Comprehensive Analysis of Long Non-Coding RNAs N4-Acetylcytidine in Alzheimer’s Disease Mice Model Using High-Throughput Sequencing

Abstract

Background:

N4-acetylcytidine (ac4C), an important posttranscriptional modification, is involved in various disease processes. Long noncoding RNAs (lncRNAs) regulate gene expression mainly through epigenetic modification, transcription, and posttranscriptional modification. Alzheimer’s disease (AD) is a neurodegenerative disease characterized by amyloidosis of the brain. However, the role of lncRNA ac4C modification in AD remains unclear.

Objective:

In this study, we investigated the association between ac4C modification and AD, and the underlying mechanisms of ac4C modification in AD.

Methods:

The male 9-month-old APP/PS1 double transgenic mice, age- and sex-matched wild type (WT) mice were used in this study. Then, ac4C-RIP-seq and RNA-seq were used to comprehensively analyze lncRNA ac4C modification in AD mice. The lncRNA-miRNA-mRNA regulatory networks using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed the regulatory relationships among these three lncRNAs and AD.

Results:

The results showed that there were 120 significantly different ac4C peaks located on 102 lncRNAs in AD, of which 55 were hyperacetylated and 47 were hypoacetylated. Simultaneously, 231 differentially expressed lncRNAs were identified, including 138 upregulated lncRNAs and 93 downregulated lncRNAs. Moreover, 3 lncRNAs, lncRNA Gm26508, lncRNA A430046D13Rik, and lncRNA 9530059O14Rik, showed significant changes in both the ac4C and RNA levels using conjoint analysis.

Conclusion:

The abundance of lncRNA ac4C modification is significantly different in AD and indicates that lncRNA ac4C is associated with the occurrence and development of AD, which could provide a basis for further exploration of the related regulatory mechanisms.

Keywords

ac4C acetylation Alzheimer’s disease high-throughput sequencing lncRNAs

INTRODUCTION

An increasing number of studies have indicated that posttranscriptional chemical modifications of RNAs such as N4-acetylcytidine (ac4C), N6-methyladenosine (m6A), and 5-methylcytosine (m5C) serve an important function in regulating gene expression, and these are expected to become a new direction for the development of the epistemological transcriptome [1-3]. ac4C is a highly conserved RNA modification and was the first acetylation event described in mRNA [4]. N-acetyltransferase 10 (NAT10) has been reported to catalyze ac4C in coding sequences, ablation of NAT10 reduced detection of ac4C at the mapped mRNA sites and was associated with downregulation of target mRNA [5]. In addition, ac4C has been demonstrated to influence RNA stability and promote protein translation efficiency in eukaryotes and prokaryotes [6]. Meanwhile, it has been reported that ac4C is involved in the occurrence of various diseases, including inflammation, metabolic diseases, and autoimmune diseases [7]. lncRNA is a type of RNA without a protein coding function that is greater than 200 nucleotides in length [8]. Recent studies have shown that lncRNAs participate in the pathological processes of some human diseases, including nervous system diseases, immune system disorders, and the cardiovascular system diseases [9 –11].

Alzheimer’s disease (AD) is the most common central nervous system disease and is clinically characterized by language deterioration, progressive cognitive dysfunction, and memory loss [12, 13]. AD is currently the most common form of dementia, affecting more than 45 million people worldwide, and it is very common among elderly individuals with an incidence of 60% to 80% [14 –16]. Deposition of amyloid-β (Aβ) and tau hyperphosphorylation are both the major causes of neuronal loss in the brain and the most common neuropathological hallmarks of AD [17, 18]. There is growing evidence that lncRNAs play a vital role in the pathogenesis of AD [19]. The abundant lncRNAs of bearing miRNA-complementary sites can regulate gene expression as competitive endogenous RNAs or ‘sponges’ of miRNAs [20, 21]. It has been found that lncRNA MAGI2-AS3 alleviates amyloid-β-induced neurotoxicity and neuroinflammation by sponging miR-374b-5p [22]. A study suggested that the lncRNA BACE1-AS is upregulated in the brain of AD patients and can be used as a biomarker of different pathologies [23]. Another study indicated that the siRNA-mediated silencing of lncRNA BACE1-AS expression to attenuate β-secretase-1 (BACE1) to cleave amyloid-β protein precursor (AβPP) and to reduce the production of Aβ₁₄₂ oligomers [24]. In addition, the expression of BACE1 mRNA is controlled by regulatory non-coding RNA, which may drive AD-associated pathophysiology [25]. Research has demonstrated that silencing of BACE1-AS alleviated neuronal injury via regulating autophagy through the miR-214-3p/ATG5 signaling axis in AD [26]. However, ac4C modification of lncRNAs has rarely been reported in AD.

The aim of the present study was to establish the expression profiles of ac4C modification of lncRNAs. Therefore, we identified unique ac4C binding sites, analyzed the role of ac4C modification in the expression patterns of lncRNAs, and screened differentially expressed lncRNAs for both ac4C acetylation and RNA expression levels using ac4C RNA immunoprecipitation sequencing (ac4C-RIP-seq) (Fig. 1). In conclusion, our findings illustrate the specific ac4C acetylation status of lncRNAs in the pathophysiological processes of AD, which provides a scientific basis for further research on the function of lncRNAs and indicates that lncRNA acetylation is a potential therapeutic target.

Fig. 1

A flow chart of acetylated ac4C-modified lncRNAs in the cerebral cortex of AD mice. First, total RNA from the cerebral cortex was extracted, and ac4C immunoprecipitation magnetic beads were used to enrich ac4C acetylation. Second, differential ac4c-acetylated lncRNA modification and RNA expression were screened by sequencing. Finally, lncRNAs with both ac4C modification and differences in RNA levels were screened for bioinformatics analysis.

MATERIALS AND METHODS

Animals

APP/PS1 double transgenic mice were purchased from the Model Animal Research Center of Nanjing University and were bred in the animal center of Anhui Medical University. The experimental scheme was approved by the Animal Ethics Committee of Anhui Medical University (LLSC20210753).

Histopathological analysis

Three male 9-month-old APP/PS1 mice were used for this experiment, which confirmed that the AD model was successfully established by our research group [27]. And three age- and sex-matched wild type (WT) mice were used as control group. The mice were sacrificed by cervical dislocation under anesthesia with sodium pentobarbital (50 mg/kg) by intraperitoneal injection. The cerebral cortex was then removed, and thioflavin-S staining, transferase- mediated dUTP nick- end labelling (TUNEL) staining, and Hematoxylin-Eosin (H&E) staining were performed to observe pathological features under the microscope.

