercise, and mental agility [103]. Research at AD has recently aimed to highlight specific targets for disease diagnosis and treatment, including several microRNA molecules that show particular promise for the development of new therapies for neurodegenerative diseases [104][105].

2 1. Epigenomic Biomarkers

Some types of short ncRNAs, miRNAs, can interfere with the processing of Aβ and non-toxic APP via the alternative non-amyloidogenic pathway mediated by ADAM10 to form soluble APPα [106]. ADAM10 is regulated by miR-221 in AD neuroblastoma cells, and inhibition of expression of this molecule results in increased levels of the gene [107]. Another notable epigenetic marker is the mechanism of the small nuclear U1 ribonucleoprotein complex (U1-snRNP), which leads to alterations in the neuronal cell cycle via a defective RNA splicing process and ultimately affects the metabolic and biochemical processes responsible for neuroinflammation, cell decay and death [108][109]. Putative epigenomic biomarkers such as miR-129, which is thought to be ubiquitously upregulated in AD brains, may furthermore be useful for drug treatment against target genes [110].

2.2. Aβ Immunotherapy

Treatments based on Aβ immunotherapy appear to be effective in clearing amyloid plaques in the human brain, but not in slowing disease progression or reversing cognitive impairment. The use of immune checkpoint blockers (ICBs) is gaining interest as a more effective epigenetic target against the neuroinflammatory processes that characterize AD amyloid pathology [108]. Some ICBs based on antibodies against the protein ligand 1 complex of programmed cell death (PD-L1) are being investigated as therapies for cancer. In AD animal models, the use of drugs targeting the PD-1/PD-L1 complex can trigger an immune response that prevents the accumulation of neurotoxic substances associated with APP [111]. Inhibition of this complex promotes ligand degradation in antigen-presenting cells by increasing immune tolerance and preventing T-cell degradation, which helps to reduce neuroinflammation and recover from impaired cognitive functions [112][113]. However, the results of various experiments with BCIs are conflicting regarding their therapeutic ability to treat AD [114][115], and further research in this area is needed.

2.3. HDAC Inhibitors

Targeted therapy with HDAC inhibitors (HDACis) aims to reduce the cognitive deficits associated with AD or other neuropathologies [116][117]. The US Food and Drug Administration (FDA) and the Chinese agency of the same name (CFDA) have approved some HDACi drugs such as vorinostat (SAHA), panobinostat (LBH589), belinostat (PXD101), romidepsin (FK-228), and chidamide (HBI-8000), and although most of them were primarily developed to treat hematologic malignancies, some are also being investigated for the treatment of CNS disorders [118]. Lacosamide [119], tubastatin A [120], quisinostat [121], trichostatin A [122], and M344 [123] are the other HDACi that have recently been reported as prominent targets for AD. Valproic acid [124], 4-phenylbutyrate [125], MPT0G211 [126] and nicotinamide [127] also showed similar therapeutic effects in AD animal models. By combining the crucial structural features of the antioxidant ebselen and HDACi pharmacophores (vorinostat, tubastatin A, panobinostat and quisinostat), a class of novel synthetic hybrid compounds was created for AD therapy, and the compound identified as 7f was a potent HDACi [128]. The efficacy of the compounds CM-414 and CM-695 as a novel multitarget therapy focusing on inhibition of HDACs and phosphodiesterase 5 (PDE5) was demonstrated in Tg2576 mice, showing inhibition of intermediate class I HDACs and greater inhibition of HDAC6 and PDE5 [129][130]. Finally, Lim et al. pioneered the development of novel aspirin-inspired acetyl donor HDACi [118].

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