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Altered N6-methyladenosine Modification Patterns and Transcript Profiles Contributes to Cognitive Dysfunction in High-Fat Induced Diabetic Mice

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15 December 2023

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18 December 2023

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Abstract
Abstract: Objectives: To establish the expression profile of N6-methyladenosine(m6A) modifications in the hippocampus of mice with diabetic cognitive impairment (DCI) and elucidate the potential regulatory mechanism. Methods: A DCI model was established by feeding a high-fat diet to C57BL/6J mice. m6A and RNA sequencing was conducted to profile the m6A-tagged transcripts in the hippocampus. Results: Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing analyses yielded differentially m6A-modified and expressed genes in the hippocampus of DCI mice, which were enriched in pathways involving synaptic transmission and axonal guidance. Mechanistic analyses revealed a remarkable change in m6A modification levels through alteration of the mRNA expression of m6A methyltransferases (METTL3 and METTL14) and demethylase (FTO) in the hippocampus of DCI mice. Conclusions: We identified a co-mediated specific RNA regulatory strategy that broadens the epigenetic regulatory mechanism of RNA-induced neurodegenerative disorders associated with metabolic and endocrine diseases.
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Subject: Biology and Life Sciences  -   Neuroscience and Neurology

1. Introduction

Patients with type 2 diabetes mellitus (T2DM) are more likely to develop mild cognitive impairment(MCI) than those without T2DM, and patients with T2DM and MCI are more likely to progress to Alzheimer’s disease (AD) than those without either disease [1]. Epidemiological data show that patients with T2DM have a 1.4- to 1.8-fold higher risk of MCI and a 1.5- to 2.5-fold higher risk of dementia than the general population [2]. Cognitive impairment due to hippocampal damage in patients with T2DM mainly includes abnormalities of memory function, attention, visual–spatial cognitive function, executive function, and information processing speed [3]. Diabetic cognitive impairment (DCI), an irreversible degenerative disease of the central nervous system, has remained a major problem because the exact mechanism underlying its development has not been fully elucidated and effective means of prevention and treatment are lacking [4]. DCI burdens the patient’s family and society [5]. An understanding of the precise pathogenic mechanisms of DCI could lead to strategies for its early detection, identification, and intervention, which would effectively prolong the onset of hippocampal damage in individuals with T2DM, thereby, averting the progression of dementia and AD and ultimately enhancing the overall quality of life of patients.
N6-methyladenosine (m6A) is an RNA modification that affects the splicing, export, translation, and degeneration of mRNAs [6]. As a dynamic modification, m6A plays vital roles in pathological and physiological processes in eukaryotes [7]. Post-transcriptional methylation is performed by three types of enzymes: methyltransferase complexes (writers), demethylases (erasers), and binding proteins (readers) [8]. Previous studies have indicated a strong association between m6A modifications and metabolic disorders, including obesity and T2DM [9]. Studies have also revealed an association between m6A and neurodegenerative diseases, including glioblastomas [10], traumatic hippocampal injury [11], and Parkinson’s disease [12]. m6A is highly abundant in the mammalian hippocampus [13] and plays important regulatory roles in synaptic function [14], axonal regeneration [15], neuronal development, and neurogenesis [16]. N6-methyladenosine–sequencing (M6A-seq) of mouse hippocampal synaptosomal mRNAs revealed extensive distribution of m6A in the distal pre- and postsynaptic regions. Moreover, functional enrichment analysis showed that 1266 hyper-methyl esterified genes were enriched in synapse-related functional pathways, including synaptic maturation, organisation, assembly, and regulation of synaptic transmission [17]. m6A is dynamically regulated during learning. Learning training enhances m6A modifications in immediate early genes (IEGs) related to memory, such as ARC and CFO. Additionally, this training facilitates the translation of these genes, indicating a strong correlation between m6A mRNA modification and learning and memory formation processes. [18]. m6A also promotes hippocampus-dependent learning and memory through a binding protein encoded by YTHDF1 [19]. These findings improve our understanding of the mechanisms underlying the regulation of learning and memory at the RNA level. Moreover, they provide a new direction for studying the molecular mechanisms underlying damage to hippocampal neurons.
This study aimed to establish the profile of m6A modifications in mice with DCI and elucidate the potential regulatory mechanisms of m6A methylation underlying DCI. We performed methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-Seq) and RNA-Sequencing (RNA-seq) to analyse the differences in gene methylation and mRNA expression in mice with DCI and verified the change in the expression of methylase and its regulatory role in DCI. In this study, we identified genes that were differentially m6A-modified and expressed in the DCI mouse hippocampus. These genes were enriched in pathways related to synaptic transmission and axonal guidance. Our results show that abnormalities in methylation are the genetic mechanisms underlying the development of DCI.

2. Results

Changes in body weight and fasting blood glucose levels between the two groups of mice
At the start of the experiment, the mean body weight of mice in the DCI (19.36 ± 0.24 g) and control (19.18 ± 0.15 g) groups was not statistically significant (p = 0.511). Similarly, at the start of the experiment, the mean blood glucose level in the DCI (4.20 ± 0.69 mmol/L) and control (4.12 ± 0.60 mmol/L) groups was not statistically significant (p = 0.801). The body weight of mice in both groups increased as the experiment progressed, and the differences between the groups were statistically significant one week after the start of the experiment (p < 0.01; Figure 1A). The fasting blood glucose concentration was significantly high in both the groups one week after the start of the DCI experiment than in the control group (p < 0.01; Figure 1B). During the experiment, the hyperglycaemic state of mice in the DCI group persisted, suggesting that the T2DM mouse model was successfully established.

