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Review

Missing lnc(RNAs) in Alzheimer’s Disease?

by
Rafaela Policarpo
1,2,3 and
Constantin d’Ydewalle
3,*
1
VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
2
Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, 3000 Leuven, Belgium
3
Neuroscience Discovery, Janssen Research & Development, Janssen Pharmaceutica N.V., 2340 Beerse, Belgium
*
Author to whom correspondence should be addressed.
Genes 2022, 13(1), 39; https://doi.org/10.3390/genes13010039
Submission received: 23 November 2021 / Revised: 17 December 2021 / Accepted: 21 December 2021 / Published: 23 December 2021
(This article belongs to the Special Issue Non-coding RNAs in Alzheimer's Disease)
Review Reports Versions Notes

Abstract

:
With the ongoing demographic shift towards increasingly elderly populations, it is estimated that approximately 150 million people will live with Alzheimer’s disease (AD) by 2050. By then, AD will be one of the most burdensome diseases of this and potentially next centuries. Although its exact etiology remains elusive, both environmental and genetic factors play crucial roles in the mechanisms underlying AD neuropathology. Genome-wide association studies (GWAS) identified genetic variants associated with AD susceptibility in more than 40 different genomic loci. Most of these disease-associated variants reside in non-coding regions of the genome. In recent years, it has become clear that functionally active transcripts arise from these non-coding loci. One type of non-coding transcript, referred to as long non-coding RNAs (lncRNAs), gained significant attention due to their multiple roles in neurodevelopment, brain homeostasis, aging, and their dysregulation or dysfunction in neurological diseases including in AD. Here, we will summarize the current knowledge regarding genetic variations, expression profiles, as well as potential functions, diagnostic or therapeutic roles of lncRNAs in AD. We postulate that lncRNAs may represent the missing link in AD pathology and that unraveling their role may open avenues to better AD treatments.

1. Alzheimer’s Disease and the Non-Coding Genome: What Is the Link?

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder characterized by a progressive cognitive and functional decline, and the leading cause of dementia worldwide [1,2]. Currently, over 55 million people are expected to live with dementia and despite increasing research efforts over the last years, no disease-modifying treatments are available [1,3]. The extracellular accumulation of amyloid-beta (Aβ) containing plaques and the formation of intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated and aggregated forms of Tau protein are well-known neuropathological hallmarks of AD [4,5]. Additional features observed during disease progression include neuroinflammatory responses elicited by microglia and astrocytes as well as neuronal and synaptic loss [4,5].
Most AD cases are classified as sporadic, late-onset AD (LOAD) when there is no (clear) genetic cause and when symptoms usually manifest after the age of 65 years [6]. Conversely, rare monogenic forms of early-onset AD are inherited from autosomal mutations in three genes (APP, PSEN1, and PSEN2) involved in the amyloid-beta (Aβ) precursor protein (APP) pathway culminating in the production and aggregation of toxic Aβ peptides. Nevertheless, twin-based genetic studies of dementia have estimated that LOAD depends on heritability in 60–80% of cases, suggesting that genetics play a crucial role in disease development [7]. The alleles of the APOE gene (encoding the apolipoprotein E, APOE), particularly the APOE epsilon 4 (ɛ4) allele, explain a substantial fraction of this heritability. Still, LOAD etiology is complex and oligogenic, and several genome-wide association studies (GWAS) identified LOAD-associated risk variants in over 40 loci [8,9,10,11,12,13,14,15,16,17,18]. However, many of these genetic variants or single nucleotide polymorphisms (SNPs) are often inherited together by linkage disequilibrium [19]. Additionally, several of these SNPs reside in non-coding regions of the genome which in general still lack functional validation [19,20,21]. Together, these features make it challenging to identify causal genes/variants, regulatory mechanisms, and molecular pathways underlying pathology.
Increasing evidence indicate that a large proportion of the human genome is actively transcribed into non-coding RNAs [22,23,24,25,26,27]. While these non-coding RNAs lack obvious protein-coding potential, they appear to play crucial cellular functions; many of them have been identified as novel regulators of gene expression at the epigenetic, transcriptional, post-transcriptional, and translational levels [28]. Amongst these, long non-coding RNAs (lncRNAs), a subclass of non-coding RNAs typically longer than 200 nucleotides, have been shown to participate in brain development and function, and the dysregulation of their expression implicated in many neurological disorders [29]. In particular, aberrant expression of many lncRNAs has been linked to AD [30,31]. To date, it remains unclear if and how lncRNAs influence AD development and progression.
LOAD-associated SNPs may localize to regulatory DNA elements including for example enhancers and transcription factor binding sites (TFBS). These SNPs may alter gene expression levels that in turn could prompt altered risk to LOAD. The functional effects of SNPs in enhancer DNA or TFBS are not clearly understood and may at least partially also rely on lncRNA-dependent mechanisms. In this review, we will focus on the recent advances and challenges in understanding how genetic variants in lncRNA loci modulate disease susceptibility; we will further discuss how the identification of the molecular pathways, cell type(s), and target genes affected by lncRNAs could pave the way to explore these molecules as potential biomarkers for an accelerated AD diagnosis and subsequent therapeutic intervention at early stages of the disease.

2. GWAS and the Identification of Genomic Risk Loci for Alzheimer’s Disease

Over the last years, several GWAS were carried out with the aim to identify the genetic determinants underlying the complex and heterogeneous etiology of LOAD [8,9,10,11,12,13,14,15,16,17,18]. One limitation from GWAS is that often they do not discover causal genes or polymorphisms; instead, they identify regions or haplotypes associated with specific (disease) traits [19]. Furthermore, many variants identified in non-coding regions may confer disease risk by interfering with regulatory elements of the genome, affecting chromatin interactions, and ultimately leading to changes in gene expression levels rather than affecting coding sequences. Hence, genetic mapping and functional characterization approaches remain crucial to identify disease-causative cell types, genes, and variants, and assess their impact on the progression of AD pathogenesis; approaches that are particularly challenging with non-coding variants [19,32].

Recent Advances in Alzheimer’s Disease GWAS

In 2007, the first two LOAD GWAS confirmed the APOE ε4 allele as a major risk factor for AD [33,34]. Since then, several other GWAS and meta-analyses contributed to the exponential increase in the identification of novel AD-associated loci. Just within the last three years, four new AD GWAS were published [9,11,14,18]. In 2018, Marioni et al. reported 27 susceptibility loci [11]. This was followed by a 2019 study with increased sample size, which identified 29 disease-associated loci [14]. In the same year, a GWAS based on clinically diagnosed AD resulted in the identification of 24 risk loci [9]. The latest study by Wightman and colleagues included over a million European individuals as controls; and more than 90,000 samples from either clinically diagnosed cases or people with family history of AD [18]. The authors identified 38 AD-risk loci, including five new loci—AGRN, TNIP1, AVCR2, NTN5, LILRB2—which had not been associated to any neurodegenerative disorder yet, and two—TMEM106B and GRN—which were linked to frontotemporal dementia before [35,36,37,38]. Most of the reported variants were also identified in previous GWAS studies [8,9,11,14].
Remarkably, this and other genetic studies point towards a central role for the immune system/inflammatory pathways in LOAD [39]. Functional genomic studies found an association of several coding variants in genes that play a role in microglia and peripheral myeloid cells with AD (e.g., TREM2 [40,41,42], MS4A4A/MS4A6E [43], ABI3 [42,44], PLCG2 [42,45], ABCA7 [46,47], CD33 [48,49], PILRA [50], and SPI1 [51,52]). However, many of these and other identified genes are involved in multiple AD-associated pathways, such as the amyloid cascade [53,54,55,56,57], Tau pathology [58], lipid metabolism and transport [59], neuronal development and synaptic function [60,61], autophagy [62] or endocytosis [56,63]. Thus, investigating the specific cell type(s), cellular states(s)/pathway(s), and spatiotemporal circumstances in which many of these variants affect disease susceptibility will be crucial to understand the biological mechanisms behind AD pathology.

