Reduced levels of N6-methyladenosine in RNA of peripheral blood mononuclear cells from patients with Alzheimer's disease

Background

Alzheimer’s disease (AD), a degenerative brain disorder and the most prevalent form of dementia, involves two hallmark lesions: extracellular amyloid plaques and neurofibrillary tangles [1]. The causes of AD are unclear and are thought to involve a combination of genetic and environmental factors [2]. Therefore, epigenetic modifications caused by environmental factors may contribute to AD pathogenesis [3, 4].

Attachment of a methyl group at the nitrogen-6 of adenosine in RNA leads to N6-methyladenosine (m6A) [5], which is the most prevalent reversible post-transcriptional modification of RNA in mammals. This modification helps regulate the localization, transport, and translation of protein-encoding mRNAs [6, 7]. Methyltransferases such as methyltransferase-like protein 3 (METTL3), METTL14, and Wilms tumor 1-associating protein (WTAP) generate m6A, while demethylases such as AlkB homolog 5 (ALKBH5) and “fat mass and obesity-associated protein” (FTO) remove the modification [8]. Proteins that recognize and bind to m6A, such as YTH domain-containing RNA-binding protein, fragile X mental retardation protein and eukaryotic initiation factor 3, promote the alternative splicing, translation, or decay of the modified RNAs [5, 9–11].

In the healthy brain, m6A is important for synaptic function [12] and cognitive function [6, 7, 13, 14]. Knocking down proteins that bind to m6A in hippocampus alters synaptic gene expression and compromises memory in mice [7, 12]. Downregulating FTO in hippocampus inhibits learning and memory formation in mice [13, 14]. METTL3 promotes the translation of neuronal early response genes, supporting memory consolidation in hippocampus [6].

Brain tissues of AD patients and mouse models of AD show alterations in the level and locations of m6A in RNA. One study of AD patients reported reduced m6A levels in RNA from susceptible pyramidal neurons, but increased m6A levels from glial cells in hippocampus and cortex [15]. Another study reported downregulated METTL3 in hippocampus of AD patients [16]. Different mouse models of AD have suggested reduced [17] or elevated [18] levels of m6A in RNA from cortex and hippocampus.

In this paper, we studied the m6A levels and locations in RNA from peripheral blood mononuclear cells (PBMCs) in both AD patients and healthy individuals. We also compared the expression of the methylases and demethylases that regulate m6A modification and investigated the potential impact of different m6A modifications on gene transcripts in AD. This research aims to understand the involvement of m6A dysregulation in AD pathogenesis and assess the potential of m6A RNA and its regulatory proteins as diagnostic or prognostic biomarkers for AD.

Results

We isolated PBMCs from AD patients and controls (Table 1) and compared them in terms of gene expression, including m6A-related methylases and demethylases. We also compared their transcriptomes in terms of the level and locations of m6A.

Table 1

Demographic and clinical characteristics of AD patients and healthy controls in the study.
Characteristic	AD patients (n = 42)	Healthy controls (n = 42)	P
Age (years)			0.114
Range	50–80	50–81
Mean	67.76	64.83
Sex			0.661
Male	18	20
Female	24	22
Duration of AD (years)	2–8
MMSE score
Range	1–25	27–30
Mean ± SD	11.36 ± 6.100	28.302 ± 1.269	< 0.001
MoCA score
Range	1–23	27–30
Mean ± SD	7.86 ± 4.370	28.265 ± 1.307	< 0.001

AD, Alzheimer’s disease; MMSE, Mini-mental State Examination; MoCA, Montreal Cognitive Assessment; SD, standard deviation.

Association of AD with lower m6A RNA and downregulation of m6A-related enzymes in PBMCs

The relative levels of m6A RNA methylation were lower in PBMCs of AD patients than in controls (Fig. 1A). AD patients showed lower levels of the methyltransferases METTL3, METTL14, and WTAP (Fig. 1B-D), as well as lower levels of the demethylases ALKBH5 and FTO (Fig. 1B-D). Similarly, the plasma of AD patients contained lower levels of all five enzymes than the plasma of controls (Supplementary Fig. 1).