Acetylated RNA immunoprecipitation sequencing (ac4C-RIP-Seq)

ac4C-RIP-Seq was commissioned by Shanghai Biotechnology Corporation (Shanghai, China). Briefly, using TRIzol reagent (Invitrogentrademark, cat. no15596018), the total RNA was isolated and purified by high-frequency reciprocating vibration of grinding beads at low temperature. The ac4C antibody then reacted with modified sites on RNA by immunoprecipitation. The immunoprecipitated RNA was recovered, and an EpiTM mini Longrna-SEQ kit was used to prepare the library. A Bioptic Qsep100 Analyser (Bioptic Inc., Taiwan, China) was then used for quality inspection of the library. Library sequencing was performed on a Nova-Seq high-throughput sequencing platform in PE150 mode.

Sequencing data processing

Raw data or raw reads were obtained from the original image files by high-throughput sequencing after base recognition and error filtering. FastQC (v.0.10.1) was used to analyze the quality of sequencing data, base content distribution, and sequencing fragment proportion information. The filtered clean reads were then compared with the reference genome of the corresponding species by HISAT2 aligner (v2.1.0), and the unique mapped reads were obtained for further analysis. The purpose of clean read alignment on the genome was to determine the enrichment of these short sequences in the genome using exomePeak (v2.16.0) for PeakCalling. HOMER (http://homer.ucsd.edu/homer/ngs/peakMotifs.html) [28] software was then used for motif analysis of the peaks. For HOMER analysis, identified ac4C peaks which p value < 0.05 were chosen for the de novo motif analysis using homer (v4.10.4) under parameters: “-len 12 -rna”. The differential analysis was screened with the exomePeak R package, and the significant difference filter conditions were a p value < 0.05 and a | fold change |>2.

Prediction of target genes, construction of networks, and GO and KEGG analyses

First, the targeted miRNAs of lncRNAs with significantly differential ac4C acetylation and expression levels were predicted using the StarBase (https://starbase.sysu.edu.cn/) [29] and Rnainter databases (http://rnainter.org/) [30]. Next, based on the confidence score [31], the top 10 miRNAs were selected to predict the targeted mRNAs in the TargetScan database (http://www.targetscan.org/vert71/) [32], and the top 50 mRNAs were selected based on the total context++score [33]. Subsequently, the constructed network files were imported into Cytoscape 3.7.2, and the lncRNA-miRNA–mRNA network diagram was drawn.

Based on the lncRNA-miRNA–mRNA regulatory network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using OmicShare tools (http://www.omicshare.com/tools) [34]. The top 20 GO terms and KEGG pathways were represented in bicyclic graphs.

Statistical analysis

SPSS 23.0 software was used for the statistical analysis, and the data are expressed as the mean±standard deviation (SD). Paired Student’s t tests were used to detect significant differences between two groups, and a one-way ANOVA was used for multiple comparisons. The results were considered significant when the p value was < 0.05.

RESULTS

Determination of the AD model

Aβ deposition in the cerebral cortex of mice is a neuropathological feature of AD. A green fluorescence represents positive expression of Aβ, as shown by thioflavin-S staining. In comparison with the control group, Aβ deposition was markedly increased in the model group (Fig. 2A). The apoptosis of neurons in the cerebral cortex was detected by TUNEL staining. The results showed that the apoptosis of neurons in the control group was less than that in the model group (Fig. 2B). HE staining showed that the morphology of nerve cells was normal, and the structure was clear and arranged neatly in the control group, whereas in the model group, pyknosis of neuronal nuclei, disordered arrangement of nerve cells, and loose and edematous tissue were observed. Based on the pathological results of the above experiments, the AD model was successfully established (Fig. 2C).

Fig. 2

Pathological characteristics of the cerebral cortex in AD mice. A) Thioflavin-S staining in the cerebral cortices of AD mice (×400). B) TUNEL staining of cerebral cortices (×400). C). HE staining of cerebral cortices (×400).

ac4C acetylated modification of lncRNAs in AD

A principal component analysis (PCA) is an automated technique used to simplify data analysis [35]. In the PCA score plots (Fig. 3A), the samples from each group were completely separate, indicating that there were significant differences between the control and model groups. In Fig. 3B, a total of 4,958 ac4C peaks were distributed across the 184 lncRNAs in the control group, and 16,561 ac4C peaks were distributed across the 8,833 lncRNAs in the model group. Among them, 4,745 ac4C peaks and 171 lncRNAs were shared by both groups. Compared with the control group, 11,816 ac4C peaks and 8,662 ac4C acetylation modifier lncRNAs appeared, and 213 ac4C peaks and 13 ac4C acetylation modifier lncRNAs disappeared in the model group.

Fig. 3

Study of acetylated ac4c lncRNAs in AD mice. A) Principal component analysis of ac4C acetylation in each group. B) Venn diagram of ac4C peaks and ac4C acetylated lncRNAs. C) The most conserved sequence motif of ac4C peaks. D) The number of ac4C peaks per lncRNA in the control group. E) The number of ac4C peaks per lncRNA in the model group. F) The distribution of the ac4C peaks on different chromosomes in the control group. G) The distribution of the ac4C peaks on different chromosomes in the model group.

Next, based on the ac4C modification of lncRNAs, we analyzed the ac4C motif in AD. As expected, this modification mainly occurs on cytosine, and the most significant ac4C acetylation occurred at the CXXCXX motifs, in which C represents cytosine and X represents any base except C, i.e., A, U, or G (Fig. 3C).

We then examined the number of ac4C peaks in each lncRNA and found that there were more lncRNAs at one ac4C acetylation site in both the control and model groups (Fig. 3D, E). We then analyzed the distributions of the ac4C peaks that were mapped on different chromosomes, and ac4C acetylation was mainly distributed on chromosome 1, chromosome 2, and chromosome 3 (Fig. 3F, G).

Differentially ac4C-acetylated lncRNAs in AD

Based on a p value < 0.05 and |fold change|>2, 120 differentially acetylated ac4C peaks were distributed on 102 lncRNAs, which included 55 that were hyperacetylated and 47 that were hypoacetylated (Fig. 4A). Figure 4B shows the gene length of hyperacetylated and hypoacetylated ac4C peaks of lncRNAs; whether acetylation was high or low, the length of ac4C peaks is generally < 200 bp.