Results of the Morris water maze test

In the Morris water maze test (Figure 2), all mice showed shorter escape latency than on day 1. The difference was not statistically significant (p = 0.985), but the escape latency of mice in the control group was significantly shorter than that of mice in the DCI group on days 2–4 (Figure 2A). The difference in swimming speed between the two groups was not statistically significant (Figure 2B). The average swimming distance for mice in the DCI group was significantly higher on day 5 than that for mice in the control group (Figure 2C). The time spent in the target quadrant (p < 0.05, Figure 2D) and the number of platform crossings (p < 0.01, Figure 2E) for mice in the DCI group were significantly lower than for mice in the control group, indicating the development of cognitive impairment and the successful establishment of the DCI mouse model.

H&E staining of the hippocampus in the two groups of mice

Changes in the hippocampus of DCI mice were assessed using H&E staining. As shown in Figure 3, the hippocampal cells in the control group were tightly packed and showed a regular morphology and uniform staining. In contrast, in the DCI group, the number of neurons was reduced, and they were sparsely arranged and had condensed nuclei.

GO and KEGG pathway analyses of DEGs in the hippocampus of DCI mice

To explore the differences in transcriptome profiles, we performed RNA-seq analysis of hippocampal samples obtained from control and DCI mice. The expression levels of 666 genes were significantly altered, of which 297 were up-regulated and 369 were down-regulated (Figure 4A). Table 1 shows the top 20 DEGs in the hippocampus of DCI mice. GO analysis showed that the up-regulated genes were most enriched in nucleobase-containing compound, cellular nitrogen compound, heterocycle, and cellular aromatic compound metabolic processes (Figure 4B). In contrast, down-regulated genes were enriched in several metabolic pathways, cellular component organization, cellular amide metabolic process, and ganglioside metabolic process (Figure 4C). KEGG pathway analysis further revealed that the up-regulated genes were enriched in RNA transport, spliceosome, and mRNA surveillance pathways (Figure 4D). The down-regulated genes were associated with metabolic pathways, including those implicated in non-alcoholic fatty liver disease, Parkinson’s disease, and Alzheimer’s disease (Figure 4E).

Altered m6A modification of genes in the hippocampus of DCI mice

To compare the m6A modification of genes between the control and DCI groups, we performed a transcriptome-wide m6A-seq analysis using MeRIP-seq. In total, 23,711 non-overlapping m6A peaks in the control group were found within 10,664 coding gene transcripts (mRNAs) in the DCI group. In contrast, 23,751 non-overlapping m6A peaks in the DCI group were found within 10,809 mRNAs in the three replicates of the control group. Of these, 9345 methylated genes overlapped between the control and DCI groups (Figure 5). A total of 1278 DMMSs were identified, of which 60.88% (778/1278) and 39.12% (500/1278) showed significantly higher and lower levels of methylation, respectively, in DCI versus the control. Table 2 shows the top 10 up- and down-methylated m6A sites with the highest fold-change values (DCI vs. control). To analyse their distributional profiles, m6A peaks were distributed throughout the RNA (Figure 6A) and were divided into the following five groups based on the transcript regions in which they were distributed: 5′UTR, 3′UTR, segments of the transcription start site region, stop codon segments, start codon segments, and coding sequences (CDS). The distribution of m6A peaks was significantly enriched in the vicinity of the 3′UTR, CDS, and stop codons (Figure 6B). To assess the regions of m6A modification on these genes, those annotated by peak were screened, and the coverage of reads on these genes was determined. In the DCI group, m6A modification regions were biased toward the 3′UTR region (Figure 6C). All the DMMSs within mRNAs were mapped to chromosomes to assess their distribution profiles. The m6A modification profiles differed across mouse chromosomes. The top five chromosomes harbouring the most DMMSs were ch2, ch5, ch7, ch11, and ch1 (Figure 7).
To examine the biological function of m6A modification in DCI mice, protein-coding genes containing DMMSs from the two groups were selected for GO and KEGG analyses (Figure 8A). In the GO analysis, the hyper-methylated genes were significantly enriched in the cellular process, regulation of biological process, catalytic activity, and heterocyclic compound binding. The KEGG pathway analysis revealed that hyper-methylated genes were associated with the T-cell receptor signaling pathway, small cell lung cancer, and RIG-I-like receptor signaling pathway (Figure 8B). For the hypo-methylated genes, GO analysis revealed significant enrichment in the cellular process, metabolic process, binding organic cyclic compound, binding heterocyclic compound, and catalytic activity (Figure 8C). The KEGG pathway analysis revealed the association of hypo-methylated genes with hypertrophic cardiomyopathy and the Hippo signaling pathway (Figure 8D).

Differential m6A-modification and expression of genes in the cerebral cortex result from altered m6A methyltransferase and demethylase levels.