Correlation of m6A level in PBMCs with sex, age, and disease severity of AD patients

The m6A level positively correlated with scores on the Mini-mental State Examination (MMSE; r = 0.372, P = 0.018) and the Montreal Cognitive Assessment (MoCA; r = 0.509, P = 0.001; Fig. 2A, B) after correction for age and sex. The m6A level showed an inverse correlation with age after correction for sex and scores on the MMSE and MoCA (r = -0.319, P = 0.048; Fig. 2C). However, m6A level did not correlate with sex after correction for age and scores on the MMSE and MoCA (P = 0.361; Fig. 2D).

Altered m6A-modified RNA loci in AD patients

The original data of MeRIP-Seq were of high quality, with > 98% of reads meeting the Q20 criterion and > 94% of reads meeting the Q30 criterion (Supplementary Table 1). AD patients showed 393 m6A peaks differing significantly from those in controls, of which 230 were significantly higher and 163 significantly lower in patients (Fig. 3A). The top five upregulated peaks were located in the genes miR1184-3, Notch2nla, Timm50, Rpl23ap88, and Tigar, while the top five downregulated peaks were located in genes Ankrd36b, Bola2b, Golga8a, Rere, and H4c12 (Supplementary Table 2).

The m6A peaks differing between patients and controls were enriched mainly in the coding sequence near the stop codon and 3’-untranslated region (Fig. 3B, C). The peaks differing significantly between the two groups occurred most often in the 3’-untranslated region (49.79%), followed by 5’-untranslated regions (21.39%), other exons (19.70%), and first exons (9.11%) (Fig. 3D). Compared to healthy controls, AD patients showed higher proportions of m6A peaks in 5’-untranslated regions (20.75% vs. 19.30%) and first exons (11.80% vs. 11.71%), but lower proportions in 3’-untranslated regions (45.05% vs. 46.49%) and other exons (22.40% vs. 22.50%) (Fig. 3E, F).

The major GO terms and KEGG pathways involving differential m6A peaks are shown in Fig. 4A-D. In the GO analysis, the upregulated peaks were significantly associated with biological processes of regulation of DNA-templated transcription, signal transduction, and multicellular organism development. The downregulated peaks, in contrast, were associated mainly with the biological processes of regulation of DNA-templated transcription, signal transduction, and negative regulation of transcription by RNA polymerase II. Both upregulated and downregulated peaks were associated with the cellular components of membrane, nucleus, and cytoplasm, as well as with the molecular functions of protein binding, metal ion binding, and DNA binding (Fig. 4A, C).

Upregulated KEGG pathways included cell cycle, antigen processing and presentation, and peroxisome (Fig. 4B). Downregulated KEGG pathways included cAMP signaling, steroid biosynthesis, and pancreatic secretion (Fig. 4D).

Altered gene expression in AD patients

RNA sequencing data showed 477 genes upregulated and 363 genes downregulated in AD patients relative to controls (Fig. 5A). The top five most upregulated genes were Cx3cr1, Gimap4, Adgrg1, Linc00861, and Spn, and the top five most downregulated genes were Cxcl8, Icam1, Namptp1, Tnfaip3, and Per1 (Fig. 5A, B, Supplementary Table 3). The altered expression of five upregulated genes (Cx3cr1, Gimap4, Spn, Tldc2, and Pars2) and four downregulated genes (Cxcl8, Icam1, Tnfaip3, and Il1b) was verified using qRT-PCR (Fig. 5C).

The major GO terms and KEGG pathways involving differentially expressed genes are shown in Fig. 5D-G. Upregulated genes were associated with the top three GO biological processes of signal transduction, regulation of DNA-templated transcription, and multicellular organism development. The top three GO cellular components were membrane, integral component of membrane, and plasma membrane, while the top three GO molecular functions were protein binding, metal ion binding, and DNA binding (Fig. 5D).

Downregulated genes were associated with the top three GO biological processes of signal transduction, positive regulation of transcription by RNA polymerase II, and immune response. The top three GO cellular components were membrane, cytoplasm, and plasma membrane, while the top three GO molecular functions were protein binding, metal ion binding, and DNA binding (Fig. 5F).