3. A Genetic Link between lncRNAs and Alzheimer’s Disease?

So far, most genetic studies in LOAD focused on the identification of mutations, variants or polymorphisms located near or within protein-coding genes, and how these contribute to the underlying AD pathogenesis [64]. However, top GWAS variants mostly reside in non-coding regulatory regions of the genome outside of protein-coding gene sequences, including promoters, enhancers, and non-coding RNAs [19,20,65,66]. This is not surprising considering that large-scale annotation projects such as GENCODE found over 20,000 non-coding genes in the human genome, of which almost 18,000 consist of lncRNA genes (https://www.gencodegenes.org/human/stats.html accessed on 22 November 2021) [67].
Given the broad range of gene regulatory mechanisms exerted by lncRNAs and increasing evidence demonstrating their involvement in a wide spectrum of brain disorders, it is likely that SNPs located within lncRNA-containing loci might interfere with their biological function and, thus, contribute to AD pathology (Table 1). The AD-associated SNPs rs190982 and rs11771145 are located within two lncRNA genes associated with MEF2C (MEFC2-AS1) and EPHA1 (EPHA1-AS1) loci, respectively [8]. Recently, the SNP rs3935067 was also identified at the EPHA1-AS1 locus [18]. The rs2632516 variant was also identified in several AD GWAS and annotated to both a microRNA (MIR142) and a lncRNA (TSPOAP1-AS1) gene [9,14,17,18]. Another study by Chen and colleagues identified a SNP (rs7990916) located at a brain-specific lncRNA which shows a distinct distribution between cognitively normal elderly, mild cognitive impairment (MCI), and AD subjects [68]. While these SNPs have been associated to LOAD and may confer changes in the expression levels of the lncRNAs they reside in, it is unclear to date how they contribute to AD pathology or AD disease progression.
Based on large-scale GWAS data involved in three ethnicities, microarray data and RNA-seq data analysis, Han et al. identified five lncRNA genes with a potential role in AD [69]. The authors further predicted the function of these lncRNAs using multiple approaches, including genome mapping, expression quantitative trait loci, differential co-expression and gene set enrichment analyses [69]. Four out of the five identified lncRNAs can modify AD susceptibility by regulating genes and pathways involved in the immune system and Aβ-associated mechanisms [69]. In particular, two of these lncRNAs (NONHSAT018519.2 and NONHSAT016928.22), localize within the BDNF and ADAM12 loci, respectively, and are differentially expressed in a region-dependent manner between AD patients and healthy controls within the European ancestry group [69]. Decreased expression of both BNDF and ADAM12 in AD brain samples has been reported, and these genes code for proteins which have been implicated in AD pathology [70,71,72,73].
The occurrence and frequency of specific genetic variants might be reflected on the population ancestry making it essential to replicate GWAS data in different ethnicities [74,75]. A recent study investigated 18 SNPs associated with LOAD in European-based studies in 150 AD patients and 114 controls from the South Brazilian population [76]. Despite the limitation in sample size, four SNPs were found to overlap between both populations [76]. Additionally, eight out of 54 variants found in linkage disequilibrium with the associated SNPs were located withing lncRNA genes; and six were found potentially involved in AD [76]. Of particular interest was the SNP allele rs769449*A, which was found in linkage disequilibrium with the APOE ε4 isoform rs429358*C allele and associated with LOAD susceptibility in the study’s cohort. Another SNP, rs769449, localizes to two overlapping lncRNA genes (NONHSAT179793.1 and NONHSAT066732.2). This latter SNP likely affects the secondary structure of NONHSAT066732.2, leading to changes in the interaction of this lncRNA with two miRNAs potentially involved in inflammatory responses [76].
Future efforts to identify AD-associated variants located within non-coding regions, and particularly lncRNA genes, may be fundamentally necessary to understand how specific SNPs modulate the expression and function(s) of these regulatory non-coding transcripts and their associated protein-coding genes. This will open the possibility to functionally validate their biological functions, explore their expression dynamics and assess the impact of their dysregulation in AD-related genes and (ultimately) monitor clinical progression.
Table
Table 1. AD-risk variants identified within lncRNA genomic loci in AD GWAS.
Table 1. AD-risk variants identified within lncRNA genomic loci in AD GWAS.
LncRNA IDOther IDsVariant IDSNP Position
(GRCh38.p13)		Associated
Gene(s)		Reference(s)
MEF2C-AS1-rs190982chr5:88927603MEF2C[8]
EPHA1-AS1-rs11771145
rs3935067		chr7:143413669
chr7:143104331		EPHA1[8]
[18]
TSPOAP1-AS1BZRAP1-AS1rs2632516chr17:58331728MIR142, SUPT4H1[9,14,17,18,77]
NONHSAT160355.1-rs7232
rs12453		chr11:60173126
chr11:60178272		MS4A6A1, TCN1[69]
NONHSAT152299.1---C4A, C4B, TCF4, GRIP1[69]
NONHSAT016928.2Lnc-DHX32-1:1--ADAM12[69]
NONHSAT016928.2BDNF-AS:20--BDNF[69]
NONHSAT021264.2lnc-FAM180B-2:1rs71457224
rs10769282		chr11:47602821
chr11:60178272		MTCH21[76]
NONHSAT179794.1AC011481.3rs10414043
rs7256200		chr19:44912456
chr19:44912678		APOC11
APOE		[76]
NONHSAT066732.2Lnc-ZNF296-6:1rs429358chr19:44908684APOE1, AC011481.3[76]
NONHSAT179793.1-rs429358chr19:44908684APOE1, AC011481.3,
lnc-ZNF296-6:1		[76]
NONHSAT187478.1HSALNT0039381rs4663105chr2:127133851LOC1053736051[76]
NONHSAT182593.1-rs744373chr2:127137039-[76]
rs730482chr2:127136908LOC1053736051
TCONS_00021856LINC01080rs7990916chr13:80065389-[68]
1 Gene identified based on SNP information from: https://www.ncbi.nlm.nih.gov/snp/ accessed on 22 November 2021; - unknown/information not available.

4. Long Non-Coding RNAs: A Diagnostic Tool for Alzheimer’s Disease?

Classical AD diagnostics rely on the clinical manifestations of the disease supported by brain imaging approaches and blood/cerebrospinal fluid (CSF) biomarker strategies [78] These include the measurement of Aβ1-42/Aβ1-40 ratio, Tau and phosphorylated Tau peptides in the CSF; amyloid and Tau position emission tomography (PET) as direct imaging biomarkers for Aβ and Tau pathology, respectively; and volumetric magnetic resonance imaging (MRI) of the brain as a surrogate of neurodegeneration [79].
It is, however, well accepted that the neuropathological mechanisms underlying this disease start decades before clinical symptoms are manifested [80]. Thus, there is still the need to establish a biological definition of AD based on biomarkers that reflect such biological alterations at early stages of the disease. LncRNAs may represent an additional and attractive novel class of biomarkers for several reasons. First, they exhibit highly regulated spatiotemporal expression patterns, particularly in the brain and spinal cord [24,29,81]. Second, many lncRNA are differentially expressed in the brain during progression of various neurodegenerative disorders [29,30]. Last but not least, several lncRNAs can be detected not only in tissues and cells, but also in different body fluids such as CSF and blood [82]. Therefore, exploring the expression dynamics of circulating lncRNAs, and evaluating their combination with the already established AD clinical biomarkers, could help to improve early AD diagnosis, to evaluate disease progression, and to monitor treatment efficacy.

4.1. Expression Profile of Specific lncRNAs in AD Brain

Several lncRNAs are aberrantly expressed in AD brains compared to healthy controls, suggesting a strong association between altered lncRNA expression and AD pathology (Table 2) [30,31]. Although a direct contribution to AD pathology of many of these lncRNAs remains unknown, multiple studies have shown their implication in AD-related pathways including Aβ and Tau production and/or clearance, autophagy, neuronal proliferation, and apoptosis [31].
Expression of MAPT-AS1, a lncRNA associated with the MAPT gene, was recently found to inversely correlate with Tau pathology, decreasing with higher Braak stages [83]. Additionally, it has been suggested that MAPT-AS1 plays a regulatory role on Tau translation by competing with the MAPT mRNA internal ribosome entry site for ribosomal binding [83].
Faghihi et al. characterized BACE1-AS as a natural antisense transcript (NAT) associated with the BACE1 gene [84], which encodes the β-secretase BACE1, an enzyme involved in the amyloidogenic pathway and synthesis of Aβ peptides. BACE1 is highly abundant in the brain, and both its expression levels and enzymatic activity are increased in AD brains. BACE1-AS plays a critical role in upregulating the levels of Aβ1-42 peptides by increasing BACE1 mRNA and protein levels in SH-SY5Y, HEK293T, and HEK-SW human cell lines and in mice [84]. Additionally, expression levels of this NAT are increased in the brains of AD patients and in amyloid precursor protein (APP) transgenic mice indicating that BACE1-AS levels can serve both as a candidate diagnostic marker and therapeutic target for AD [84].
Similarly, the lncRNAs BC200, 17A, NDM29, and 51A can increase Aβ production, and their expression levels were increased in brain tissue from patients with AD [85]. BC200 facilitates Aβ production by modulating BACE1 expression levels [86]. In SH-SY5Y cells overexpressing Aβ1-42, knockdown of BC200 led to a significant reduction in BACE1 levels, increased cell viability, and reduced cell apoptosis by directly targeting BACE1 [87]. Importantly, the increase in BC200 levels in the neocortex of AD patients is associated with the severity of the disease [86]. The lncRNA 17A enhances the secretion of Aβ in neuroblastoma cells in response to inflammatory stimuli by regulating the alternative splicing of the GABA B2 receptor and subsequently abolishing its intracellular signaling [88]. APP synthesis is boosted upon upregulation of the lncRNA NDM29 in vitro leading to increased secretion of Aβ [89]. Finally, the lncRNA 51A drives a splicing shift in the canonical variant A of SORL1 [90], a known risk gene for LOAD [91]. In AD brains, SORL1 expression is reduced [92] shifting APP processing towards the β-secretase pathway and promoting Aβ peptide formation [93,94]. Therefore, 51A could play a role in Aβ generation by inhibiting SOLR1 expression in patients with AD [90]. EBF3-AS is another lncRNA whose expression levels is increased in different brain regions of LOAD patients and in the hippocampus of APP/PS1 transgenic mice [95,96]. According to Gu et. al, EBF3-AS positively regulates the expression of EBF3, a DNA-binding transcription factor, and promotes neuronal apoptosis in a human neuroblastoma cell line [96]. Another study has revealed the presence of an antisense transcript, LRP1-AS, at the LRP1 locus. Importantly, LRP1 is a receptor that has been implicated in multiple AD-associated pathways [53], such as both clearance and production of Aβ peptides [97], internalization of APOE [98] and Tau uptake [99]. While the role of LRP1-AS in AD progression is not entirely clear, Yamanaka et al. have identified this lncRNA as a negative regulator of LRP1 gene expression [100]. In line with this, LRP1 mRNA expression is reduced in samples from the superior frontal gyrus of AD patients, while LRP1-AS levels are increased in this brain region during pathology [100].
Table
Table 2. Examples of deregulated lncRNAs in brain tissue samples from patients with AD.
Table 2. Examples of deregulated lncRNAs in brain tissue samples from patients with AD.
LncRNA IDTrendEvaluated Tissue/SamplesProposed FunctionReference(s)
MAPT-AS1↓ in ADHippocampus, Parietal cortex, Temporal cortex (http://aging.brain-map.org/ accessed 22 November 2021);
Bulk brain tissue (https://doi.org/10.7303/syn3388564 accessed on 22 November 2021)		Inhibits Tau translation by competing for ribosomal RNA pairing
with the MAPT mRNA internal ribosome entry site (IRES)		[83,101,102]
BACE1-AS↑ in ADParietal cortex;
Cerebellum;
Superior frontal gyrus;
Entorhinal cortex;
Hippocampus		Under stress conditions, upregulates BACE1 mRNA and subsequently BACE1 protein expression, leading to the accumulation of Aβ peptides[84]
BC200↑ in ADSuperior frontal gyrus;
Hippocampus		Might facilitate Aβ production by upregulating BACE1 expression levels[86]
NDM29↑ in ADFrontal and temporal cortexIncreases APP synthesis, leading to increased secretion of Aβ peptides[89]
51A↑ in ADFrontal and temporal cortexDrives a splicing shift of SORL1 from the synthesis of the variant A to an alternatively spliced protein form, which leads to an impaired processing of APP and increased Aβ formation[90]
EBF3-AS↑ in ADCerebellum;
Superior frontal gyrus;
Entorhinal cortex;
Hippocampus		Promotes neuron apoptosis in AD, and is involved in regulating the expression of the DNA-binding transcription factor EBF3[95,96]
LRP1-AS↑ in ADSuperior frontal gyrusNegatively regulates Lrp1 expression by binding to Hmgb2 protein and inhibit its activity to enhance Srebp1a-dependent transcription of Lrp1[100]