To investigate how DCI altered the m6A modification and affected gene expression, we evaluated the expression of 26 m6A RNA methylation regulators selected from published literature; these included nine writers (METTL3, METTL14, Mettl16, Wtap, Virma, Zc3h13, Rbm15, Rbm15b, Cbll1), 15 readers (Ythdc1, Ythdc2, Ythdf1, Ythdf2, Ythdf3, Hnrnpc, Fmr1, Lrpprc, Hnrnpa2b1, Igfbp1, Igfbp2, Igfbp3, Rbmx, Elavl1, Igf2bp1), and two erasers (FTO and Alkbh5). Compared with the control group, the expression of METTL14 and FTO was significantly down-regulated in the DCI group. The expression of METTL3 was significantly up-regulated in the DCI group compared with that in the control group (p < 0.05) (Figure 9).
To determine the alterations in m6A modification-related protein levels in the hippocampal tissues of diabetic mice, we measured the mRNA and protein levels of METTL3, METTL14, and FTO in the hippocampal tissue of mice using qRT-PCR and western blotting. As shown in Figure 9C and 9D, compared with the control group, the mRNA and protein levels of METTL3 were increased, and those of METTL14 and FTO were decreased in the hippocampal tissue of diabetic mice, which was consistent with the results of RNA sequencing analysis.

Conjoint analysis for m6A MeRIP-seq and RNA-seq data

The combined analysis of DMMSs and DEGs yielded 163 mRNAs with significantly altered m6A peaks and mRNA levels. Both m6A and mRNA levels were up-regulated for 41 mRNAs and down-regulated for 14 mRNAs. Moreover, 30 genes showed up-regulated mRNA expression and down-regulated m6A levels, whereas 78 genes showed down-regulated mRNA expression and up-regulated m6A levels (Figure 10A). Finally, a PPI network was constructed to show the connections between the proteins encoded by the 163 genes (Figure 10B). GO and KEGG analyses for four groups (1. m6A up-regulated and mRNA down-regulated; 2. m6A up-regulated and mRNA up-regulated; 3. m6A down-regulated and mRNA down-regulated; and 4. m6A down-regulated and mRNA up-regulated) of genes are shown in Figure 11. In the GO analysis, the m6A up-regulated and mRNA down-regulated genes were significantly enriched in the cellular metabolic process, aromatic compound biosynthetic process, and regulation of RNA metabolic process. The KEGG analysis showed that these genes were enriched in fatty acid metabolism, small cell lung cancer, propanoate metabolism, and pyruvate metabolism. For the m6A up-regulated and mRNA up-regulated genes, GO analysis revealed enrichment in ferrous iron binding, sulfinoalanine decarboxylase activity, aspartate 1-decarboxylase activity, and negative regulation of cell junction assembly and KEGG analysis revealed enrichment in the type 2 diabetes mellitus, adipocytokine signaling, and insulin signaling pathways. The m6A down-regulated and mRNA down-regulated genes were enriched in oligopeptide binding, glutathione binding, and disulphide oxidoreductase activity in the GO analysis whereas they were enriched in the citrate cycle (TCA cycle), propanoate metabolism, and apoptosis - multiple species in the KEGG analysis. The m6A down-regulated and mRNA up-regulated genes were enriched in embryonic skeletal joint morphogenesis, activation of protein kinase activity, and NADP metabolic process in the GO analysis and in neomycin, kanamycin, and neurotrophin signaling pathway in the KEGG analysis.