Upregulated KEGG pathways included herpes simplex virus 1 infection, antigen processing and presentation, and natural killer cell-mediated cytotoxicity (Fig. 5E). Downregulated KEGG pathways included tumor necrosis factor (TNF) signaling pathway, NF-kappa B signaling pathway and NOD-like receptor signaling pathway (Fig. 5G).

Correlation of altered patterns of m6A RNA with altered gene expression in PBMCs of AD patients

We identified the set of overlapping genes whose m6A methylation at the RNA level and whose expression were altered, and classified them into four groups (Fig. 6A): hypermethylation and upregulation, 123 genes; hypomethylation and upregulation, 119 genes; hypomethylation and downregulation, 58 genes; and hypermethylation and downregulation, 106 genes.

In the GO analysis of overlapping genes, the top three biological processes were signal transduction, regulation of DNA-templated transcription, and G protein-coupled receptor signaling pathway. The top three cellular components were membrane, cytoplasm, and plasma membrane, while the top three molecular functions were protein binding, metal ion binding, and DNA binding (Fig. 6C). The KEGG analysis of the overlapping genes identified the following main pathways: cytokine-cytokine receptor interaction, cAMP signaling pathway, cell adhesion molecules, TNF signaling, and MAPK signaling (Fig. 6B).

Discussion

Increasing evidence supports a potential pathogenic role for epigenetic modifications in AD. Aberrant m6A methylation has been reported in brain tissues from AD patients and animal models, but whether this alteration also occurs in PBMCs remains unknown. This study showed downregulated m6A RNA in PBMCs and plasma of AD patients compared with healthy controls. In addition, the m6A methyltransferases METTL3, METTL14 and WTAP as well as demethylases FTO and ALKBH5 were downregulated in PBMCs and plasma of patients. We performed MeRIP-seq and RNA sequencing analyses and found that many of the m6A peaks differing between patients and controls were related to cancer and neuroinflammation.

The observation of decreased m6A modification in PBMCs of AD patients, which is consistent with previous results in pyramidal neurons of patients with AD and mild cognitive impairment and with results in a mouse model of AD [15, 17]. The observations in PBMCs may help provide some insights into the pathophysiological state in the brain. The m6A modification has been associated with downregulation of mRNAs and therefore proteins in a mouse model of AD [17]. Little is known about the functional impact of this downregulation. In pyramidal neurons in the hippocampus of mice, decreased m6A levels in neuronal lead to oxidative stress and aberrant cell cycling [15], which may contribute to AD.

We found downregulation of the m6A methyltransferases METTL3, METTL14 and WTAP, as well as demethylases FTO and ALKBH5 in PBMCs from AD patients. METTL3 has been shown to be downregulated in the middle temporal gyrus of patients with mild cognitive impairment, although that study did not observe downregulation of METTL14 or FTO [15]. Downregulation of METTL3 has also been reported in another study of brains from AD patients [16]. In fact, reduced METTL3 activity has been proposed as an initiating factor in m6A dysregulation, and then leading to a reduction in levels of other m6A regulators reduction at a later stages of AD [15]: in that study, METTL3 depletion reduced m6A levels in the hippocampus of adult mice and thus caused significant cognitive and memory impairment, which was accompanied by dramatic neuronal loss and death along with oxidative stress and aberrant cell cycle events. Interestingly, that study showed that METTL3 overexpression rescued amyloid β-induced synaptic damage and cognitive impairment. On the other hand, the APP/PS1 mouse model of AD showed elevated m6A levels in cortex and hippocampus [18]. These apparently contradictory results may reflect differences in animal models, pathological state, disease stage, and other factors.

Whatever the explanation, past studies and the present work clearly implicate altered m6A modification in AD. Indeed, we found that the m6A level correlated inversely with global cognitive status as measured by the MMSE and MoCA. Whether m6A downregulation is a cause or effect in AD and how it influences disease pathology remain to be clarified. It is important to clarify whether altering m6A levels during early disease may benefit AD patients.