4.2. General Expression Profiles of lncRNAs in the AD Brain

Additional studies have focused on understanding the overall dynamics of lncRNA expression patterns during AD pathology. Zhou and colleagues used re-annotation of microarray datasets to specifically identify AD-associated lncRNAs from post mortem tissue samples [103]. The analysis indicated that dozens of lncRNAs are aberrantly expressed in AD patients compared to age-matched controls [103]. Interestingly, two significantly dysregulated lncRNAs identified in this study are involved in protein ubiquitination and lipid homeostasis, suggesting a role for altered lncRNAs in AD-relevant signaling pathways [103]. Moreover, lncRNA expression signatures could be used to discriminate between AD and control tissue samples with comparable sensitivity and specificity to those from protein-coding genes [103]. However, the number of lncRNAs necessary for optimal sample prediction was much lower than that of protein-coding genes, indicating that lncRNAs might be just as relevant as prognostic tools [103].
In another report, Zhou and colleagues performed a comparative analysis in four distinct brain regions––entorhinal cortex, hippocampus, post-central gyrus, and superior frontal gyrus. This study demonstrated that the expression profiles of many lncRNAs are altered both in a region- and age-specific manner in the AD brain [104]. In addition, the authors used machine learning tools to identify a panel of nine lncRNAs that can discriminate between AD and healthy control cases with a diagnostic sensitivity and specificity of 86.3% and 89.5%, respectively, in two independent cohorts [104]. Wu et al. further confirmed the region-specific changes in the expression patterns exhibited by lncRNAs by comparing gene expression data in six distinct brain regions from AD and control patients [105].
A transcriptomic analysis of human cortical samples including 12,892 known lncRNAs and 19,053 protein-coding genes found differentially expressed transcripts from both types of genes in AD cases compared to control individuals [106]. Co-expression network analysis revealed that three lncRNAs––RP3-522J7, MIR3180-2, and MIR3180-3—are frequently co-expressed with relevant AD risk protein-coding genes [106]. For instance, all three lncRNAs are co-expressed with S100B, a protein-coding gene which is linked to AD pathology [107].Additionally, in line with previous reports, many of these transcripts are specifically enriched in the brain compared to other body tissues and expressed in a region-dependent manner emphasizing their potential as diagnostic markers for AD [106]. Magistri et al. performed RNA sequencing analysis from LOAD hippocampus samples and identified 31 NATs and 89 long intergenic non-coding RNAs (lincRNAs) as differentially expressed in AD compared to controls [95]. Differential expression of four lncRNAs was further validated by RT-qPCR in different brain regions. Interestingly, the expression of one of the lncRNAs, named AD-linc1, is upregulated in LOAD samples, and its expression is induced in a human neuronal in vitro model upon exposure to Aβ42 [95]. Another transcriptomic profiling study using samples from LOAD cases further reports the aberrant expression of several lncRNAs in the hippocampus of advanced Braak stage patients [108].
Although aging is the main risk factor for developing AD, disease onset is also influenced by gender. For instance, women represent about two thirds of all people diagnosed with AD [109]. Thus, a recent study using microarray datasets and bioinformatics analysis specifically explored how lncRNA expression profiles associate with both age and gender in AD [110]. The authors observed changes in the expression patterns of 16 age-associated and 13-gender associated lncRNAs in the frontal cortex of AD patients compared to healthy controls [110]. Of these, three gender-associated lncRNAs––RNF144A-AS1, LY86-AS1, and LINC00639––negatively correlate with AD Braak stage; and two age-associated lncRNAs––LINC00672 and SNHG19––positively and negatively correlate with Braak stage, respectively [110]. While the underlying mechanisms of most of these lncRNAs are unknown, pathways involved in neurodegenerative disorders, lysosome, synaptic vesicle cycle, axon guidance, and endocytosis pathways are enriched within age- and gender-associated lncRNAs [110].
In summary, the identification of brain region-, age-, and gender-associated lncRNAs and their differential expression patterns in the human AD brain provide potential targets for further investigating their biological functions. Subsequently, it opens the exciting possibility of developing age- and gender-specific diagnosis, prevention, and precision therapeutic options for patients with AD.

4.3. Circulating LncRNAs Expression in AD

Most studies investigating the potential to use lncRNAs as diagnostic and prognostic markers focused on different types of cancer and cardiovascular diseases. However, multiple lncRNAs show significantly altered expression patterns in body fluids from AD patients (Table 3) [82]. Thus, exploring free circulating lncRNA transcripts in CSF, extracellular vesicles/exosomes, blood, or plasma can be an interesting non-invasive approach to detect biomarkers for AD and other neurodegenerative disorders.
Levels of the lncRNAs BACE1-AS are increased in plasma samples from 88 AD patients compared to 72 controls from a Han Chinese cohort [111]. Of note, Feng and colleagues also evaluated the expression levels of 17A, 51A, and BC200 in plasma samples and found no differences between AD and controls [111]. In addition, the authors found no correlation between the expression levels of these lncRNAs with either a clinical cognitive test called the mini-mental state examination (MMSE) or age, indicating that the diagnostic value of BACE1-AS was independent of these parameters [111]. Another study using a smaller cohort reported that BACE1-AS levels in plasma are able to discriminate between control, pre-AD, and full-AD individuals, indicating a predictive value for this lncRNA in AD [112]. Contrarily to the previous report, a positive correlation was found between BACE1-AS plasma levels and age, while low MMSE scores were associated with higher levels of BACE1-AS [112]. Similar data were recently reported by Wang et al. [113]. The authors evaluated the expression of several lncRNAs in plasma-derived exosomes together with image data from the entorhinal cortex and hippocampus of AD patients [113]. Plasma exosomal levels of BACE1-AS, but not 51A nor BC200, were increased in AD individuals [113]. These data contrast with a study showing upregulated levels of the lncRNA 51A in plasma from AD individuals [114]. Furthermore, integrating BACE1-AS plasma levels with MRI data from right entorhinal cortex volume and thickness increased specificity (96.15%) and sensitivity (90.91%) as a combinatorial AD biomarker compared to BACE1-AS levels alone [113]. However, no associations were found between BACE1-AS expression levels and age or MMSE scores in this study [113].
Both CSF and plasma levels of the lncRNA MALAT1 are downregulated in AD patients compared to control and Parkinson’s disease (PD) individuals [116]. In line with this, microRNA-125b, whose expression is regulated by MALAT1, is upregulated in AD samples [116]. MicroRNA-125b overexpression enhances Aβ production by increasing APP and BACE1 expression in mouse neuroblastoma Neuro2a APPSwe/Δ9 cells [121]. Another study described a role for this microRNA (miRNA) on Tau phosphorylation and neuronal apoptosis [122]. Interestingly, both MALAT1 and microRNA-125b levels in the CSF but not in plasma can predict the decline in MMSE score at years one, two and three in patients with AD [116]. Though most studies demonstrated a role for MALAT1 in various cancer types [123], MALAT1 overexpression was recently reported to prevent neuron apoptosis, promote neurite outgrowth, and reduce inflammation in two AD mouse models [115]. Thus, MALAT1 could not only represent a valuable biomarker but also a potential therapeutic target to help prevent neuronal loss in AD.
The lncRNAs RP11-462G22.1 and PCA3 were previously linked to PD [124], and their expression levels are upregulated in CSF-derived exosomes from both AD and PD patients [117]. While these lncRNAs cannot discriminate AD from PD patients, these data further emphasize a general dysregulation of lncRNAs in neurodegeneration.
Differential expression of lncRNAs between AD patients and healthy controls were also investigated in blood samples. For instance, a microarray analysis from peripheral blood mononuclear cells (PBMCs) found 14 upregulated and 20 downregulated lncRNAs in AD samples [125]. Another study compared the expression profile of lncRNAs in PBMCs from AD individuals with two other neurodegenerative disorders, PD and amyotrophic lateral sclerosis, and controls [118]. In AD patients, a total of 23 genes, including 19 protein-coding genes and three lncRNAs––CH507-513H4.4, CH507-513H4.6 and CH507-513H4.3––emerged as differentially expressed [118]. While the roles of these transcripts are currently not known, their levels were exclusively altered in AD-derived PBMCs, suggesting a disease-specific expression pattern [118].
The lncRNA activated by TGF-β, named lncRNA-ATB, is also overexpressed in the CSF and serum of patients with AD [119]. This lncRNA was previously associated to multiple pathologies, including multiple types of cancer [126,127,128], keloids [129], and osteoarthritis [130]. Wang and colleagues explored the role of lncRNA-ATB in a cellular model and found that suppressing the levels of this lncRNA might have a protective effect against Aβ-induced neurotoxicity via regulation of miR-200 expression [119]. The miR-200 family has also been implicated in the pathogenesis of AD and explored as a potential biomarker for the disease [131].
Both mRNA and lncRNA transcripts can contain different miRNAs binding sites indicating that these transcripts can regulate each other by competing with shared miRNA-binding sites; these transcripts are so-called competing endogenous RNAs (ceRNAs) [132]. Very recently, Huaying et al. explored a potential deregulation in ceRNA networks in AD by analyzing mRNA, miRNA, and lncRNA gene expression patterns; Nine lncRNAs are associated with AD, PD, and other neurodegenerative disorders [120]. Furthermore, five of the identified lncRNAs––SNHG14/UBE3A-ATS, PART1, NNT-AS1, AC093010.3 and ARMCX5-GPRASP2––were evaluated as a potential combinatorial AD biomarker in two distinct AD cohorts [120]. RT-qPCR data revealed a downregulation of the lncRNA PART1, and an upregulation of SNHG14/UBE3A-ATS in serum samples from AD patients compared to healthy controls [120]. While the role of these lncRNAs in AD pathology is unknown, SNHG14/UBE3A-ATS has been proposed as a therapeutic target for Angelman syndrome due to its role in silencing the expression of UBE3A [133].
Taken together, these studies indicate that exploring the biological functions of lncRNAs is crucial to understand their dynamic expression profiles in a pathophysiological context and evaluate their suitability as biomarkers for AD.