Fig. 4

Description of differential ac4C acetylation of lncRNAs between the model and control groups. A) The number of differential peaks and the corresponding lncRNAs. B) The length of differentially acetylated ac4C peaks. C) Positions of differentially acetylated ac4C lncRNAs on chromosomes. D) Volcano plots show the top 10 hyperacetylated lncRNAs and the top 10 hypoacetylated lncRNAs. Blue points represent significantly hypoacetylated lncRNAs, and red points represent significantly hyperacetylated lncRNAs. E) Hierarchical clustering shows the top 10 hyperacetylated lncRNAs and the top 10 hypoacetylated lncRNAs. F) Radar plot analysis of the top 10 hyperacetylated lncRNAs and the top 10 hypoacetylated lncRNAs.

By analyzing the enrichment level of ac4C-acetylated lncRNAs on each chromosome, we concluded that ac4c acetylation was mainly distributed on chromosome 2 with 14 ac4c-acetylated lncRNAs, followed by chromosome 4 with 11 ac4c-acetylated lncRNAs (Fig. 4C). The top 10 hyperacetylated lncRNAs, including Gm15648, Gm17134, and Gm12992, and the top 10 hypoacetylated lncRNAs, including 4933431E20Rik, Gm26840, and Gm15873, were then identified and visually displayed in volcano plots, hierarchical clustering, and radar plots (Fig. 4D–F). Detailed information on these ac4C-acetylated lncRNAs is shown in Table 1.

Table 1

The top 10 hyperacetylated and hypoacetylated lncRNAs

Gene	ID	Chromosome	Start	End	p	log₂FC	Hyper/Hypo
Gm15648	ENSMUSG00000085425	16	17153257	17153704	3.16E-49	9.21	Hyper
Gm17134	ENSMUSG00000090860	10	79863936	79864172	0.000331	7.54	Hyper
Gm12992	ENSMUSG00000085667	4	1.32E+08	1.32E+08	5.01E-18	7.28	Hyper
5031415H12Rik	ENSMUSG00000097282	17	70751448	70751598	3.16E-13	7.27	Hyper
Gm26778	ENSMUSG00000097142	14	1.03E+08	1.03E+08	1E-253	7.09	Hyper
4933421O10Rik	ENSMUSG00000085037	4	33027886	33028066	8.91E-05	6.93	Hyper
Gm20488	ENSMUSG00000107314	5	1.49E+08	1.49E+08	1.55E-09	6.43	Hyper
Gm14377	ENSMUSG00000109501	7	47124733	47124914	4.68E-06	6.32	Hyper
BC037039	ENSMUSG00000103309	18	37806968	37807148	3.16E-16	6.14	Hyper
4930488L21Rik	ENSMUSG00000097520	8	93797342	93808867	1.82E-06	6.06	Hyper
4933431E20Rik	ENSMUSG00000086968	3	1.08E+08	1.08E+08	2E-25	–6.76	Hypo
Gm26840	ENSMUSG00000097728	4	1.56E+08	1.56E+08	3.24E-08	–6.75	Hypo
Gm15873	ENSMUSG00000085725	7	4526891	4530575	0.002884	–6.39	Hypo
Chn1os3	ENSMUSG00000086544	2	73612458	73613263	1.26E-40	–6.30	Hypo
4930599N23Rik	ENSMUSG00000073144	6	39146943	39148136	3.98E-14	–5.91	Hypo
Meg3	ENSMUSG00000021268	12	1.1E+08	1.1E+08	6.17E-06	–5.56	Hypo
Gm26702	ENSMUSG00000097872	2	25578416	25578626	1.26E-26	–5.56	Hypo
Crybg3	ENSMUSG00000091562	16	59559118	59559298	0.0002	–5.39	Hypo
Dlx6os1	ENSMUSG00000090063	6	6820726	6820907	1.35E-07	–5.36	Hypo
Gm14455	ENSMUSG00000087633	2	1.72E+08	1.72E+08	0.031623	–5.32	Hypo

Analysis of differentially expressed lncRNAs in AD

The violin diagram represents the el probability density and range of the data [36]. The violin diagram (Fig. 5A) shows the results of the analysis of lncRNA enrichment degree in each sample. In the control group, the fold-enrichment average value was 5.85, the fold-enrichment average value of the model group was 7.77. The PCA illustrates the significant differences between the control and model groups (Fig. 5B). Next, 231 differentially expressed lncRNAs, including 138 upregulated lncRNAs and 93 downregulated lncRNAs in AD, were obtained based on the criteria of a p value < 0.05 and a |fold change|>2 (Fig. 5C). Simultaneously, the top 10 hyperacetylated lncRNAs (e.g., AC153971.1, AC133079.1, Gm37277, etc.) and the top 10 hypoacetylated lncRNAs (e.g., Gm14260, Gm42849, and AC144772.1.) are visually displayed in volcano plots, hierarchical clustering, and radar plots (Fig. 5D–F). Detailed information on these differentially expressed lncRNAs is shown in Table 2.

Fig. 5

Overall expression of lncRNAs and analysis of differentially expressed lncRNAs in AD. A) Violin plot indicating the abundance of lncRNA expression in the control and model groups. B) Principal component analysis of lncRNA expression in each group. C) The number of differentially expressed lncRNAs. D) Volcano plots displaying the 10 upregulated lncRNAs and the top 10 downregulated lncRNAs. Blue points represent significantly upregulated lncRNAs, and red points represent significantly downregulated lncRNAs. E) Hierarchical clustering analysis of the top 10 upregulated lncRNAs and the top 10 downregulated lncRNAs. F) Radar plot analysis of the top 10 upregulated lncRNAs and the top 10 downregulated lncRNAs.