We also found a negative association between m6A levels in PBMCs and age of AD patients. Conversely, Shafik et al. showed that, m6A modification displays temporal and spatial dynamics during neurodevelopment and aging [17]. The expression level of m6A methylation was low throughout embryogenesis while increased significantly in adulthood [19]. Shafik et al. also observed more m6A sites as age increases in both mouse and human during the aging process [17].The association of m6A modification with age and disease severity should be explored in greater depth, since it may serve as a useful diagnostic or prognostic biomarker in AD.

Our m6A-modified RNA immunoprecipitation sequencing results showed that, many of the differentially expressed m6A peaks were related with cancer. In addition, Notch2nl was also enriched with upregulated m6A peaks. Notch2nl interacts with Notch receptors and enhances cortical neurogenesis [20], and Notch signaling is involved in maintaining synaptic plasticity, long-term learning and memory, and neurogenesis. Notch is considered to play a role in AD progression [21]. Our KEGG pathway analysis revealed enrichment of cell cycle signaling pathways, which appears consistent with a previous study showing that METTL3 knockdown in the hippocampus alters expression of cell cycle regulators, resulting in unscheduled cell cycle re-entry and activation of apoptotic pathways [15, 22, 23]. Most genes differentially expressed between AD patients and healthy controls in our study were associated with neuroinflammation, including numerous genes related to proinflammatory cytokines, infiltrating immune cells, and activated glial cells, which have been extensively studied for potential roles in AD pathogenesis [24, 25].

By combining MeRIP-seq and RNA sequencing data, our study identified some transcripts that were hyper or hypomethylated and differentially expressed. For example, TNF, IL-17, MAPK, cAMP signaling pathway, and Th17 cell differentiation were enriched. IL-17, a Th17-derived proinflammatory cytokine, is upregulated during AD and triggers the onset of deficits in cognition, short-term memory, and synaptic transmission in mouse models of AD [26, 27]. Th17 cells have been reported to infiltrate into the brain of AD models by disrupting tight junctions in the blood-brain barrier and then activating microglia, which secrete inflammatory molecules such as IL-1β, TNF-ɑ, and IL-6 [28]. One study [29] found that m6A regulators are related to inflammation and the immune microenvironment of AD, the immune infiltrating cells in the m6A patterns with higher severity of AD stage were associated with a Th17-dominant immunity. METTL3 overexpression may activate the TRAF6-NF-κB pathway in an m6A-dependent manner, leading to microglial inflammation [30]. The link between m6A modification and pro- or anti-inflammatory responses may provide new targets for developing therapies against AD and other neuroinflammation-related diseases.

Our findings should be interpreted carefully in light of our small sample and our exclusion of patients with mild cognitive impairment. Nevertheless, our work justifies further research into the utility of m6A RNA and its regulatory proteins in peripheral blood as diagnostic or prognostic biomarkers in AD. Future work can also build on our identification of numerous differentially expressed genes in AD to explore what drives the disease, including the effects of altered m6A modification.

Methods

Patients and samples

Our study was approved by the Ethics Committee of The Affiliated Hospital of Jining Medical University (Approved Number: 2021-12-C013, China), and written consent was provided by each participant before enrolment. From January 2020 to June 2021, we recruited 42 patients who were diagnosed with AD at the memory clinic of the Affiliated Hospital of Jining Medical University. The inclusion criteria for AD patients were: (1) first-time diagnosis of AD according to the criteria of the National Institute on Aging Alzheimer’s Association [31] after clinical evaluation, neuropsychological tests, and magnetic resonance imaging; (2) age between 50 and 80 years; and (3) no history of anti-dementia or mood-stabilizing medications. The exclusion criteria were: (1) history of cerebrovascular disease associated with cognitive impairment; (2) frontotemporal dementia, dementia with Lewy bodies, primary progressive aphasia, and/or Parkinson disease dementia; (3) dementia due to other causes such as infection, poisoning, drugs, and/or metabolic diseases; (4) complications involving functional failure of the heart, lung, liver, kidney, and/or other important organs; or (5) incomplete clinical data.

In addition, we recruited 42 healthy volunteers from the health management center of the same hospital, who were matched to enrolled patients in terms of sex and age. The inclusion criteria for healthy controls were: (1) age older than 50 years; and (2) no history, symptoms, or signs of relevant psychiatric or neurological disease; and (3) no cognitive impairment.