5. Conclusions and Future Perspectives

Recent advances in AD GWAS revealed several disease-associated variants mapping to non-coding regions of the genome, including within genomic loci harboring lncRNA genes. Despite the generally low abundance of lncRNAs, expression levels of several of them are dysregulated in the course of AD. However, many studies show contrasting data, and often there is no clear overlap between the differentially expressed lncRNAs between controls and AD. These discrepancies likely reflect both methodological and biological variations. In addition, there are still several limitations to the use of lncRNAs in clinical practice for AD and other brain disorders. For instance, detection of some lncRNAs in circulation can be difficult due to their lower levels of expression; in addition, there is still a lack of consensus on which genes are stable and appropriate to be used as reference genes for circulating lncRNAs; finally, many circulating lncRNAs can be found dysregulated in multiple disorders, therefore, lacking diagnostic specificity. We strongly recommend establishing a cross-sectoral working group where basic and translational researchers jointly develop standard operating procedures for the identification and quantification of lncRNAs as potential biomarker for neurological (and other) diseases. We also advocate for the thorough functional characterization of lncRNAs already linked to AD and other neurodegenerative diseases. Comprehensive analyses of lncRNAs dysregulated in AD will undoubtedly provide new insights into disease pathogenesis. We are convinced that new detailed insights in AD-associated lncRNAs may uncover previously unappreciated links between genes and pathways involved in the disease. Ultimately, these lncRNAs may pave the way for the development of novel and innovative biomarker strategies or therapeutic avenues for AD and other neurodegenerative diseases.

Author Contributions

R.P. and C.d. were responsible for the conception of the article. R.P. created the tables and wrote the first draft of the review. R.P. and C.d. did the scientific literature review, and critically revised the entire review. All authors approved the final version of the manuscript.

Funding

R.P. holds a doctoral student fellowship from VLAIO, the Flemish Agency for Innovation and Entrepeneurship (Vlaams Agentschap Innoveren en Ondernemen). This work was supported by VLAIO (R&D grants HBC.2018.2290 and HBC.2020.3236) and VIB-KU Leuven.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

C.d. was employed by company Janssen Pharmaceutica N.V. R.P. declares no conflict of interest.