3. Discussion

Epidemiological and animal studies have shown that T2DM increases the risk of developing neurodegenerative diseases [20] . However, the molecular mechanisms through which T2DM affects the central nervous system remain unknown. m6A, the most common post-transcriptional modification, plays an essential role in several biological processes. In vivo, the hippocampus exhibits a developmentally altered abundance of m6A methylation. m6A methylation is biased toward neuronal transcripts and is sensitive to neuronal activity [21] . In the hippocampus, m6A regulates several developmental and physiological processes, including neurogenesis, axonal growth, synaptic plasticity, circadian rhythms, cognitive function, and stress response [22] . With continuous progress in high-throughput sequencing technology, increasing attention to the m6A landscape has been focused on neoplastic and non-neoplastic diseases of the CNS. Revealing the m6A landscape in the hippocampus of DCI mice will help explore new mechanisms underlying DCI and should provide new targets for prevention and treatment. In this study, we elucidated the epigenetic mechanisms of m6A in the hippocampus of DCI mice. First, we found that DM induction in mice fed a high-fat diet caused cognitive disorders and damage to hippocampal neurons. Next, we performed MeRIP-seq and RNA-seq and identified 1278 m6A peaks to be significantly differentially methylated. Among them, 500 peaks were significantly down-regulated in DCI mice. Employing the conjoint MeRIP-seq and RNA-seq analyses, we determined the presence of DEGs and differentially modified RNAs in the control and DCI groups. Based on our findings, we speculate that alterations in m6A modifications in the hippocampus may be responsible for diabetic nerve damage, which may lead to the development of cognitive disorders in patients with diabetes.
Cognitive function-related hippocampal structures, such as the hippocampus, are susceptible to hyper- and hypo-glycaemia. A vast spectrum of pathological changes has been observed in rodent models of diabetes, including synaptic alterations, decreased cell proliferation, increased microvascular permeability, neuron loss, and hippocampal atrophy [23]. In our study, behavioural tests showed that DCI mice exhibited memory impairment. In addition, we found that the DCI mouse model suffered from structural damage to the hippocampus, as evidenced by the results of H&E staining. Next, RNA-seq analysis revealed that the genes with up-regulated mRNA levels (compared with the control group) were significantly enriched in RNA transport, spliceosome, mRNA surveillance pathway, and RNA degradation. The genes with down-regulated mRNA levels were significantly enriched in non-alcoholic fatty liver disease, Parkinson’s disease, Alzheimer’s disease, and metabolic pathways. MeRIP-seq analysis revealed that methylation in the hippocampus tissue of DCI mice was characterised by more hyper-methylated regions on chromosomes 1, 7, 9, and 12 compared with that in the control, and these hyper-methylated regions were associated with neurodegenerative diseases and specific immune-related pathways, axonal guidance, NF-kappaB signalling pathway, T-cell receptor signalling pathway, and FoxO signalling pathway. More hypo-methylated regions were observed on chromosomes 2, 3, 5, 11, 15, and X, and these down-regulated methylation genes were enriched in pathways associated with T2DM and cell signalling, such as transcriptional misregulation in cancer, hypertrophic cardiomyopathy, Hippo signalling pathway, mTOR signalling pathway, basal cell carcinoma, longevity regulating pathways-multiple species, FoxO signalling pathway, Toll-like receptor signalling pathway, signalling pathways regulating pluripotency of stem cells, and TNF. Taken together, genes associated with neurodevelopmental and neurodegenerative changes were characterised by the up-regulation of m6A methylation and down-regulation of mRNA expression in the hippocampus of DCI mice. In contrast, genes associated with T2DM, obesity, and cancer showed a trend toward down-regulation of m6A methylation and up-regulation of mRNA expression.
To investigate the mechanism by which DCI alters m6A modification and affects gene expression, we evaluated 26 previously reported regulators of m6A RNA methylation. We found that m6A methylase, METTL3, METTL14, and FTO were significantly altered in the DCI group compared with that in the control group. METTL3 and METTL14 are the essential subunits of the m6A writer complex. METTL3 is the catalytic subunit and METTL14 activates METTL3 via allostery and recognition of RNA substrates [24]. Many investigators have evaluated the necessity of m6A in neuronal function by conditionally deleting METTL14. This deletion reduced striatal m6A levels without altering the number or morphology of cells, and significantly reduced the total m6A level in mRNAs isolated from the substantia nigra region, thereby, altering dopaminergic neuron function [25]. In the present study, the expression of METTL14 was significantly down-regulated in the DCI group, which may have led to the inability to form a stable METTL3 and METTL14 complex conformation and reduced the catalytic activity. Reduction in m6A levels can induce apoptosis in dopaminergic neurons by elevating oxidative stress and Ca2+ influx [26]. FTO was the first demethylase involved in m6A to be discovered [27]. FTO mediates m6A and m1A RNA demethylation [19] . m6A-dependent demethylation by FTO is associated with dopaminergic neurotransmission, adult neurogenesis, and axonal elongation. Moreover, elevated FTO levels crucially influence glucose and lipid metabolism-related gene expression in patients with DM [28] . In the present study, the FTO expression was reduced in the hippocampus of DCI mice, consistent with previous findings [29] . Therefore, we speculate that HFD-induced T2DM is secondary to FTO down-regulation, which is a protective mechanism in organisms; however, the role of FTO in the CNS of patients with diabetes remains to be investigated.
To better understand the roles of m6A methylation in T2DM patients with cognitive impairment, we screened all the differentially expressed peaks combined with the differentially expressed mRNA (1. m6A up-regulated and mRNA down-regulated; 2. m6A up-regulated and mRNA up-regulated; 3. m6A down-regulated and mRNA down-regulated; and 4. m6A down-regulated and mRNA up-regulated). The m6A up-regulated mRNA-down-regulated genes include Pcgf2, Map6d1, Angpt2, Rec8, and Dcaf5. Most of these genes, such as transcription factor family genes Mybl1 [30] and Fnip2, are related to the occurrence and development of T2DM and morbid obesity [31]. Wnt10a, which is associated with both Parkinson's disease and fat accumulation, exhibits a direct correlation with the inflammatory cytokine gene Lin37. Although Wnt10 and Lin37 are not directly related to the occurrence of diabetes and morbid obesity, the results of the PPI network analysis showed that these genes directly interact with genes associated with the development of T2DM, such as Mybl1. KEGG enrichment analysis showed that these genes were significantly enriched in the mTOR signalling pathway, metabolic pathway, fatty acid metabolism, and mTORComplel1-S6K1 signalling pathway, which is directly related to insulin resistance [32]. The genes with up-regulated m6A peak and up-regulated mRNA included Ddx5, Wdr89, Nrarp, Lyrm9, Stum, and Ttc14. The enrichment analysis revealed that most of these genes were related to immune response. Among these, Irs4 is directly associated with insulin resistance [33]. KEGG enrichment analysis showed that these genes were significantly enriched in T2DM, FoxO signalling pathway, and PI3K-Akt signalling pathway. Activation of the PI3K/AKT pathway can accelerate apoptosis, and most studies have confirmed the inflammatory cascade reactions. The inflammatory cascade can down-regulate glucose transporters and insulin-related molecules (such as GLUT4, cyclin A, cyclin E, and IRS-1), leading to elevated blood glucose and insulin resistance in patients with T2DM [34]. Genes with down-regulated m6A peaks and down-regulated mRNAs levels included Gins2, Efs, Emd, Suclg1, Entpd3, Gpbp1l1, Ngfr, and Pfkfb3. The enrichment analysis revealed that most of these genes were related to metabolism and cell cycle. Among these, the cyclic nucleic acid replication factor Gins2 is an important component of the replication fork uncapping enzyme GINS complex in cell cycle [35]. In addition, mice knocked out for Ngfr exhibited a delayed initial growth rate and a neurological manifestation of the hind-limb grip response in the tail suspension test [36]. The PPI network analysis showed that Ngfr directly interacts with genes associated with neurodegenerative diseases, such as Bmp4, Ngf, and Angpt2. Therefore, Ngfr may indirectly contribute to the development of neurodegenerative diseases by acting on these genes. The genes with down-regulated m6A peak and up-regulated mRNA levels included Mapk13, Adamts19, Bpifb9b, Lrrc71, Dnali1, Cd320, Sp7, Mfap2, and Ngf. Pathway enrichment analysis has shown that these genes are significantly enriched in neurodegenerative pathologies, such as Parkinson's disease, Alzheimer's disease, and depression. For example, it has been shown that Cd320 is a cell membrane surface receptor involved in the intracellular transport of vitamin B12, which is integral to the function of the nervous system and is involved in lipid synthesis and maintenance of metabolism and function of nerve myelin [37] . Other genes in this group are involved in various neurodegenerative diseases through their involvement in apoptosis, oxidative stress, and neurotransmitter imbalance [38]
Neurodegenerative diseases are caused by many factors, including oxidative stress, neurotransmitter imbalance, genetic and epigenetic factors, and defects in neurogenesis [37]. Our findings in this study indicate that genes exhibiting a decrease in m6A modification but an increase in mRNA expression within the hippocampus of mice fed an HFD are implicated in the pathogenesis of diseases, such as T2DM and obesity. A strong trend towards the down-regulation of genes related to neurodevelopment, including synaptic transmission and CNS developmental pathways, and cell signalling, was found, in addition to altered mRNA expression of methylation-related enzymes. The PPI network analysis revealed significant association between genes implicated in neurodegenerative disorders and those involved in the progression of T2DM, suggesting a direct interaction and mutual influence between them.