Fasting venous blood samples were collected from all subjects into ethylenediaminetetraacetic acid-containing tubes. PBMCs were isolated using Ficoll-Hypaque density gradient centrifugation as described [32].

RNA isolation and quantitative real-time PCR (qRT-PCR)

Total RNA was isolated from PBMCs and purified using TRIzol reagent (cat# 15596018, Invitrogen, Carlsbad, CA, USA). Sample quality and RNA amount were examined using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). For inclusion in the experiments, the absorbance ratio A₂₆₀/A₂₈₀ of RNA had to be 1.8-2.0, in which case 1 µg was reverse-transcribed into cDNA using the SuperScript III First-Strand Kit (cat# 18080051, Invitrogen). The cDNA was diluted two-fold in RNAase-free ddH₂O, then used as template (1 µL) in qRT-PCR with the ChamQ™ Universal SYBR qPCR Master Mix (cat# Q711-02, Vazyme, Nanjing, Jiangsu, China) and primers designed using the online tool https://www.ncbi.nlm.nih.gov/tools/primer-blast/ (Supplementary Table 4). The 2^−ΔΔCt method was used to measure mRNA levels relative to GAPDH [33]. Only genes with transcript Ct ≤ 30 were considered to be expressed.

Quantification of m6A methylation

The level of m6A-modified RNA in 1000 ng total RNA from PBMCs was measured using a commercial colorimetric kit (cat# ab185912, Abcam, Cambridge, MA, USA), according to the manufacturer’s instructions. Absorbance was measured at 450 nm, which was converted to m6A levels through a standard curve.

Western blot analysis

Total protein was isolated from PBMCs of AD patients and controls using RIPA lysis buffer (Beyotime Biotechnology, Nanjing, China) containing phenylmethylsulfonyl fluoride (Beyotime Biotechnology). Lysates were left undisturbed for 30 min and then were centrifuged at 12,000 × g for 20 min at 4°C. Total protein concentration in the supernatant was estimated using a bicinchoninic acid assay (Beyotime Biotechnology), and equal amounts of protein (30 µg) were separated by electrophoresis on precast 10% Bis-Tris Gels (Bio-Rad, Laboratories, Hercules, CA, USA) and transferred to polyvinylidene difluoride membranes (Millipore, Billerica, MA, USA). The membranes were incubated for 16 h at 4°C with rabbit primary antibodies against the following proteins: METTL3 (1:1,000, cat# ab195352, Abcam), METTL14 (1:1,000, cat# A8530, ABclonal, Wuhan, Hubei, China), WTAP (1:1,000, cat# 56501, Cell Signaling Technology, Danvers, MA, USA), ALKBH5 (1:1,000, cat# ab195377, Abcam), FTO (1:1,000, cat# ab124892, Abcam), and β-actin (1:50,000, cat# AC026, ABclonal). After washing with TBST, membranes were incubated with horseradish peroxidase-conjugated anti-rabbit IgG (1:5,000, cat# ab6721, Abcam). Antibody binding was revealed using the ECL kit (cat# 32106, Thermo Fisher Scientific, Waltham, MA, USA), images were acquired using a Tanon 5200 imaging analysis system (Tanon Science & Technology, Shanghai, China), and bands were quantified using Image J (version 1.52v, US National Institutes of Health, Bethesda, MD, USA).

Enzyme-linked immunosorbent assay (ELISA)

Plasma was extracted from venous blood by centrifuging at 3,000 × g for 20 min, divided into 1-mL aliquots, and frozen at -80°C until analysis. The plasma was assayed for the following proteins using commercial ELISAs (Meimian, Yancheng, Jiangsu, China): METTL3 (cat# MM-0395H1), METTL14 (cat# MM-0395H2), WTAP (cat# MM-48756R1), ALKBH5 (cat# MM-2545E1) and FTO (cat# MM-51110H2). Absorbance was measured at 450 nm, and values were converted to relative proteins level using a standard curve.