Table 2

The top 10 upregulated and downregulated lncRNAs

Gene	ID	Chromosome	Start	End	p	log2FC	Up/Down
AC153971.1	ENSMUSG00000110894	10	3E+07	30619958	0.039778	8.04	Up
AC133079.1	ENSMUSG00000113588	12	6E+07	61801212	0.040608	8.00	Up
Gm37277	ENSMUSG00000103280	1	5E+06	4977084	1.01E-06	7.99	Up
Gm42591	ENSMUSG00000105500	5	1E+08	1.32E+08	0.000132	7.82	Up
BC030343	ENSMUSG00000105345	5	1E+08	1.43E+08	0.046742	7.77	Up
9530085L11Rik	ENSMUSG00000107742	8	1E+07	13337466	0.048332	7.72	Up
4933412O06Rik	ENSMUSG00000113960	13	2E+07	15977215	0.000437	7.56	Up
Gm33280	ENSMUSG00000102202	1	4E+07	36177827	0.001464	7.48	Up
AC120375.1	ENSMUSG00000115210	14	3E+07	31476371	0.001213	7.46	Up
AC132474.2	ENSMUSG00000111271	9	4E+07	40254234	0.001013	7.40	Up
Gm14260	ENSMUSG00000086658	2	2E+08	1.69E+08	0.034154	–8.28	Down
Gm42849	ENSMUSG00000107043	5	3E+07	34106176	8.98E-06	–8.21	Down
AC144772.1	ENSMUSG00000115340	15	4E+07	35857937	2.60E-05	–8.19	Down
AC121942.3	ENSMUSG00000114746	13	4E+07	36767500	0.000112	–7.88	Down
Gm43544	ENSMUSG00000105936	1	1E+08	1.39E+08	0.04459	–7.85	Down
Gm44573	ENSMUSG00000104150	7	4E+07	39194330	0.002838	–7.62	Down
Gm45170	ENSMUSG00000108318	7	8E+07	79253424	0.002809	–7.60	Down
D930030I03Rik	ENSMUSG00000097576	16	4E+07	38273856	0.002873	–7.57	Down
Gm42513	ENSMUSG00000105065	3	6E+07	57989255	0.002898	–7.57	Down
Gm2453	ENSMUSG00000102909	1	2E+08	1.67E+08	0.004083	–7.48	Down

Joint analysis of ac4C-seq and RNA-seq

To identify the lncRNAs that commonly had significant differential ac4C acetylation and expression levels, we performed a conjunction analysis for ac4C-seq and RNA-seq. The cumulative curve shows the correlation between the lncRNA acetylation level and expression abundance (Fig. 6A). A Venn diagram shows 3 lncRNAs (lncRNA Gm26508, lncRNA A430046D13Rik, and lncRNA 9530059O14Rik) that were screened (Fig. 6B). As shown in Fig. 6C, lncRNA Gm26508 and lncRNA A430046D13Rik were hyperacetylated and had downregulated RNA levels, whereas lncRNA 9530059O14Rik was hypoacetylated and had upregulated RNA levels. Detailed information on these lncRNAs is shown in Table 3.

Fig. 6

Conjoint analysis of ac4C-seq and RNA-seq in AD mice. A) Cumulative frequency plot of ac4c-acetylated lncRNA levels and RNA levels of lncRNAs. B) Venn diagram indicating the association between differential ac4c-acetylated modification and lncRNA expression. C) Four quadrant graph analysis of differential acetylation and differential RNA expression.

Table 3

LncRNA differential ac4C acetylation levels and RNA levels

Gene	ID	Log2FC-ac4C	p-value ac4C	Log2FC-lncRNA	p-value lncRNA	Regulation
Gm26508	ENSMUSG00000097614	5.71	4.57E-06	–1.03	0.01	Hyper-down
A430046D13Rik	ENSMUSG00000097353	–2.81	0.03	–2.92	0.04	Hypo-down
9530059O14Rik	ENSMUSG00000097736	1.27	0.01	4.24	0.01	Hyper-up

Construction and GO/KEGG analysis of the lncRNA Gm26508-miRNA–mRNA network

To further investigate the biological functions of lncRNA Gm26508, we constructed an interactive network diagram (Fig. 7A). In addition, we performed GO and KEGG enrichment analyses on these genes. The GO enrichment analysis showed that 781 terms were significantly enriched in biological processes (BP), 132 terms were enriched in cellular components (CC), and 143 terms were enriched in molecular functions (MF). Of these, the genes were primarily enriched in the regulation of cell communication, intracellular parts, protein binding, etc. In addition, a total of 216 pathways were enriched. Of these, 8 were significantly enriched, including the transforming growth factor-β (TGF-beta) signaling pathway, SNARE interactions in vesicular transport, glutamatergic synapse, etc. Figure 7B–E shows the top 20 terms of GO, including BP, CC, MF, and KEGG pathways. In addition, the complete results of GO and KEGG pathway analyses are listed (Supplementary Table 1).

Fig. 7

Functional analysis of lncRNA Gm26508. A) lncRNA Gm26508-miRNA–mRNA network. B) The top 20 enriched biological processes of GO terms. C) The top 20 enriched cellular components of GO terms. D) The top 20 enriched molecular functions of GO terms. E) The top 20 enriched KEGG pathways.

Construction and GO/KEGG analysis of the lncRNA A430046D13Rik-miRNA–mRNA network

To visualize the relationship between lncRNA A430046D13Rik and the predicted genes, we constructed the lncRNA A430046D13Rik-miRNA–mRNA network (Fig. 8A). To further explore the functions and pathways of these genes, GO and KEGG enrichment analyses were performed. The GO enrichment analysis showed that 1,345 terms were significantly enriched in BP, 187 terms were enriched in CC, and 223 terms were enriched in MF. Particularly enriched functions included positive regulation of cellular processes, intracellular parts, protein binding, etc. Subsequently, a KEGG enrichment analysis indicated that a total of 246 pathways were enriched, including 22 that were significantly enriched such as RNA degradation, protein export, ubiquitin mediated proteolysis, etc. The top 20 GO terms (BP, CC, MF) and KEGG pathways are displayed in Fig. 8B–E. The completes information on the GO and KEGG pathway analyses are shown in Supplementary Table 2.

Fig. 8

Functional analysis of lncRNA A430046D13Rik. A) lncRNA A430046D13Rik-miRNA–mRNA network. B) The top 20 enriched biological processes of GO terms. C) The top 20 enriched cellular components of GO terms. D) The top 20 enriched molecular functions of GO terms. E) The top 20 enriched KEGG pathways.