References

  1. Gauthier, S.; Rosa-Neto, P.; Morais, J.A.; Webster, C. World Alzheimer Report 2021: Journey through the Diagnosis of Dementia; Alzheimer Disease International: London, UK, 2021. [Google Scholar]
  2. Scheltens, P.; De Strooper, B.; Kivipelto, M.; Holstege, H.; Chételat, G.; Teunissen, C.E.; Cummings, J.; van der Flier, W.M. Alzheimer’s disease. Lancet 2021, 397, 1577–1590. [Google Scholar] [CrossRef]
  3. Cummings, J.; Lee, G.; Zhong, K.; Fonseca, J.; Taghva, K. Alzheimer’s disease drug development pipeline: 2021. Alzheimer’s Dement. Transl. Res. Clin. Interv. 2021, 7, 1–24. [Google Scholar] [CrossRef]
  4. Deture, M.A.; Dickson, D.W. The neuropathological diagnosis of Alzheimer’s disease. Mol. Neurodegener. 2019, 14, 1–18. [Google Scholar] [CrossRef] [Green Version]
  5. Reddy, P.H. A Critical Assessment of Research on Neurotransmitters in Alzheimer’s Disease. J. Alzheimer’s Dis. 2017, 57, 969–974. [Google Scholar] [CrossRef] [Green Version]
  6. Zhang, Q.; Sidorenko, J.; Couvy-Duchesne, B.; Marioni, R.E.; Wright, M.J.; Goate, A.M.; Marcora, E.; Huang, K.L.; Porter, T.; Laws, S.M.; et al. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nat. Commun. 2020, 11, 1–11. [Google Scholar] [CrossRef]
  7. Gatz, M.; Reynolds, C.A.; Fratiglioni, L.; Johansson, B.; Mortimer, J.A.; Berg, S.; Fiske, A.; Pedersen, N.L. Role of Genes and Environments for Explaining Alzheimer Disease. Arch. Gen. Psychiatry 2006, 63, 168–174. [Google Scholar] [CrossRef] [PubMed]
  8. Lambert, J.C.; Ibrahim-Verbaas, C.A.; Harold, D.; Naj, A.C.; Sims, R.; Bellenguez, C.; DeStafano, A.L.; Bis, J.C.; Beecham, G.W.; Grenier-Boley, B.; et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 2013, 45, 1452–1458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Kunkle, B.W.; Grenier-Boley, B.; Sims, R.; Bis, J.C.; Damotte, V.; Naj, A.C.; Boland, A.; Vronskaya, M.; van der Lee, S.J.; Amlie-Wolf, A.; et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 2019, 51, 414–430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Marioni, R.E.; Harris, S.E.; Zhang, Q.; McRae, A.F.; Hagenaars, S.P.; Hill, W.D.; Davies, G.; Ritchie, C.W.; Gale, C.R.; Starr, J.M.; et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 2018, 8, 99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Harold, D.; Abraham, R.; Hollingworth, P.; Sims, R.; Gerrish, A.; Hamshere, M.L.; Pahwa, J.S.; Moskvina, V.; Dowzell, K.; Williams, A.; et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 2009, 41, 1156. [Google Scholar] [CrossRef]
  12. Lambert, J.-C.; Heath, S.; Even, G.; Campion, D.; Sleegers, K.; Hiltunen, M.; Combarros, O.; Zelenika, D.; Bullido, M.J.; Tavernier, B.; et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet. 2009, 41, 1094–1099. [Google Scholar] [CrossRef]
  13. Jansen, I.E.; Savage, J.E.; Watanabe, K.; Bryois, J.; Williams, D.M.; Steinberg, S.; Sealock, J.; Karlsson, I.K.; Hägg, S.; Athanasiu, L.; et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 2019, 51, 404–413. [Google Scholar] [CrossRef]
  14. Naj, A.C.; Jun, G.; Beecham, G.W.; Wang, L.-S.; Vardarajan, B.N.; Buros, J.; Gallins, P.J.; Buxbaum, J.D.; Jarvik, G.P.; Crane, P.K.; et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat. Genet. 2011, 43, 436–441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Hollingworth, P.; Harold, D.; Sims, R.; Gerrish, A.; Lambert, J.-C.; Carrasquillo, M.M.; Abraham, R.; Hamshere, M.L.; Pahwa, J.S.; Moskvina, V.; et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat. Genet. 2011, 43, 429–435. [Google Scholar] [CrossRef] [Green Version]
  16. Schwartzentruber, J.; Cooper, S.; Liu, J.Z.; Barrio-Hernandez, I.; Bello, E.; Kumasaka, N.; Young, A.M.H.; Franklin, R.J.M.; Johnson, T.; Estrada, K.; et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat. Genet. 2021, 53, 392–402. [Google Scholar] [CrossRef] [PubMed]
  17. Wightman, D.P.; Jansen, I.E.; Savage, J.E.; Shadrin, A.A.; Bahrami, S.; Holland, D.; Rongve, A.; Børte, S.; Winsvold, B.S.; Drange, O.K.; et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 2021, 53, 1276–1282. [Google Scholar] [CrossRef]
  18. De Rojas, I.; Moreno-Grau, S.; Tesi, N.; Grenier-Boley, B.; Andrade, V.; Jansen, I.E.; Pedersen, N.L.; Stringa, N.; Zettergren, A.; Hernández, I.; et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat. Commun. 2021, 12, 3417. [Google Scholar] [CrossRef] [PubMed]
  19. Novikova, G.; Andrews, S.J.; Renton, A.E.; Marcora, E. Beyond association: Successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer’s disease risk. Mol. Neurodegener. 2021, 16, 1–13. [Google Scholar] [CrossRef]
  20. Maurano, M.T.; Humbert, R.; Rynes, E.; Thurman, R.E.; Haugen, E.; Wang, H.; Reynolds, A.P.; Sandstrom, R.; Qu, H.; Brody, J.; et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 2012, 337, 1190–1195. [Google Scholar] [CrossRef] [Green Version]
  21. Tak, Y.G.; Farnham, P.J. Making sense of GWAS: Using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 2015, 8, 1–18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Dunham, I.; Kundaje, A.; Aldred, S.F.; Collins, P.J.; Davis, C.A.; Doyle, F.; Epstein, C.B.; Frietze, S.; Harrow, J.; Kaul, R.; et al. An integrated encyclopedia of DNA elements in the human genome. Nature 2012, 489, 57–74. [Google Scholar]
  23. Hon, C.; Ramilowski, J.A.; Harshbarger, J.; Bertin, N.; Rackham, O.J.L.; Gough, J.; Denisenko, E.; Schmeier, S.; Poulsen, T.M.; Severin, J.; et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nat. Publ. Gr. 2017, 543, 199–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Djebali, S.; Davis, C.A.; Merkel, A.; Dobin, A.; Lassmann, T.; Mortazavi, A.; Tanzer, A.; Lagarde, J.; Lin, W.; Schlesinger, F.; et al. Landscape of transcription in human cells. Nature 2012, 489, 101–108. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Carninci, P.; Kasukawa, T.; Katayama, S.; Gough, J.; Frith, M.C.; Maeda, N.; Oyama, R.; Ravasi, T.; Lenhard, B.; Wells, C.; et al. The Transcriptional Landscape of the Mammalian Genome. Science 2005, 309, 1559–1563. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Zhao, L.; Wang, J.; Li, Y.; Song, T.; Wu, Y.; Fang, S.; Bu, D.; Li, H.; Sun, L.; Pei, D.; et al. NONCODEV6: An updated database dedicated to long non-coding RNA annotation in both animals and plants. Nucleic Acids Res. 2021, 49, D165–D171. [Google Scholar] [CrossRef] [PubMed]
  27. Lorenzi, L.; Chiu, H.; Cobos, F.A.; Gross, S.; Volders, P.; Cannoodt, R.; Nuytens, J.; Vanderheyden, K.; Anckaert, J.; Lefever, S.; et al. The RNA Atlas expands the catalog of human non-coding RNAs. Nat. Biotechnol. 2021, 68, 1453–1465. [Google Scholar] [CrossRef]
  28. Statello, L.; Guo, C.J.; Chen, L.L.; Huarte, M. Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol. 2021, 22, 96–118. [Google Scholar] [CrossRef] [PubMed]
  29. Policarpo, R.; Sierksma, A.; De Strooper, B.; D’Ydewalle, C. From junk to function: LncRNAs in CNS health and disease. Front. Mol. Neurosci. 2021, 14, 151. [Google Scholar] [CrossRef] [PubMed]
  30. Cortini, F.; Roma, F.; Villa, C. Emerging roles of long non-coding RNAs in the pathogenesis of Alzheimer’s disease. Ageing Res. Rev. 2019, 50, 19–26. [Google Scholar] [CrossRef]
  31. Zhang, Y.; Zhao, Y.; Ao, X.; Yu, W.; Zhang, L.; Wang, Y.; Chang, W. The Role of Non-coding RNAs in Alzheimer’s Disease: From Regulated Mechanism to Therapeutic Targets and Diagnostic Biomarkers. Front. Aging Neurosci. 2021, 13, 1–22. [Google Scholar] [CrossRef]
  32. Frydas, A.; Wauters, E.; van der Zee, J.; Van Broeckhoven, C. Uncovering the impact of noncoding variants in neurodegenerative brain diseases. Trends Genet. 2021, in press. [Google Scholar] [CrossRef]
  33. Coon, K.D.; Myers, A.J.; Craig, D.W.; Webster, J.A.; Pearson, J.V.; Lince, D.H.; Zismann, V.L.; Beach, T.G.; Leung, D.; Bryden, L.; et al. A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J. Clin. Psychiatry 2007, 68, 613–618. [Google Scholar] [CrossRef] [PubMed]
  34. Grupe, A.; Abraham, R.; Li, Y.; Rowland, C.; Hollingworth, P.; Morgan, A.; Jehu, L.; Segurado, R.; Stone, D.; Schadt, E.; et al. Evidence for novel susceptibility genes for late-onset Alzheimer’s disease from a genome-wide association study of putative functional variants. Hum. Mol. Genet. 2007, 16, 865–873. [Google Scholar] [CrossRef]
  35. Busch, J.I.; Martinez-Lage, M.; Ashbridge, E.; Grossman, M.; Van Deerlin, V.M.; Hu, F.; Lee, V.M.Y.; Trojanowski, J.Q.; Chen-Plotkin, A.S. Expression of TMEM106B, the frontotemporal lobar degeneration-associated protein, in normal and diseased human brain. Acta Neuropathol. Commun. 2013, 1, 36. [Google Scholar] [CrossRef] [Green Version]
  36. Gass, J.; Cannon, A.; Mackenzie, I.R.; Boeve, B.; Baker, M.; Adamson, J.; Crook, R.; Melquist, S.; Kuntz, K.; Petersen, R.; et al. Mutations in progranulin are a major cause of ubiquitin-positive frontotemporal lobar degeneration. Hum. Mol. Genet. 2006, 15, 2988–3001. [Google Scholar] [CrossRef] [Green Version]