4. Materials and Methods

Animals

Specific pathogen-free (SPF) male C57BL/6J mice (6-weeks-old, 19.08 ± 0.58 g) were purchased from the Beijing Weitong Lihua Experimental Animal Technology Co., Ltd [Experimental Animal License number: SCXK (Beijing) 2019-0010]. All mice were raised in an animal facility, at an indoor temperature of 18–22 °C and relative humidity of 40–60%, under 12 h alternating dark/light cycles. All the mice were allowed free access to food and water. Before starting the experiments, all animals were housed in an animal house environment for one week. The animals were randomly divided into the DCI and control groups, with nine animals in each group. The DCI group was fed a high-fat diet (HFD; Cat #D12492, Research Diets, USA; 60 kcal% fat) for six weeks, whereas the control group was fed a standard-fat diet (SFD; Cat #1022, Beijing HFK Bioscience, China; 10 kcal% fat). The study protocol was approved by the Animal Ethics Committee of the University (approval Number: LA2019184).

Behavioural test

The Morris water maze (MWM) test is commonly used to evaluate spatial learning and memory in rodents. The experimental apparatus comprised a black circular water pool (diameter, 1.50 m; height, 0.60 m). The water temperature in the pool was maintained at 24 ± 2 °C. The pool had a featureless inner surface divided into four equal quadrants (A–D) [21]. A translucent 10 × 10 cm platform, submerged 1 cm below the water surface, was hidden in the centre of quadrant NE (target quadrant) during the training period and removed at the time of the probe task. Training was conducted three times daily for five consecutive days before the probe task. Each mouse was allowed to swim until it found the platform or until 60 s had elapsed. The mice were then left on the platform for 10 s. The platform was removed from the pool for the space exploration task and the mice were allowed to swim for 60 s. The swim escape latency, path length, and time spent in the target quadrants were recorded using a video tracking system (Zhongshi Dichuang Information Technology Co. Ltd., Beijing, China).

Tissue sectioning and staining

Haematoxylin and eosin (H&E) staining was performed according to the following procedure: The hippocampus tissue was quickly excised and immersed in 4% paraformaldehyde at 4 °C for 24 h before being embedded in paraffin. A tissue slicer was used to obtain coronal sections (4 µm thick). Finally, the sections were de-waxed with xylene and dehydrated using an ethanol gradient before H&E staining, following the manufacturer's instructions (Beyotime, Shanghai, China) to visualize structural damage.

High-throughput m6A-seq and RNA-seq

The mice were anaesthetised and transcardially perfused with 200 mL of saline at 4 °C. The hippocampus was then carefully dissected and stored in liquid nitrogen. Total RNA from the hippocampus was extracted after lysing it with Tissuelyser-24 (Shanghai, China) using the TRIzol™ reagent. Total RNA (20 µg) was used, and three biological replicates were used for the control and DCI groups. The RNA samples were chemically fragmented to obtain 200 nt fragments. The fragmented RNA was incubated overnight with an anti-m6A antibody and Dynabeads. After washing three times with 1× IP buffer and three times with 1× wash buffer, the immunoprecipitated mRNA fragments were extracted with phenol-chloroform and precipitated with ethanol. m6A antibody-enriched mRNA and input mRNA libraries were constructed as follows: cDNA was synthesised from total RNA after removing the rRNA, followed by PCR amplification; finally, the completed libraries were purified on a BiOptic Qsep100 Bio-Fragment Analyzer for quality control. The libraries were sequenced on an Illumina NovaSeq platform following the PE150 protocol.