Construction and GO KEGG analysis of the lncRNA 9530059O14Rik-miRNA–mRNA network

To fully explore the functions of lncRNA 9530059O14Rik, we performed an association analysis of lncRNA 9530059O14Rik (Fig. 9A). Additionally, a GO analysis and a KEGG pathway enrichment analysis were performed for these genes. The GO enrichment analysis showed that 1030 terms were significantly enriched in BP, 97 terms were enriched in CC, and 168 terms were enriched in MF; particularly enriched functions included positive regulation of RNA metabolic processes, the nucleus, sequence-specific DNA binding, etc. Similarly, the KEGG enrichment analysis results indicated that a total of 237 pathways were enriched, of which 13 were significantly enriched, including the cholinergic synapse, the dopaminergic synapse, vascular smooth muscle contraction, etc. The top 20 terms enriched GO functions in the BP, CC, MF, and KEGG pathways are shown (Fig. 9B–E). Additionally, the specific information of GO term and KEGG pathway analyses are presented in Supplementary Table 3.

Fig. 9

Functional analysis of lncRNA 9530059O14Rik. A) lncRNA 9530059O14Rik-miRNA–mRNA network. B) The top 20 enriched biological processes of GO terms. C) The top 20 enriched cellular components of GO terms. D) The top 20 enriched molecular functions of GO terms. E) The top 20 enriched KEGG pathways.

DISCUSSION

ac4C is a highly conserved internal posttranscriptional and extensively occurring RNA modification that helps to correctly read the codon during the process of translation and improves the efficiency of translation [37]. lncRNAs are expressed in a variety of tissues and are involved in the pathophysiology of diseases [38]. lncRNAs have been reported to play an important role in plant growth and development, stress responses (biotic and abiotic), regulating cell differentiation, cell cycle and the occurrence of many human and animal diseases [39]. Some studies have reported that the histone modification and mediated gene expression of lncRNAs are related to the development and progression of AD [40, 41]. However, the acetylated ac4C modification of lncRNAs in AD has not been confirmed. In this study, APP/PS1 double transgenic mice were used to establish an AD model. The APP/PS1 double transgenic mice had double mutations in APP and PSEN1, in which neurogenesis was impaired in the early stages [42]. Research has shown that APP/PS1 double transgenic mice display significant memory deficits at 5 months of age, which are aggravated over the lifespan [43]. A recent study found that significant memory deficits and Aβ deposition were observed in 9-month-old APP/PS1 double transgenic mice, which is consistent with the features of AD [44]. Our pathological results also confirmed that 9-month-old APP/PS1 double transgenic mice had significant Aβ deposition and apoptosis of neurons in the cerebral cortex. These results indicated that the AD model have been successfully constructed.

It has already been indicated that the cerebral cortex is involved in cognitive control and information processing, which is the major region of the mammalian forebrain [45]. In particular, the cerebral cortex is typically characterized by multiple layers of neurons [46]. Using an RNA sequencing approach, it has been revealed that the abnormal expression profile of miRNA and mRNA contributes to AD in the cerebral cortex [47].

Using ac4C-seq to build libraries, we found that 4,958 ac4C peaks were distributed on 184 lncRNAs in the control group, and 16,561 ac4C peaks were located on 8,833 lncRNAs in the model group. This result may be related to the low initial amount of RNA in the sample, which affects the enrichment efficiency. Interestingly, the number of ac4C peaks was much larger than the number of lncRNAs, whether in the control group or the model group. This suggests that there were multiple ac4C acetylation sites for one lncRNA. Recent work also has suggested lncRNAs regulate chromatin remodeling, transcription, and posttranscriptional regulation of gene expression in the immune system [48]. ac4C has been shown to influence RNA stability, ribosome biogenesis and promote protein translation efficiency [49, 50]. Studies have also clearly demonstrated a critical role of interleukins in the progressive development of AD [51]. Multiple acetylation sites in the AD brain could also act on Kruppel-like factor 4 (KLF4) to enhance an immunological response and thus accelerating disease progression [52]. Therefore, we think is indeed an issue worthy of discussion, which provides a good direction for the next step of the research group.

“CXX” is a C-rich sequence characterized by several obligate cytidines separated by two nonobligate nucleotides, and repeating motifs such as CXXCXX were reproducibly observed in the transcriptome [53]. Within these motifs, C represents cytosine, and X represents any base except C, i.e., A, U, or G. We also found the repeating CXX motif in lncRNAs of AD, which provided further evidence for ac4C modification of lncRNAs.

ac4C is the only acetylation modification that occurs at cytidine residues in eukaryotic RNA and affects RNA expression [54]. Through conjoint analysis, we identified three lncRNAs that were differentially expressed not only with regard to ac4C modification but also in RNA expression: lncRNA Gm26508, lncRNA A430046D13Rik, and lncRNA 9530059O14Rik. Interestingly, the lncRNA Gm26508 and lncRNA A430046D13Rik are hyperacetylated and have downregulated RNA levels, whereas lncRNA 9530059O14Rik is hypoacetylated and has upregulated RNA levels. This indicates that the interrelations of ac4C acetylation and RNA expression are complicated processes.

Because these three lncRNAs are considered novel genes, the mechanism by which they participate in AD development has not been reported in the literature. New information has suggested that lncRNAs can act as miRNA sponges to affect target mRNA expression [55]. Thus, it has been proven that an effective method for deciphering the biological function of lncRNAs is to investigate their relationship with miRNAs and/or mRNAs. To further understand the functions of the above three lncRNAs, we constructed ceRNA networks and performed a bioinformatics analysis. The GO and KEGG analysis identified the molecules involved in neurodegeneration, namely TGF-beta and apolipoprotein binding. We identified the novel ac4C function in lncRNAs that played an important role in AD and was associated with the prognosis of AD patients.