  37. Aswathy, P.M.; Jairani, P.S.; Raghavan, S.K.; Verghese, J.; Gopala, S.; Srinivas, P.; Mathuranath, P.S. Progranulin mutation analysis: Identification of one novel mutation in exon 12 associated with frontotemporal dementia. Neurobiol. Aging 2016, 39, 218.e1–218.e3. [Google Scholar] [CrossRef] [Green Version]
  38. Li, Z.; Farias, F.H.G.; Dube, U.; Del-Aguila, J.L.; Mihindukulasuriya, K.A.; Fernandez, M.V.; Ibanez, L.; Budde, J.P.; Wang, F.; Lake, A.M.; et al. The TMEM106B FTLD-protective variant, rs1990621, is also associated with increased neuronal proportion. Acta Neuropathol. 2020, 139, 45–61. [Google Scholar] [CrossRef]
  39. Novikova, G.; Kapoor, M.; Tcw, J.; Abud, E.M.; Efthymiou, A.G.; Chen, S.X.; Cheng, H.; Fullard, J.F.; Bendl, J.; Liu, Y.; et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 2021, 12, 1610. [Google Scholar] [CrossRef] [PubMed]
  40. Claes, C.; Van Den Daele, J.; Boon, R.; Schouteden, S.; Colombo, A.; Monasor, L.S.; Fiers, M.; Ordovás, L.; Nami, F.; Bohrmann, B.; et al. Human stem cell-derived monocytes and microglia-like cells reveal impaired amyloid plaque clearance upon heterozygous or homozygous loss of TREM2. Alzheimers. Dement. 2019, 15, 453–464. [Google Scholar] [CrossRef]
  41. Jonsson, T.; Stefansson, H.; Steinberg, S.; Jonsdottir, I.; Jonsson, P.V.; Snaedal, J.; Bjornsson, S.; Huttenlocher, J.; Levey, A.I.; Lah, J.J.; et al. Variant of TREM2 Associated with the Risk of Alzheimer’s Disease. N. Engl. J. Med. 2012, 368, 107–116. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Sims, R.; van der Lee, S.J.; Naj, A.C.; Bellenguez, C.; Badarinarayan, N.; Jakobsdottir, J.; Kunkle, B.W.; Boland, A.; Raybould, R.; Bis, J.C.; et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat. Genet. 2017, 49, 1373–1384. [Google Scholar] [CrossRef]
  43. Deming, Y.; Filipello, F.; Cignarella, F.; Cantoni, C.; Hsu, S.; Mikesell, R.; Li, Z.; Del-Aguila, J.L.; Dube, U.; Farias, F.G.; et al. The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Sci. Transl. Med. 2019, 11, eaau2291. [Google Scholar] [CrossRef] [Green Version]
  44. Karahan, H.; Smith, D.C.; Kim, B.; Dabin, L.C.; Al-amin, M.; Wijeratne, H.R.S.; Pennington, T.; Viana, G.; Mccord, B.; Lin, P.B.; et al. Deletion of Abi3 gene locus exacerbates neuropathological features of Alzheimer’s disease in a mouse model of Abeta amyloidosis. Sci. Adv. 2021, 3954, 1–20. [Google Scholar]
  45. Andreone, B.J.; Przybyla, L.; Llapashtica, C.; Rana, A.; Davis, S.S.; van Lengerich, B.; Lin, K.; Shi, J.; Mei, Y.; Astarita, G.; et al. Alzheimer’s-associated PLCγ2 is a signaling node required for both TREM2 function and the inflammatory response in human microglia. Nat. Neurosci. 2020, 23, 927–938. [Google Scholar] [CrossRef]
  46. Aikawa, T.; Ren, Y.; Yamazaki, Y.; Tachibana, M.; Johnson, M.R.; Anderson, C.T.; Martens, Y.A.; Holm, M.-L.; Asmann, Y.W.; Saito, T.; et al. ABCA7 haplodeficiency disturbs microglial immune responses in the mouse brain. Proc. Natl. Acad. Sci. USA 2019, 116, 23790–23796. [Google Scholar] [CrossRef] [PubMed]
  47. Jehle, A.W.; Gardai, S.J.; Li, S.; Linsel-Nitschke, P.; Morimoto, K.; Janssen, W.J.; Vandivier, R.W.; Wang, N.; Greenberg, S.; Dale, B.M.; et al. ATP-binding cassette transporter A7 enhances phagocytosis of apoptotic cells and associated ERK signaling in macrophages. J. Cell Biol. 2006, 174, 547–556. [Google Scholar] [CrossRef] [Green Version]
  48. Raj, T.; Ryan, K.J.; Replogle, J.M.; Chibnik, L.B.; Rosenkrantz, L.; Tang, A.; Rothamel, K.; Stranger, B.E.; Bennett, D.A.; Evans, D.A.; et al. CD33: Increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 2014, 23, 2729–2736. [Google Scholar] [CrossRef] [Green Version]
  49. Chan, G.; White, C.C.; Winn, P.A.; Cimpean, M.; Replogle, J.M.; Glick, L.R.; Cuerdon, N.E.; Ryan, K.J.; Johnson, K.A.; Schneider, J.A.; et al. CD33 modulates TREM2: Convergence of Alzheimer loci. Nat. Neurosci. 2015, 18, 1556–1558. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Rathore, N.; Ramani, S.R.; Pantua, H.; Payandeh, J.; Bhangale, T.; Wuster, A.; Kapoor, M.; Sun, Y.; Kapadia, S.B.; Gonzalez, L.; et al. Paired Immunoglobulin-like Type 2 Receptor Alpha G78R variant alters ligand binding and confers protection to Alzheimer’s disease. PLoS Genet. 2018, 14, e1007427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Jones, R.E.; Andrews, R.; Holmans, P.; Hill, M.; Taylor, P.R. Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease. Sci. Rep. 2021, 11, 14935. [Google Scholar] [CrossRef]
  52. Rustenhoven, J.; Smith, A.M.; Smyth, L.C.; Jansson, D.; Scotter, E.L.; Swanson, M.E.V.; Aalderink, M.; Coppieters, N.; Narayan, P.; Handley, R.; et al. PU.1 regulates Alzheimer’s disease-associated genes in primary human microglia. Mol. Neurodegener. 2018, 13, 44. [Google Scholar] [CrossRef] [PubMed]
  53. Shinohara, M.; Tachibana, M.; Kanekiyo, T.; Bu, G. Role of LRP1 in the pathogenesis of Alzheimer’s disease: Evidence from clinical and preclinical studies. J. Lipid Res. 2017, 58, 1267–1281. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Sakae, N.; Liu, C.-C.; Shinohara, M.; Frisch-Daiello, J.; Ma, L.; Yamazaki, Y.; Tachibana, M.; Younkin, L.; Kurti, A.; Carrasquillo, M.M.; et al. ABCA7 Deficiency Accelerates Amyloid-β Generation and Alzheimer’s Neuronal Pathology. J. Neurosci. 2016, 36, 3848–3859. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Biffi, A.; Shulman, J.M.; Jagiella, J.M.; Cortellini, L.; Ayres, A.M.; Schwab, K.; Brown, D.L.; Silliman, S.L.; Selim, M.; Worrall, B.B.; et al. Genetic variation at CR1 increases risk of cerebral amyloid angiopathy. Neurology 2012, 78, 334–341. [Google Scholar] [CrossRef] [Green Version]
  56. Thomas, R.S.; Henson, A.; Gerrish, A.; Jones, L.; Williams, J.; Kidd, E.J. Decreasing the expression of PICALM reduces endocytosis and the activity of β-secretase: Implications for Alzheimer’s disease. BMC Neurosci. 2016, 17, 50. [Google Scholar] [CrossRef] [Green Version]
  57. Uddin, M.S.; Kabir, M.T.; Begum, M.M.; Islam, M.S.; Behl, T.; Ashraf, G.M. Exploring the Role of CLU in the Pathogenesis of Alzheimer’s Disease. Neurotox. Res. 2020, 39, 2108–2119. [Google Scholar] [CrossRef] [PubMed]
  58. Calafate, S.; Flavin, W.; Verstreken, P.; Moechars, D. Loss of Bin1 Promotes the Propagation of Tau Pathology. Cell Rep. 2016, 17, 931–940. [Google Scholar] [CrossRef] [Green Version]
  59. Frieden, C.; Wang, H.; Ho, C.M.W. A mechanism for lipid binding to apoE and the role of intrinsically disordered regions coupled to domain–domain interactions. Proc. Natl. Acad. Sci. USA 2017, 114, 6292–6297. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Salazar, S.V.; Cox, T.O.; Lee, S.; Brody, A.H.; Chyung, A.S.; Haas, L.T.; Strittmatter, S.M. Alzheimer’s Disease Risk Factor Pyk2 Mediates Amyloid-β-Induced Synaptic Dysfunction and Loss. J. Neurosci. 2019, 39, 758–772. [Google Scholar] [CrossRef] [Green Version]
  61. Prokopenko, D.; Morgan, S.L.; Mullin, K.; Hofmann, O.; Chapman, B.; Kirchner, R.; Amberkar, S.; Wohlers, I.; Lange, C.; Hide, W.; et al. Whole-genome sequencing reveals new Alzheimer’s disease–associated rare variants in loci related to synaptic function and neuronal development. Alzheimer’s Dement. 2021, 17, 1509–1527. [Google Scholar] [CrossRef] [PubMed]
  62. Tian, Y.; Chang, J.C.; Fan, E.Y.; Flajolet, M.; Greengard, P. Adaptor complex AP2/PICALM, through interaction with LC3, targets Alzheimer’s APP-CTF for terminal degradation via autophagy. Proc. Natl. Acad. Sci. USA 2013, 110, 17071–17076. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Knupp, A.; Mishra, S.; Martinez, R.; Braggin, J.E.; Szabo, M.; Kinoshita, C.; Hailey, D.W.; Small, S.A.; Jayadev, S.; Young, J.E. Depletion of the AD Risk Gene SORL1 Selectively Impairs Neuronal Endosomal Traffic Independent of Amyloidogenic APP Processing. Cell Rep. 2020, 31, 107719. [Google Scholar] [CrossRef] [PubMed]
  64. Andrews, S.J.; Fulton-howard, B.; Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 2020, 19, 326–335. [Google Scholar] [CrossRef]
  65. Zhou, X.; Chen, Y.; Mok, K.Y.; Kwok, T.C.Y.; Mok, V.C.T.; Guo, Q.; Ip, F.C.; Chen, Y.; Mullapudi, N.; Weiner, M.W.; et al. Non-coding variability at the APOE locus contributes to the Alzheimer’s risk. Nat. Commun. 2019, 10, 1–16. [Google Scholar] [CrossRef] [Green Version]
  66. Amlie-Wolf, A.; Tang, M.; Way, J.; Dombroski, B.; Jiang, M.; Vrettos, N.; Chou, Y.F.; Zhao, Y.; Kuzma, A.; Mlynarski, E.E.; et al. Inferring the Molecular Mechanisms of Noncoding Alzheimer’s Disease-Associated Genetic Variants. J. Alzheimer’s Dis. 2019, 72, 301–318. [Google Scholar] [CrossRef] [PubMed]
  67. Frankish, A.; Diekhans, M.; Jungreis, I.; Lagarde, J.; Loveland, J.E.; Mudge, J.M.; Sisu, C.; Wright, J.C.; Armstrong, J.; Barnes, I.; et al. Gencode 2021. Nucleic Acids Res. 2021, 49, D916–D923. [Google Scholar] [CrossRef] [PubMed]
  68. Chen, G.; Qiu, C.; Zhang, Q.; Liu, B.; Cui, Q. Genome-Wide Analysis of Human SNPs at Long Intergenic Noncoding RNAs. Hum. Mutat. 2013, 34, 338–344. [Google Scholar] [CrossRef] [PubMed]