Sequencing data processing

Adapters and filter sequences were trimmed using Cutadapt (v2.5.0). Sequencing data quality, mass distribution, base content distribution, and repeated sequencing fragment proportion were analysed using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc) [39]. The remaining reads were aligned to the human ensemble genome, GRCh38 (mouse ensemble genome GRCm38), using the Hisat2 aligner (v2.1.0) with the following parameter: “-rna-strandedness RF.” m6A peaks were identified using the exomePeak R package (v2.13.2) with the following parameters: “PEAK_CUTOFF_PVALUE 0.05, PEAK_CUTOFF_FDR NA, FRAGMENT_LENGTH 200.” The m6A peaks with a p-value < 0.05 were chosen for subsequent de novo motif analysis using homer (v4.10.4) with the following parameter: “-len 6 -RNA.” m6A-RNA-related genomic features were visualised using the Guitar R package (v1.16.0). The HOMER software (http://homer. ucsd. edu/homer/ngs/peak Motifs. html) was used to analyse the motifs of the m6A peaks [40]. The BAM files of sequencing results were visualised using IGV (http://software. broadinstitute. org/software/igv/) [41].

Identification of differentially expressed genes (DEGs) and differentially methylated m6A sites (DMMSs)

The DiffReps software was used to identify the DEGs. The default filter criteria were p-value < 0.05 and | fold change| >0.5. Screening of differential m6A peaks was conducted using the exomePeak R package, and the filtering threshold was set at p-value < 0.05 and | fold change| >1.

Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) analysis of DEGs and DMMSs

The GO program (http://www.geneontology.org) comprises a structured controlled vocabulary of annotated genes, gene products, and sequences. We performed GO functional analysis to annotate and speculate on the potential roles of the DEGs and DMMSs. The GO terms with p-value ≤ 0.05 were regarded to be significantly enriched. The KEGG pathway analysis coordinates the molecular datasets of metabolomics, transcriptomics, genomics, and proteomics onto a KEGG pathway map to explain the biological functions of these molecules. A KEGG term with a p-value ≤ 0.05 was considered significantly enriched.

Protein–protein interaction (PPI) network analysis

We conducted a conjoint analysis of genes showing differential expression and differential m6A modification and used the p-value and fold change criteria to screen candidates for PPI network analysis. These DEGs were imported into the STRING database, which contains comprehensive information about interactions between proteins, to determine the interactions between genes [42]. The PPI network was constructed by importing data into the Cytoscape 3.5.1 software, and the network was analysed using a network analyser. Genes showing interactions with combined scores greater than 0.4 were selected to construct a PPI network diagram [43].

Validation of gene expression levels

After evaluating the gene expression of 26 m6A RNA methylation regulators screened from the published literature, we found significant differences in the expression of METTL3, METTL14, and FTO between the two groups of mice. The expression levels of METTL3, METTL14, and FTO were evaluated in hippocampal tissue samples from mice in the control and HFD groups. Total RNA was extracted from frozen tissue samples using TRIzol reagent, followed by centrifugation and isopropanol precipitation. The RNA pellets were washed with ethanol, air-dried, and dissolved in RNase-free water. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed using diluted cDNA products, with Actb as an internal control. The primers used for qRT-PCR were designed using the Primer3 website. The protein levels of METTL3, METTL14, and FTO were quantified using western blot analysis. The hippocampal tissue was homogenised in RIPA buffer and the supernatant was collected. The protein content in the supernatant was quantified using the BCAN Protein Assay Kit. Equal quantities of total protein extracts were electrophoresed, and the proteins were transferred onto PVDF membranes. After blocking and incubation with primary antibodies, the membranes were incubated with corresponding secondary antibodies. The membranes were then washed, and the protein bands were visualised using an enhanced chemiluminescence kit. The expression levels of proteins were determined by densitometry analysis using the ImageJ software.

Statistical analysis

All statistical analyses were conducted using the R software (version 4.2.2). The values are presented as mean ± standard deviation (SD) for continuous numerical data with normal distribution. The student’s t-test was used to compare two groups of normally distributed data with equal variance, and the Welch’s t-test was employed for normally distributed data with unequal variance. A p-value of < 0.05 was considered statistically significant.

5. Conclusions

This study demonstrates that the perturbation of glucose metabolism resulting from feeding an HFD induces changes in the expression of enzymes related to m6A modification. These alterations lead to modifications in the methylation patterns of genes associated with neurodegenerative disorders, particularly in the hippocampal region of mice. These findings provide new insights into the mechanism of HFD-induced cognitive dysfunction in diabetic mice, which could lead to a new therapeutic strategy for diabetes-induced hippocampal lesions. Further studies should prioritise the validation of these genes to ascertain the precise pathways associated with this pathological process. This validation should be conducted through in vitro and in vivo experiments, wherein methylation-related enzymes are manipulated to observe their effect on gene expression, the diabetic hippocampus is analysed for apoptosis, and its correlation with cognitive status is elucidated.

Author Contributions

Zhaoming Cao: Conceptualization, Methodology, Software & Writing- Original draft preparation Yu An.: Data curation, Visualization, Investigation. Yanhui Lu: Supervision, Writing- Reviewing and Editing.