We determined that lncRNA Gm26508 was mainly enriched in the regulation of cell communication and modulation of chemical synaptic transmission. Synaptic transmission activity can be detected by astrocytes, which release chemical transmitters to affect neuronal function [56]. In addition, lncRNA Gm26508 was related to the mitogen-activated protein kinase (MAPK) signaling pathway and TGF-beta signaling pathway, among others. These two signaling pathways are classic regulatory pathways in AD [57]. Extracellular signal-regulated protein kinase 1/2 (ERK1/2) is a member of MAPK family [58]. ERK1/2 signaling pathway is related to neuronal survival and synaptic transmission and has neuroprotective effect when activated [59]. In addition, studies have shown that the deficiency of TGF-β1 signaling pathway increased both Aβ accumulation and Aβ-induced neurodegeneration in AD models [60]. After activation, the pathway exerts an anti-AD effect by modulating synaptic transmission [61]. lncRNA A430046D13Rik and lncRNA 9530059O14Rik mainly participate in biological processes such as the regulation of RNA metabolic processes and biosynthetic processes. In addition, the C-type lectin receptor signaling pathway, oxytocin signaling pathway, and chemokine signaling pathway were the main enriched pathways. These pathways are involved in the regulation of neuroinflammation in AD [62 –64]. Among them, the C-type lectin receptor can induce the activation of nuclear NF-κB in innate immunity and inflammation [65]. In addition, ac4C has been demonstrated to excite neuroglia and induce cell signaling to maintain an inflammatory response, and the increase of IL-1β via oxidative stress was associated with all-cause mortality in elder populations [66, 67].

Conclusion

In this study, our research identified the lncRNAs of the ac4C transcriptome profile and provided a global landscape of the potential relationship between ac4C acetylation and AD. Through conjoint analysis of differentially acetylated lncRNAs and differentially expressed lncRNAs, three lncRNAs were identified: lncRNA Gm26508, lncRNA A430046D13Rik, and lncRNA 9530059O14Rik. Our study provides valuable information on ac4C modification in AD. Our results provide novel insight into the association between ac4C modification and AD, which should be useful for clarifying the underlying mechanisms of ac4C modification in AD.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the Major Science and Technology Project of Anhui Province (2019): Preclinical study of Xinan special preparation Zhiniao Capsule in the treatment of Alzheimer’s disease (No.201903a07020016), The University Synergy Innovation Program of Anhui Province (No. GXXT-2020-025).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

References

Mathoux

, Henshall

, Brennan

(2021) Regulatory mechanisms of the RNA modification m6A and significance in brain function in health and disease. Front Cell Neurosci 15, 671932.

Thomas

, Bryson

, Meier

(2019) Nucleotide resolution sequencing of N4-acetylcytidine in RNA. Methods Enzymol 621, 31–51.

Ellison

, Abner

, Lovell

(2017) Multiregional analysis of global 5-methylcytosine and 5-hydroxymethylcytosine throughout the progression of Alzheimer’s disease. J Neurochem 140, 383–394.

Xiang

, Zhou

, Zeng

, Guo

, Huang

, Wu

, Liu

, Liang

, Zeng

, Liang

(2021) NAT10-mediated N4-acetylcytidine of RNA contributes to post-transcriptional regulation of mouse oocyte maturation. Front Cell Dev Biol 9, 704341.

Arango

, Sturgill

, Alhusaini

, Dillman

, Sweet

, Hanson

, Hosogane

, Sinclair

, Nanan

, Mandler

, Fox

, Zengeya

, Andresson

, Meier

, Coller

, Oberdoerffer

(2018) Acetylation of cytidine in mRNA promotes translation efficiency. Cell 175, 1824–1886e1824.

Karthiya

, Wasil

, Khandelia

(2020) Emerging role of N4-acetylcytidine modification of RNA in gene regulation and cellular functions. Mol Biol Rep 47, 9189–9199.

Jin

, Xu

, Zou

, Duan

(2020) The processing, gene regulation, biological functions, and clinical relevance of N4-acetylcytidine on RNA: A systematic review. Mol Ther Nucleic Acids 20, 13–24.

Wang

, Le

, Wei

, Cheng

(2021) CREB3 transactivates lncRNA ZFAS1 to promote papillary thyroid carcinoma metastasis by modulating miR-373-3p/MMP3 regulatory axis. Int J Endocrinol 2021, 9981683.

Wang

, Wang

, Zhang

, Xu

, Li

, Zhou

, Liu

, Cao

(2020) Decreased expression of the host long-noncoding RNA-GM facilitates viral escape by inhibiting the kinase activity TBK1 via S-glutathionylation. Immunity 53, 1168–1181e1167.

10.

Lan

, Chen

, Jin

, Xu

, Zhu

(2021) Long non-coding RNA: Insight into mechanisms of Alzheimer’s disease. Front Mol Neurosci 14, 821002.

11.

, Lin

, Gao

(2018) LncRNA TUG1 promotes proliferation of vascular smooth muscle cell and atherosclerosis through regulating miRNA-21/PTEN axis. Eur Rev Med Pharmacol Sci 22, 7439–7447.

12.

Pellegrini

, Rosso

, Chu

(2021) Gaetano Perusini: The forgotten neuroscientist behind “Alzheimer’s” disease. Neuroscientist, doi: 10.1177/10738584211059466.

13.

Parnetti

, Chipi

, Salvadori

, D’Andrea

, Eusebi

(2019) Prevalence and risk of progression of preclinical Alzheimer’s disease stages: A systematic review and meta-analysis. Alzheimers Res Ther 11, 7.

14.

Winblad

, Amouyel

, Andrieu

, Ballard

, Brayne

, Brodaty

, Cedazo-Minguez

, Dubois

, Edvardsson

, Feldman

, Fratiglioni

, Frisoni

, Gauthier

, Georges

, Graff

, Iqbal

, Jessen

, Johansson

, Jonsson

, Kivipelto

, Knapp

, Mangialasche

, Melis

, Nordberg

, Rikkert

, Qiu

, Sakmar

, Scheltens

, Schneider

, Sperling

, Tjernberg

, Waldemar

, Wimo

, Zetterberg

(2016) Defeating Alzheimer’s disease and other dementias: A priority for European science and society. Lancet Neurol 15, 455–532.

15.

Giorgi

, Ryskalin

, Ruffoli

, Biagioni

, Limanaqi

, Ferrucci

, Busceti

, Bonuccelli

, Fornai

(2017) The neuroanatomy of the reticular nucleus locus coeruleus in Alzheimer’s disease. Front Neuroanat 11, 80.

16.

, Huang

, Wang

, Li

, Hou

, Shen

, Ou

, Dong

, Tan

, Yu

, Initiative

ADN

(2019) Metabolically healthy obesity reduces the risk of Alzheimer’s disease in elders: A longitudinal study. Aging (Albany NY) 11, 10939–10951.

17.

Yang

, Onos

, Choi

, Keezer

, Skelly

, Carter

, Howell

(2021) Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer’s disease. Cell Rep 34, 108739.

18.