  69. Han, Z.; Xue, W.; Tao, L.; Zhu, F. Identification of Key Long Non-Coding RNAs in the Pathology of Alzheimer’s Disease and their Functions Based on Genome-Wide Associations Study, Microarray, and RNA-seq Data. J. Alzheimer’s Dis. 2019, 68, 339–355. [Google Scholar] [CrossRef] [PubMed]
  70. Hock, C.; Heese, K.; Hulette, C.; Rosenberg, C.; Otten, U. Region-specific neurotrophin imbalances in Alzheimer disease: Decreased levels of brain-derived neurotrophic factor and increased levels of nerve growth factor in hippocampus and cortical areas. Arch. Neurol. 2000, 57, 846–851. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Jiao, S.-S.; Shen, L.-L.; Zhu, C.; Bu, X.-L.; Liu, Y.-H.; Liu, C.-H.; Yao, X.-Q.; Zhang, L.-L.; Zhou, H.-D.; Walker, D.G.; et al. Brain-derived neurotrophic factor protects against tau-related neurodegeneration of Alzheimer’s disease. Transl. Psychiatry 2016, 6, e907. [Google Scholar] [CrossRef]
  72. Eremenko, E.; Mittal, K.; Berner, O.; Kamenetsky, N.; Nemirovsky, A.; Elyahu, Y.; Monsonego, A. BDNF-producing, amyloid-beta specific CD4 T cells as targeted drug-delivery vehicles in Alzheimer’s disease. EBioMedicine 2019, 43, 424–434. [Google Scholar] [CrossRef] [PubMed]
  73. Malinin, N.L.; Wright, S.; Seubert, P.; Schenk, D.; Griswold-Prenner, I. Amyloid-β neurotoxicity is mediated by FISH adapter protein and ADAM12 metalloprotease activity. Proc. Natl. Acad. Sci. USA 2005, 102, 3058–3063. [Google Scholar] [CrossRef] [Green Version]
  74. Peterson, R.E.; Kuchenbaecker, K.; Walters, R.K.; Chen, C.; Popejoy, A.B.; Periyasamy, S.; Lam, M.; Iyegbe, C.; Strawbridge, R.J.; Brick, L.; et al. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell 2019, 179, 589–603. [Google Scholar] [CrossRef]
  75. Gurdasani, D.; Barroso, I.; Zeggini, E.; Sandhu, M.S. Genomics of disease risk in globally diverse populations. Nat. Rev. Genet. 2019, 20, 520–535. [Google Scholar] [CrossRef] [PubMed]
  76. Kretzschmar, G.C.; Alencar, N.M.; Suellen, S.; Sulzbach, C.D.; Meissner, C.G.; Petzl-erler, M.L.; Souza, R.L.R.; Beate, A.; Boldt, W. GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs. Front. Mol. Biosci. 2021, 8, 1–11. [Google Scholar] [CrossRef] [PubMed]
  77. Kramer, N.J.; Carlomagno, Y.; Zhang, Y.-J.; Almeida, S.; Cook, C.N.; Gendron, T.F.; Prudencio, M.; Van Blitterswijk, M.; Belzil, V.; Couthouis, J.; et al. Spt4 selectively regulates the expression of C9orf72 sense and antisense mutant transcripts. Science 2016, 353, 708–712. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. McKhann, G.M.; Knopman, D.S.; Chertkow, H.; Hyman, B.T.; Jack, C.R.J.; Kawas, C.H.; Klunk, W.E.; Koroshetz, W.J.; Manly, J.J.; Mayeux, R.; et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011, 7, 263–269. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Zetterberg, H.; Bendlin, B.B. Biomarkers for Alzheimer’s disease—Preparing for a new era of disease-modifying therapies. Mol. Psychiatry 2021, 26, 296–308. [Google Scholar] [CrossRef] [PubMed]
  80. Masters, C.L.; Bateman, R.; Blennow, K.; Rowe, C.C.; Sperling, R.A.; Cummings, J.L. Alzheimer’s disease. Nat. Rev. Dis. Prim. 2015, 1, 1–18. [Google Scholar] [CrossRef]
  81. Derrien, T.; Johnson, R.; Bussotti, G.; Tanzer, A.; Djebali, S.; Tilgner, H.; Guernec, G.; Martin, D.; Merkel, A.; Knowles, D.G.; et al. The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression. Genome Res. 2012, 22, 1775–1789. [Google Scholar] [CrossRef] [Green Version]
  82. Garofalo, M.; Pandini, C.; Sproviero, D.; Pansarasa, O.; Cereda, C.; Gagliardi, S. Advances with long non-coding rnas in alzheimer’s disease as peripheral biomarker. Genes 2021, 12, 1124. [Google Scholar] [CrossRef] [PubMed]
  83. Simone, R.; Javad, F.; Emmett, W.; Wilkins, O.G.; Almeida, F.L.; Barahona-Torres, N.; Zareba-Paslawska, J.; Ehteramyan, M.; Zuccotti, P.; Modelska, A.; et al. MIR-NATs repress MAPT translation and aid proteostasis in neurodegeneration. Nature 2021, 594, 117–123. [Google Scholar] [CrossRef] [PubMed]
  84. Faghihi, M.A.; Modarresi, F.; Khalil, A.M.; Wood, D.E.; Sahagan, B.G.; Morgan, T.E.; Finch, C.E.; St, G.; Iii, L.; Kenny, P.J.; et al. Expression of a noncoding RNA is elevated in Alzheimer’s disease and drives rapid feed-forward regulation of beta-secretase. Nat. Med. 2008, 14, 723–730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Doxtater, K.; Tripathi, M.; Khan, M. Recent advances on the role of long non-coding RNAs in Alzheimer’s disease. Neural Regen. Res. 2020, 15, 2253–2254. [Google Scholar]
  86. Mus, E.; Hof, P.R.; Tiedge, H. Dendritic BC200 RNA in aging and in Alzheimer’s disease. Proc. Natl. Acad. Sci. USA 2007, 104, 10679–10684. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Li, H.; Zheng, L.; Jiang, A.; Mo, Y.; Gong, Q. Identification of the biological affection of long noncoding RNA BC200 in Alzheimer’s disease. Neuroreport 2018, 29, 1061–1067. [Google Scholar] [CrossRef] [PubMed]
  88. Massone, S.; Vassallo, I.; Fiorino, G.; Castelnuovo, M.; Barbieri, F.; Borghi, R.; Tabaton, M.; Robello, M.; Gatta, E.; Russo, C.; et al. 17A, a novel non-coding RNA, regulates GABA B alternative splicing and signaling in response to inflammatory stimuli and in Alzheimer disease. Neurobiol. Dis. 2011, 41, 308–317. [Google Scholar] [CrossRef] [PubMed]
  89. Massone, S.; Ciarlo, E.; Vella, S.; Nizzari, M.; Florio, T.; Russo, C.; Cancedda, R.; Pagano, A. NDM29, a RNA polymerase III-dependent non coding RNA, promotes amyloidogenic processing of APP and amyloid β secretion. Biochim. Biophys. Acta-Mol. Cell Res. 2012, 1823, 1170–1177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Ciarlo, E.; Massone, S.; Penna, I.; Nizzari, M.; Gigoni, A.; Dieci, G.; Russo, C.; Florio, T.; Cancedda, R.; Pagano, A. An intronic ncRNA-dependent regulation of SORL1 expression affecting Aβ formation is upregulated in post-mortem Alzheimer’s disease brain samples. DMM Dis. Model. Mech. 2013, 6, 424–433. [Google Scholar] [CrossRef] [Green Version]
  91. Rogaeva, E.; Meng, Y.; Lee, J.H.; Gu, Y.; Kawarai, T.; Zou, F.; Katayama, T.; Baldwin, C.T.; Cheng, R.; Hasegawa, H.; et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer disease. Nat. Genet. 2007, 39, 168–177. [Google Scholar] [CrossRef] [PubMed]
  92. Dodson, S.E.; Gearing, M.; Lippa, C.F.; Montine, T.J.; Levey, A.I.; Lah, J.J. LR11/SorLA expression is reduced in sporadic Alzheimer disease but not in familial Alzheimer disease. J. Neuropathol. Exp. Neurol. 2006, 65, 866–872. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Khvotchev, M.; Südhof, T.C. Proteolytic Processing of Amyloid-β Precursor Protein by Secretases Does Not Require Cell Surface Transport. J. Biol. Chem. 2004, 279, 47101–47108. [Google Scholar] [CrossRef] [Green Version]
  94. Andersen, O.M.; Reiche, J.; Schmidt, V.; Gotthardt, M.; Spoelgen, R.; Behlke, J.; von Arnim, C.A.F.; Breiderhoff, T.; Jansen, P.; Wu, X.; et al. Neuronal sorting protein-related receptor sorLA/LR11 regulates processing of the amyloid precursor protein. Proc. Natl. Acad. Sci. USA 2005, 102, 13461–13466. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Magistri, M.; Velmeshev, D.; Makhmutova, M.; Faghihi, M.A. Transcriptomics Profiling of Alzheimer’s Disease Reveal Neurovascular Defects, Altered Amyloid-β Homeostasis, and Deregulated Expression of Long Noncoding RNAs. J. Alzheimer’s Dis. 2015, 48, 647–665. [Google Scholar] [CrossRef] [Green Version]
  96. Gu, C.; Chen, C.; Wu, R.; Dong, T.; Hu, X.; Yao, Y.; Zhang, Y. Long noncoding RNA EBF3-AS promotes neuron apoptosis in Alzheimer’s disease. DNA Cell Biol. 2018, 37, 220–226. [Google Scholar] [CrossRef] [PubMed]
  97. Van Gool, B.; Storck, S.E.; Reekmans, S.M.; Lechat, B.; Gordts, P.L.S.M.; Pradier, L.; Pietrzik, C.U.; Roebroek, A.J.M. LRP1 Has a Predominant Role in Production over Clearance of Aβ in a Mouse Model of Alzheimer’s Disease. Mol. Neurobiol. 2019, 56, 7234–7245. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Tachibana, M.; Holm, M.; Liu, C.; Shinohara, M.; Aikawa, T.; Oue, H.; Yamazaki, Y. APOE4-mediated amyloid-β pathology depends on its neuronal receptor LRP1. J. Clin. Investig. 2019, 129, 1272–1277. [Google Scholar] [CrossRef]
  99. Rauch, J.N.; Luna, G.; Guzman, E.; Audouard, M.; Challis, C.; Sibih, Y.E.; Leshuk, C.; Hernandez, I.; Wegmann, S.; Hyman, B.T.; et al. LRP1 is a master regulator of tau uptake and spread. Nature 2020, 580, 381–385. [Google Scholar] [CrossRef] [PubMed]
  100. Yamanaka, Y.; Faghihi, M.A.; Magistri, M.; Alvarez-Garcia, O.; Lotz, M.; Wahlestedt, C. Antisense RNA Controls LRP1 Sense Transcript Expression Through Interaction With a Chromatin-Associated Protein, HMGB2. Cell Rep. 2015, 344, 1173–1178. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  101. Miller, J.A.; Guillozet-bongaarts, A.; Gibbons, L.E.; Postupna, N.; Renz, A.; Beller, A.E.; Sunkin, S.M.; Ng, L.; Rose, S.E.; Smith, K.A.; et al. Neuropathological and transcriptomic characteristics of the aged brain. eLife 2017, 6, e31126. [Google Scholar] [CrossRef]
  102. Bennett, D.A.; Buchman, A.S.; Boyle, P.A.; Barnes, L.L.; Wilson, R.S.; Schneider, J.A. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimer’s Dis. 2018, 64, S161–S189. [Google Scholar] [CrossRef]