Funding

This research was funded by the National Key Research and Development Program of China [grant numbers:2022YFA1103602], the National Natural Science Foundation of China grant numbers:82003456], the Fundamental Research Funds for the Central Universities, and the Peking University Clinical Medicine + X youth program.

Institutional Review Board Statement

The animal study protocol was approved by the Animal Ethics Committee of the University (approval Number: LA2019184).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request; please contact the corresponding author via email.

Acknowledgments

We would like to thank Editage[http://www.editage.cn] for English language editing.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Comparison of changes in body weight and fasting blood glucose between two groups ofmice before and after modelling A) Body weight; B) Fasting blood glucose. Significance identification: ns; Not significant, p≥0.05;*, p<0.05; **,p<0.01;***,p<0.001.
Figure 1. Comparison of changes in body weight and fasting blood glucose between two groups ofmice before and after modelling A) Body weight; B) Fasting blood glucose. Significance identification: ns; Not significant, p≥0.05;*, p<0.05; **,p<0.01;***,p<0.001.
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Figure 2. Comparison of spatial probe test between the two groups of mice in the Morris water maze. A) Distance to the platform; B) Average swimming speed the orientation navigation trial; C) Mean distance to platform; D) Time in the target quadrant; E) Number of crossing platforms.
Figure 2. Comparison of spatial probe test between the two groups of mice in the Morris water maze. A) Distance to the platform; B) Average swimming speed the orientation navigation trial; C) Mean distance to platform; D) Time in the target quadrant; E) Number of crossing platforms.
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Figure 3. H&E staining results in the hippocampus of two groups of the mice. Cotrol group:A,B&C, DCI group: D, E&F. Black arrow:sparse arrangement and reduced number of neuromal cells;blue arrow:neuronal nuclei solidified.
Figure 3. H&E staining results in the hippocampus of two groups of the mice. Cotrol group:A,B&C, DCI group: D, E&F. Black arrow:sparse arrangement and reduced number of neuromal cells;blue arrow:neuronal nuclei solidified.
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Figure 4. Differential expression of genes in cerebral cortex between control and DCl mice. A) Difference ranking of RNA sequencing result. B) The top twenty items of the most enriched GO analysis of up-regulated genes. C)The top twenty items of the most enriched GO analysis of down-regulated genes. D)The top twenty items of the most enriched pathway analysis of up-regulated genes. E)The enriched Pathway analysis of down-regulated genes.
Figure 4. Differential expression of genes in cerebral cortex between control and DCl mice. A) Difference ranking of RNA sequencing result. B) The top twenty items of the most enriched GO analysis of up-regulated genes. C)The top twenty items of the most enriched GO analysis of down-regulated genes. D)The top twenty items of the most enriched pathway analysis of up-regulated genes. E)The enriched Pathway analysis of down-regulated genes.
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Figure 5. Venn diagram of m6A-modified genes in the control group and the DCl group.
Figure 5. Venn diagram of m6A-modified genes in the control group and the DCl group.
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Figure 6. Overview of altered m6A methylation map in the hippocampus of mice. A. The distribution of peaks in different regions of the gene. CDs are the CDS region of the gene, utr5 andutr3 are the 5'UTR and 3'UTR of the gene, respectively, PR intron is the intronic region of the coding gene, and NP_exon and NP_intron are the exons and intronic regions of the non-coding gene. B. Distribution of Peak in the different areas on gene exons. The horizontal coordinates are the 5'UTR, CDS, and 3'UTR,and the vertical coordinates are the relative proportions of the distribution of different peak regions, C. Heat map of reads distribution of Peak-associated genes in the DCI group. The left and right graphs represent the distribution of reads in IP and input samples on the functional regions of the genes annotated to peak, respectively. The top graph shows the cumulative distribution of reads over all functional regions of genes (Total reads are taken as a logarithm of 10), and the bottom graph shows the distribution of reads on each gene, with the color gradient from blue to yellow to red, to represent the depth of coverage from light to dark.
Figure 6. Overview of altered m6A methylation map in the hippocampus of mice. A. The distribution of peaks in different regions of the gene. CDs are the CDS region of the gene, utr5 andutr3 are the 5'UTR and 3'UTR of the gene, respectively, PR intron is the intronic region of the coding gene, and NP_exon and NP_intron are the exons and intronic regions of the non-coding gene. B. Distribution of Peak in the different areas on gene exons. The horizontal coordinates are the 5'UTR, CDS, and 3'UTR,and the vertical coordinates are the relative proportions of the distribution of different peak regions, C. Heat map of reads distribution of Peak-associated genes in the DCI group. The left and right graphs represent the distribution of reads in IP and input samples on the functional regions of the genes annotated to peak, respectively. The top graph shows the cumulative distribution of reads over all functional regions of genes (Total reads are taken as a logarithm of 10), and the bottom graph shows the distribution of reads on each gene, with the color gradient from blue to yellow to red, to represent the depth of coverage from light to dark.