Yang

, Wise

, Fukuchi

(2020) TLR4 cross-talk with NLRP3 inflammasome and complement signaling pathways in Alzheimer’s disease. Front Immunol 11, 724.

19.

Garofalo

, Pandini

, Sproviero

, Pansarasa

, Cereda

, Gagliardi

(2021) Advances with long non-coding RNAs in Alzheimer’s disease as peripheral biomarker. Genes (Basel) 12, 1124.

20.

Ebert

, Sharp

(2010) MicroRNA sponges: Progress and possibilities. RNA 16, 2043–2050.

21.

Statello

, Guo

, Chen

, Huarte

(2021) Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 22, 96–118.

22.

Zhang

, Wang

(2021) Deregulated lncRNA MAGI2-AS3 in Alzheimer’s disease attenuates amyloid-beta induced neurotoxicity and neuroinflammation by sponging miR-374b-5p. Exp Gerontol 144, 111180.

23.

Fotuhi

, Khalaj-Kondori

, Hoseinpour

Feizi MA

, Talebi

(2019) Long non-coding RNA BACE1-AS may serve as an Alzheimer’s disease blood-based biomarker. J Mol Neurosci 69, 351–359.

24.

Liu

, Huang

, Chen

, Chi

, Yu

, Wang

, Chen

(2014) Attenuated ability of BACE1 to cleave the amyloid precursor protein via silencing long noncoding RNA BACE1AS expression. Mol Med Rep 10, 1275–1281.

25.

Faghihi

, Modarresi

, Khalil

, Wood

, Sahagan

, Morgan

, Finch

, St Laurent

, 3rd, Kenny

, Wahlestedt

(2008) Expression of a noncoding RNA is elevated in Alzheimer’s disease anddrives rapid feed-forward regulation of beta-secretase. NatMed 14, 723–730.

26.

Zhou

, Ge

, Liu

, Li

, Chao

, Guan

, Diwu

, Zhang

(2021) LncRNA BACE1-AS promotes autophagy-mediated neuronal damage through the miR-214-3p/ATG5 signalling axis in Alzheimer’s disease. Neuroscience 455, 52–64.

27.

Zhang

, Su

, Sun

, Chen

, Han

, Li

, Dong

, Ding

, Fang

, Li

(2021) Ginsenoside Rg1 alleviates Abeta deposition by inhibiting NADPH oxidase 2 activation in APP/PS1 mice. J Ginseng Res 45, 665–675.

28.

Heinz

, Benner

, Spann

, Bertolino

, Lin

, Laslo

, Cheng

, Murre

, Singh

, Glass

(2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38, 576–589.

29.

, Liu

, Zhou

, Qu

, Yang

(2014) starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42, D92–97.

30.

Zheng

, Wang

, Zheng

, Zhao

, Li

, Wang

, Xue

, Tian

, Xie

(2020) Construction and analysis of the tumor-specific mRNA-miRNA-lncRNA network in gastric cancer. Front Pharmacol 11, 1112.

31.

Inturi

, Mun

, Singethan

, Schreiner

, Punga

(2018) Human adenovirus infection causes cellular E3 ubiquitin ligase MKRN1 degradation involving the viral core protein pVII. J Virol 92, e01154–17.

32.

Chiang

, Schoenfeld

, Ruby

, Auyeung

, Spies

, Baek

, Johnston

, Russ

, Luo

, Babiarz

, Blelloch

, Schroth

, Nusbaum

, Bartel

(2010) Mammalian microRNAs: Experimentalevaluation of novel and previously annotated genes. Genes Dev 24, 992–1009.

33.

Agarwal

, Bell

, Nam

, Bartel

(2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005.

34.

Wang

, Su

, Zhong

, Yang

, Chen

, Liu

, Wu

, Zhong

, Li

, Mao

, Lu

(2020) An eight-CircRNA assessment model for predicting biochemical recurrence in prostate cancer. Front Cell Dev Biol 8, 599494.

35.

Jiang

, Liu

, Wang

, Gao

, Sun

, Huang

, Meng

, Qin

(2016) Urinary metabolite profiling provides potential differentiation to explore the mechanisms of adjuvant-induced arthritis in rats. Biomed Chromatogr 30, 1397–1405.

36.

Davies

, Belda

, Broncel

, Ye

, Bisson

, Introini

, Dorin-Semblat

, Semblat

, Tiburcio

, Gamain

, Kaforou

, TreeckM (2020) An exported kinase family mediates species-specificerythrocyte remodelling and virulence in human malaria. Nat Microbiol 5, 848–863.

37.

Wang

, Zhang

, Xie

, Zou

, Zhong

, Zheng

, Zhou

, Zheng

, Chen

, Liu

(2022) NAT10-mediated mRNA N4-acetylcytidine modification promotes bladder cancer progression. Clin Transl Med 12, e738.

38.

Zheng

, Shen

, Wei

, Wan

, Xie

, Yao

, Wang

(2021) Transcriptome sequencing reveals a lncRNA-mRNA interaction network in extramammary Paget’s disease. BMC Med Genomics 14, 291.

39.

Zhang

, Wang

, Zhu

, Dong

, Cheng

, Yin

, Shen

(2019) Mechanisms and functions of long non-coding RNAs at multiple regulatory levels. Int J Mol Sci 20, 5573.

40.

Wan

, Zhou

, Hu

, Ma

, Jin

, Liu

, Jiang

(2016) Transcriptional regulation of lncRNA genes by histone modification in Alzheimer’s disease. Biomed Res Int 2016, 3164238.

41.

Chatterjee

, Roy

(2016) Insight into the epigenetics of Alzheimer’s disease: A computational study from human interactome. Curr Alzheimer Res 13, 1385–1396.

42.

, Mak

, Zheng

, Xia

, Xu

, Duan

, Dong

, Li

, Zhan

, Shang

, Tsim

(2020) Shexiang Baoxin pill, a traditional Chinese herbal formula, rescues the cognitive impairments in APP/PS1 transgenic mice. Front Pharmacol 11, 1045.

43.

Zhu

, Wang

, Zhang

, He

, Kong

, Wang

, Li

(2017) The role of neuroinflammation and amyloid in cognitive impairment in an APP/PS1 transgenic mouse model of Alzheimer’s disease. CNS Neurosci Ther 23, 310–320.

44.

, Ren

, Yu

(2021) Anti-PrP monoclonal antibody as a novel treatment for neurogenesis in mouse model of Alzheimer’s disease. , e. Brain Behav 11, 2365.