  103. Zhou, X.; Xu, J. Identification of Alzheimer’s disease-associated long noncoding RNAs. Neurobiol. Aging 2015, 36, 2925–2931. [Google Scholar] [CrossRef] [PubMed]
  104. Zhou, M.; Zhao, H.; Wang, X.; Sun, J.; Su, J. Analysis of long noncoding RNAs highlights region-specific altered expression patterns and diagnostic roles in Alzheimer’s disease. Brief. Bioinform. 2019, 20, 598–608. [Google Scholar] [CrossRef] [PubMed]
  105. Wu, J.; Chen, L.; Zheng, C.; Xu, S.; Gao, Y.; Wang, J. Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease. Interdiscip. Sci. Comput. Life Sci. 2019, 11, 645–654. [Google Scholar] [CrossRef] [PubMed]
  106. Shi, Y.; Liu, H.; Yang, C.; Xu, K.; Cai, Y.; Wang, Z. Transcriptomic Analyses for Identification and Prioritization of Genes Associated With Alzheimer ’ s Disease in Humans. Front. Bioeng. Biotechnol. 2020, 8, 1–10. [Google Scholar] [CrossRef]
  107. Cristóvão, J.S.; Gomes, C.M. S100 Proteins in Alzheimer’s Disease. Front. Neurosci. 2019, 13, 463. [Google Scholar] [CrossRef]
  108. Annese, A.; Manzari, C.; Lionetti, C.; Picardi, E.; Horner, D.S.; Chiara, M.; Caratozzolo, M.F.; Tullo, A.; Fosso, B.; Pesole, G.; et al. Whole transcriptome profiling of Late-Onset Alzheimer’s Disease patients provides insights into the molecular changes involved in the disease. Sci. Rep. 2018, 8, 1–15. [Google Scholar] [CrossRef] [Green Version]
  109. Rahman, A.; Jackson, H.; Hristov, H.; Isaacson, R.S.; Saif, N.; Shetty, T.; Etingin, O.; Henchcliffe, C.; Brinton, R.D.; Mosconi, L. Sex and Gender Driven Modifiers of Alzheimer’s: The Role for Estrogenic Control Across Age, Race, Medical, and Lifestyle Risks. Front. Aging Neurosci. 2019, 11, 315. [Google Scholar] [CrossRef] [Green Version]
  110. Cao, M.; Li, H.; Zhao, J.; Cui, J.; Hu, G. Identification of age- and gender-associated long noncoding RNAs in the human brain with Alzheimer’s disease. Neurobiol. Aging 2019, 81, 116–126. [Google Scholar] [CrossRef]
  111. Feng, L.; Liao, Y.; He, J.; Xie, C.; Chen, S.; Fan, H.; Su, Z.; Wang, Z. Plasma long non-coding RNA BACE1 as a novel biomarker for diagnosis of Alzheimer disease. BCM Neurol. 2018, 18, 1–8. [Google Scholar] [CrossRef] [Green Version]
  112. Fotuhi, S.N.; Khalaj-kondori, M.; Feizi, M.A.H.; Talebi, M. Long Non-coding RNA BACE1-AS May Serve as an Alzheimer’s Disease. J. Mol. Neurosci. 2019, 69, 351–359. [Google Scholar] [CrossRef] [PubMed]
  113. Wang, D.; Wang, P.; Bian, X.; Xu, S.; Zhou, Q.; Zhang, Y.; Ding, M.; Han, M.; Huang, L.; Bi, J.; et al. Elevated plasma levels of exosomal BACE1-AS combined with the volume and thickness of the right entorhinal cortex may serve as a biomarker for the detection of Alzheimer’s disease. Mol. Med. Rep. 2020, 22, 227–238. [Google Scholar] [CrossRef] [PubMed]
  114. Deng, Y.; Xiao, L.; Li, W.; Tian, M.; Feng, X.; Feng, H.; Hou, D. Plasma long noncoding RNA 51A as a stable biomarker of Alzheimer’s disease. Int. J. Clin. Exp. Pathol. 2017, 10, 4694–4699. [Google Scholar]
  115. Ma, P.; Li, Y.; Zhang, W.; Fang, F.; Sun, J.; Liu, M.; Li, K.; Dong, L. Long Non-coding RNA MALAT1 Inhibits Neuron Apoptosis and Neuroinflammation While Stimulates Neurite Outgrowth and Its Correlation With MiR-125b Mediates PTGS2, CDK5 and FOXQ1 in Alzheimer’s Disease. Curr. Alzheimer Res. 2019, 16, 596–612. [Google Scholar] [CrossRef]
  116. Zhuang, J.; Cai, P.; Chen, Z.; Yang, Q.; Chen, X.; Wang, X.; Zhuang, X. Long noncoding RNA MALAT1 and its target microRNA-125b are potential biomarkers for Alzheimer’s disease management via interactions with FOXQ1, PTGS2 and CDK5. Am. J. Transl. Res. 2020, 12, 5940–5954. [Google Scholar] [PubMed]
  117. Gui, Y.X.; Liu, H.; Zhang, L.S.; Lv, W.; Hu, X.Y. Altered microRNA profiles in cerebrospinal fluid exosome in Parkinson disease and Alzheimer disease. Oncotarget 2015, 6, 37043–37053. [Google Scholar] [CrossRef] [Green Version]
  118. Garofalo, M.; Pandini, C.; Bordoni, M.; Pansarasa, O.; Rey, F.; Costa, A.; Minafra, B.; Diamanti, L.; Zucca, S.; Carelli, S.; et al. Alzheimer’s, Parkinson’s Disease and Amyotrophic Lateral Sclerosis Gene Expression Patterns Divergence Reveals Different Grade of RNA Metabolism Involvement. Int. J. Mol. Sci. 2020, 21, 9500. [Google Scholar] [CrossRef]
  119. Wang, J.; Zhou, T.; Wang, T.; Wang, B. Suppression of lncRNA-ATB prevents amyloid-β-induced neurotoxicity in PC12 cells via regulating miR-200/ZNF217 axis. Biomed. Pharmacother. 2018, 108, 707–715. [Google Scholar] [CrossRef]
  120. Huaying, C.; Xing, J.; Luya, J.; Linhui, N.; Di, S.; Xianjun, D. A Signature of Five Long Non-Coding RNAs for Predicting the Prognosis of Alzheimer’s Disease Based on Competing Endogenous RNA Networks. Front. Aging Neurosci. 2021, 12, 1–12. [Google Scholar] [CrossRef]
  121. Jin, Y.; Tu, Q.; Liu, M. MicroRNA-125b regulates Alzheimer’s disease through SphK1 regulation. Mol. Med. Rep. 2018, 18, 2373–2380. [Google Scholar] [CrossRef]
  122. Ma, X.; Liu, L.; Meng, J. MicroRNA-125b promotes neurons cell apoptosis and Tau phosphorylation in Alzheimer’s disease. Neurosci. Lett. 2017, 661, 57–62. [Google Scholar] [CrossRef]
  123. Kim, J.; Piao, H.-L.; Kim, B.-J.; Yao, F.; Han, Z.; Wang, Y.; Xiao, Z.; Siverly, A.N.; Lawhon, S.E.; Ton, B.N.; et al. Long noncoding RNA MALAT1 suppresses breast cancer metastasis. Nat. Genet. 2018, 50, 1705–1715. [Google Scholar] [CrossRef]
  124. Soreq, L.; Guffanti, A.; Salomonis, N.; Simchovitz, A.; Israel, Z.; Bergman, H.; Soreq, H. Long Non-Coding RNA and Alternative Splicing Modulations in Parkinson’s Leukocytes Identified by RNA Sequencing. PLoS Comput. Biol. 2014, 10, e1003647. [Google Scholar] [CrossRef]
  125. Kurt, S.; Tomatir, A.G.; Tokgun, P.E.; Oncel, C. Altered Expression of Long Non-coding RNAs in Peripheral Blood Mononuclear Cells of Patients with Alzheimer’s Disease. Mol. Neurobiol. 2020, 57, 5352–5361. [Google Scholar] [CrossRef] [PubMed]
  126. Wei, L.; Wu, T.; He, P.; Zhang, J.-L.; Wu, W. LncRNA ATB promotes the proliferation and metastasis of lung cancer via activation of the p38 signaling pathway. Oncol. Lett. 2018, 16, 3907–3912. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Li, R.-H.; Chen, M.; Liu, J.; Shao, C.-C.; Guo, C.-P.; Wei, X.-L.; Li, Y.-C.; Huang, W.-H.; Zhang, G.-J. Long noncoding RNA ATB promotes the epithelial−mesenchymal transition by upregulating the miR-200c/Twist1 axe and predicts poor prognosis in breast cancer. Cell Death Dis. 2018, 9, 1171. [Google Scholar] [CrossRef] [PubMed]
  128. Jang, S.Y.; Kim, G.; Park, S.Y.; Lee, Y.R.; Hoon, S.; Kim, H.S.; Yoon, J.S.; Lee, J.S.; Kweon, Y.; Ha, T.; et al. Clinical significance of lncRNA-ATB expression in human hepatocellular carcinoma. Oncotarget 2017, 8, 78588–78597. [Google Scholar] [CrossRef] [PubMed]
  129. Zhu, H.-Y.; Bai, W.-D.; Li, C.; Zheng, Z.; Guan, H.; Liu, J.-Q.; Yang, X.-K.; Han, S.-C.; Gao, J.-X.; Wang, H.-T.; et al. Knockdown of lncRNA-ATB suppresses autocrine secretion of TGF-β2 by targeting ZNF217 via miR-200c in keloid fibroblasts. Sci. Rep. 2016, 6, 24728. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  130. Dang, X.; Lian, L.; Wu, D. The diagnostic value and pathogenetic role of lncRNA-ATB in patients with osteoarthritis. Cell. Mol. Biol. Lett. 2018, 23, 55. [Google Scholar] [CrossRef]
  131. Fu, J.; Peng, L.; Tao, T.; Chen, Y.; Li, Z.; Li, J. Regulatory roles of the miR-200 family in neurodegenerative diseases. Biomed. Pharmacother. 2019, 119, 109409. [Google Scholar] [CrossRef]
  132. Ala, U. Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story. Cells 2020, 9, 1574. [Google Scholar] [CrossRef] [PubMed]
  133. Wolter, J.M.; Mao, H.; Fragola, G.; Simon, J.M.; Krantz, J.L.; Bazick, H.O.; Oztemiz, B.; Stein, J.L.; Zylka, M.J. Cas9 gene therapy for Angelman syndrome traps Ube3a-ATS long non-coding RNA. Nature 2020, 587, 281–284. [Google Scholar] [CrossRef] [PubMed]
Table
Table 3. Examples of deregulated lncRNAs in peripheral tissue samples from patients with AD.
Table 3. Examples of deregulated lncRNAs in peripheral tissue samples from patients with AD.
LncRNA IDTrendEvaluated Tissue/SamplesRole in AD?Reference(s)
BACE1-AS↑ in ADPlasma; Plasma derived exosomesYes (see Table 2)[111,112,113]
BC200No change in ADPlasmaYes (see Table 2)[111,113]
51ANo change in AD;
↑ in AD;		Plasma
Plasma derived exosomes		Yes (see Table 2)[111]
[114]
MALAT1↓ in ADPlasma; CSFReported to prevent neuron apoptosis, promote neurite outgrowth, and reduce inflammation in two AD mouse models[115,116]
RP11-462G22.1, PCA3↑ in AD (and PD)CSF derived exosomesUnknown [117]
CH507-513H4.4, CH507-513H4.6, CH507-513H4.3↑ in ADPBMCsUnknown[118]
LncRNA-ATB↑ in ADCSF; SerumSuppression of this lncRNA might have a protective effect against Aβ-induced neurotoxicity via regulation of miR-200[119]
PART1↓ in ADSerumUnknown [120]
UBE3A-ATS↑ in ADSerumUnknown [120]
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Policarpo, R.; d’Ydewalle, C. Missing lnc(RNAs) in Alzheimer’s Disease? Genes 2022, 13, 39. https://doi.org/10.3390/genes13010039

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