Methylated RNA immunoprecipitation-sequencing (MeRIP-Seq)

Since MeRIP-Seq required at least 100 µg RNA for each sample, the RNAs of ten human PBMCs were pooled as one sample for MeRIP-Seq, and there were three samples for control and AD groups, respectively. RNA was isolated as described. Adequate RNA quality was defined as an RNA integrity number > 7.0 using a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) and confirmed by electrophoresis with a denaturing agarose gel.

Poly(A) RNA was purified from 50 µg total RNA using Dynabeads Oligo (dT) 25 (cat# 61005, Thermo Fisher Scientific), fragmented into small pieces at 86°C for 7 min using the Magnesium RNA Fragmentation Module (cat# e6150, New England Biolabs, Ipswich, MA, USA), incubated at 4°C for 2 h with an anti-m6A antibody (cat# 202003, Synaptic Systems, Göttingen, Niedersachsen, Germany) in 50 mM Tris-HCl, 750 mM NaCl, and 0.5% Igepal CA-630. Immunoprecipitated RNA was reverse-transcribed to cDNA by SuperScript™ II Reverse Transcriptase (cat# 1896649, Invitrogen), which was used to synthesize U-labeled second-stranded DNA with Escherichia coli DNA polymerase I (cat# m0209, New England Biolabs), RNase H (cat# m0297, New England Biolabs), and dUTP (cat# R0133, Thermo Fisher Scientific). An A-base was then added to the blunt ends of each strand in order to prepare them for ligation to the indexed adapters. Each adapter contained a T-base end for ligation to A-tailed fragmented DNA. Single- or dual-index adapters were ligated to the fragments, and size selection was performed with AMPureXP beads. After treatment with a heat-labile UDG enzyme (cat# m0280, New England Biolabs), the ligated products were amplified by PCR under the following conditions: initial denaturation at 95°C for 3 min; eight cycles of denaturation at 98°C for 15 sec, annealing at 60°C for 15 sec, and extension at 72°C for 30 sec; then final extension at 72°C for 5 min. The average insert size for the final cDNA library was 300 ± 50 bp. We then performed 2 × 150 bp paired-end sequencing (PE150) on a Novaseq™ 6000 (Illumina, San Diego, CA, USA).

Bioinformatic analyses

RNA sequencing data were cleaned up using Fastp software (https://github.com/OpenGene/fastp) [34] with default parameters in order to remove adapter contamination and low-quality reads, defined as those in which bases with Q ≤ 10 accounted for more than 20% of total reads. The sequence quality of the input and immunoprecipitated samples was also verified using fastp. HISAT2 (http://daehwankimlab.github.io/hisat2) [35] was used to map the reads to the reference genome Homo sapiens (version 101). Mapped reads of immunoprecipitated and input libraries were analyzed using the exomePeak package in R (https://bioconductor.org/packages/exomePeak) [36], which identified m6A peaks in bedGraph or bigWig formats. The output was visualized using IGV software (http://www.igv.org) [37]. MEME (http://meme-suite.org) [38] and HOMER (http://homer.ucsd.edu/homer/motif) were used to identify de novo and known motifs, followed by localization of the motif with respect to the peak summit.

Peaks were annotated based on intersection with gene architecture using the ChIPseeker package in R (https://bioconductor.org/packages/ChIPseeker) [39]. StringTie (https://ccb.jhu.edu/software/stringtie) was used to determine the expression levels of all mRNAs from input libraries, in terms of fragments per kilobase per million (FPKM), calculated as total exon fragments/mapped reads (millions) × exon length (kB).

The mRNAs showing | fold change | ≥ 2 and P < 0.05 between AD patients and controls based on the edgeR package in R (https://bioconductor.org/packages/edgeR) were considered to be differentially expressed [40].

Statistical analysis

All statistical analyses were conducted using GraphPad Prism version 8.0 (Graphpad, San Diego, CA, USA). Data were presented as mean ± standard error of the mean (SEM). Differences between two groups were assessed using Student’s t test for independent samples. Differences in gene expression were expressed in terms of “fold change (FC)” and P values, which were adjusted using Benjamini-Hochberg correction (q-value or false discovery rate). Correlations between continuous variables were analyzed using partial correlation analysis that adjusted for other covariates (age, sex, and disease severity). Statistical results associated with P < 0.05 were considered significant.

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