45.

Natale

, Esteban Masferrer

, Deivasigamani

, Gross

(2021) A role for cerebral cortex in the suppression of innate defensive behaviour. Eur J Neurosci 54, 6044–6059.

46.

Rodin

, Dou

, Kwon

, Sherman

, D’Gama

, Doan

, Rento

, Girskis

, Bohrson

, Kim

, Nadig

, Luquette

, Gulhan

, Brain Somatic Mosaicism

, Park

, Walsh

(2021) The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat Neurosci 24, 176–185.

47.

Zeng

, Jiang

, Ashraf

, Li

, Liu

(2021) MicroRNA and mRNA profiling of cerebral cortex in a transgenic mouse model of Alzheimer’s disease by RNA sequencing. Neural Regen Res 16, 2099–2108.

48.

Mathy

, Chen

(2017) Long non-coding RNAs (lncRNAs) and their transcriptional control of inflammatory responses. J Biol Chem 292, 12375–12382.

49.

Sharma

, Langhendries

, Watzinger

, Kotter

, Entian

, Lafontaine

(2015) Yeast Kre33 and human NAT10 are conserved 18S rRNA cytosine acetyltransferases that modify tRNAs assisted by the adaptor Tan1/THUMPD1. Nucleic Acids Res 43, 2242–2258.

50.

Atmoko

, Raharja

PAR

, Birowo

, Hamid

, Taher

, Rasyid

(2021) Genetic polymorphisms as prognostic factors for recurrent kidney stones: a systematic review and meta-analysis. PLoS One 16, e0251235.

51.

Liu

, Lu

, Zhu

, Liu

, Xie

, Ye

, Li

, Wang

, Ming

, Tong

, Liu

, Zhou

(2019) A long noncoding RNA, antisense il-7, promotes inflammatory gene transcription through facilitating histone acetylation and switch/sucrose nonfermentable chromatin remodeling. J Immunol 203, 1548–1559.

52.

, Tie

, Yu

, Zhang

, Wan

(2020) Identifying lncRNA-miRNA-mRNA networks to investigate Alzheimer’s disease pathogenesis and therapy strategy. Aging (Albany NY) 12, 2897–2920.

53.

Zhao

, Zhou

, Cui

, Zhou

(2019) PACES: Prediction of N4-acetylcytidine (ac4C) modification sites in mRNA. Sci Rep 9, 11112.

54.

Ito

, Horikawa

, Suzuki

, Kawauchi

, Tanaka

, Suzuki

(2014) Human NAT10 is an ATP-dependent RNA acetyltransferase responsible for N4-acetylcytidine formation in 18S ribosomal RNA (rRNA). J Biol Chem 289, 35724–35730.

55.

Jiang

, Ma

, Zou

, Wang

, Li

(2017) Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in rheumatoid arthritis. Mol Biosyst 13, 1182–1192.

56.

Covelo

, Araque

(2018) Neuronal activity determines distinct gliotransmitter release from a single astrocyte. Elife 7, e32237.

57.

Iwamoto

, Nakamura

, Takemura

, Hisaoka-Nakashima

, Morioka

(2020) TLR4-TAK1-p38 MAPK pathway and HDAC6 regulate the expression of sigma-1 receptors in rat primary cultured microglia. J Pharmacol Sci 144, 23–29.

58.

Khezri

, Yousefi

, Esmaeili

, Ghasemnejad-Berenji

(2022) The role of ERK1/2 pathway in the pathophysiology of Alzheimer’s disease: An overview and update on new developments. Cell Mol Neurobiol, doi: 10.1007/s10571-022-01191-x.

59.

Xia

, Bacskai

, Knowles

, Qin

, Hyman

(2000) Expression of the chemokine receptor CXCR3 on neurons and the elevated expression of its ligand IP-10 in reactive astrocytes: In vitro ERK1/2 activation and role in Alzheimer’s disease. J Neuroimmunol 108, 227–235.

60.

Caraci

, Battaglia

, Bruno

, Bosco

, Carbonaro

, Giuffrida

, Drago

, Sortino

, Nicoletti

, Copani

(2011) TGF-beta1 pathway as a new target for neuroprotection in Alzheimer’s disease. CNS Neurosci Ther 17, 237–249.

61.

Liu

, Lu

, Zhao

, Hu

, Mei

(2016) Growth differentiation factor-15 promotes glutamate release in medial prefrontal cortex of mice through upregulation of T-type calcium channels. Sci Rep 6, 28653.

62.

, Hao

, Ma

, Zhao

, Wang

, Yin

, Zhang

, Wang

, Zan

, Yang

, Zhang

, Shi

, Zu

, Raza

, Zhang

, Geng

, Hu

, Cui

(2020) Dectin-1/Syk signaling triggers neuroinflammation after ischemic stroke in mice. J Neuroinflammation 17, 17.

63.

Inoue

, Yamakage

, Tanaka

, Kusakabe

, Shimatsu

, Satoh-Asahara

(2019) Oxytocin suppresses inflammatory responses induced by lipopolysaccharide through inhibition of the eIF-2-ATF4 pathway in mouse microglia. Cells 8, 527.

64.

Murcia

JDG

, Weinert

, Freitas

CMT

, Arens

, Ferrel

, Grose

, Ridge

, Wilson

, Kauwe

JSK

, Weber

(2020) Atypical chemokine receptor ACKR2-V41A has decreased CCL2 binding, scavenging, and activation, supporting sustained inflammation and increased Alzheimer’s disease risk. Sci Rep 10, 8019.

65.

Kingeter

, Lin

(2012) C-type lectin receptor-induced NF-kappaB activation in innate immune and inflammatory responses. Cell Mol Immunol 9, 105–112.

66.

Duan

, Zhang

, Hu

, Lu

, Yu

, Bai

(2019) N(4)-acetylcytidine is required for sustained NLRP3 inflammasome activation via HMGB1 pathway in microglia. Cell Signal 58, 44–52.

67.

Furman

, Chang

, Lartigue

, Bolen

, Haddad

, Gaudilliere

, Ganio

, Fragiadakis

, Spitzer

, Douchet

, Daburon

, Moreau

, Nolan

, Blanco

, Dechanet-Merville

, Dekker

, Jojic

, Kuo

, Davis

, Faustin

(2017) Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states. Nat Med 23, 174–184.