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Figure 7. The bar graph of distribution of methylated m6A sites along chromosomes.
Figure 7. The bar graph of distribution of methylated m6A sites along chromosomes.
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Figure 8. Pathway enrichment of Differentially methylated m6A sites (DMMSs). A) GO analysis of hypermethylated genes. B) KEGG analysis of hypermethylated genes. C) GO analysis of hypomethylated genes. D) KEGG analysis of hypomethylated genes.
Figure 8. Pathway enrichment of Differentially methylated m6A sites (DMMSs). A) GO analysis of hypermethylated genes. B) KEGG analysis of hypermethylated genes. C) GO analysis of hypomethylated genes. D) KEGG analysis of hypomethylated genes.
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Figure 9. A. Box line plot of RNA expression of 26 m6A RNA methylation regulators in the brain tissue of control and DCI group of mice. B. Heat map of METTL3, METTL14, and FTO expression in the brain tissue of mice in control and DCI group. C. Expressions of METTL3, METTL14, and FTO in the hippocampus were detected by Western blot using anti METTL3, METTL14, and FTO antibody, respectively GAPDH was used as loading control Values are means±SEM. n=3. ***P<0.001, **P<0.01, *P<0.05vs control group. D. Reverse transcription polymerase chain reaction (RT-PCR) of METTL3,METTL14,and FTO. Values are means±SEM. n=3. ***P<0.001, **P<0.01, *P<0.05vs control group.
Figure 9. A. Box line plot of RNA expression of 26 m6A RNA methylation regulators in the brain tissue of control and DCI group of mice. B. Heat map of METTL3, METTL14, and FTO expression in the brain tissue of mice in control and DCI group. C. Expressions of METTL3, METTL14, and FTO in the hippocampus were detected by Western blot using anti METTL3, METTL14, and FTO antibody, respectively GAPDH was used as loading control Values are means±SEM. n=3. ***P<0.001, **P<0.01, *P<0.05vs control group. D. Reverse transcription polymerase chain reaction (RT-PCR) of METTL3,METTL14,and FTO. Values are means±SEM. n=3. ***P<0.001, **P<0.01, *P<0.05vs control group.
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Figure 10. Joint analysis of m6A methylation and mRNA expression. A. Nine quadrant graph diagram shows the relationship between mRNA m6A methylation and its mRNA expression. B. The protein-protein interaction network shows the connection between the proteins of the genes used in the combined analysis.
Figure 10. Joint analysis of m6A methylation and mRNA expression. A. Nine quadrant graph diagram shows the relationship between mRNA m6A methylation and its mRNA expression. B. The protein-protein interaction network shows the connection between the proteins of the genes used in the combined analysis.
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Figure 11. GO and KEGG analysis analyses of the 163 DEGs and DMMSs. A. GO analysis of m6A up-regulated & mRNA down-regulated genes. B. GO analysis of m6A up-regulated & mRNA up-regulated genes. C. GO analysis of m6A down-regulated & mRNA down-regulated genes. D. GO analysis of m6A down-regulated & mRNA up-regulated genes E. KEGG analysis of m6A up-regulated & mRNA down-regulated genes. F. KEGG analysis of m6A up-regulated & mRNA up-regulated genes. G. KEGG analysis of m6A down-regulated & mRNA down-regulated genes. H. KEGG analysis of m6A down-regulated & mRNA up-regulated genes.
Figure 11. GO and KEGG analysis analyses of the 163 DEGs and DMMSs. A. GO analysis of m6A up-regulated & mRNA down-regulated genes. B. GO analysis of m6A up-regulated & mRNA up-regulated genes. C. GO analysis of m6A down-regulated & mRNA down-regulated genes. D. GO analysis of m6A down-regulated & mRNA up-regulated genes E. KEGG analysis of m6A up-regulated & mRNA down-regulated genes. F. KEGG analysis of m6A up-regulated & mRNA up-regulated genes. G. KEGG analysis of m6A down-regulated & mRNA down-regulated genes. H. KEGG analysis of m6A down-regulated & mRNA up-regulated genes.
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Table 1. Top twenty differentially expressed genes in the brain of DCI mice.
Table 1. Top twenty differentially expressed genes in the brain of DCI mice.
Gene name Chromosome log2FC Pattern p-value
Ndufa12 10 21.70 up 0.034
Klc1 12 18.19 up 0.020
Map7d1 4 18.01 up 0.043
Ccdc124 8 15.51 up 0.017
Gtf2f1 17 13.92 up 0.026
Dbn1 13 12.82 up 0.023
Palm 3 12.30 up 0.018
Rrp1 1 11.72 up 0.038
Sfpq 4 11.36 up 0.013
Trir 8 9.92 up 0.015
Gabarapl1 6 -21.73 down 0.001
Eef1a1 9 -20.84 down 0.006
Septin4 11 -14.17 down 0.014
Cox6b1 7 -10.71 down 0.002
Atf4 15 -10.46 down 0.023
Nsg1 5 -9.15 down 0.008
Fos 12 -7.36 down 0.026
Eif1 18 -7.32 down 0.034
Tuba1b 15 -7.21 down 0.007
Mdh1 1 -7.13 down 0.043
Table 2. The top twenty differentially methylated peaks in the brain in DCI mice.
Table 2. The top twenty differentially methylated peaks in the brain in DCI mice.
Gene name Chromosome log2FC p-value Peak region Pattern
Foxb2 19 5.68 0.006 CDS up
Tex15 8 5.65 0.035 CDS up
Angpt2 8 5.40 7.60E-05 3’UTR up
Snai2 16 5.25 0.004 CDS up
Fstl5 3 5.06 2.73E-04 3’UTR up
Tgfb2 1 5.01 0.007 CDS up
Lrrc2 9 4.98 7.59E-09 3’UTR up
Ccdc62 5 4.98 0.001 3’UTR up
B3gnt3 8 4.94 0.006 3’UTR up
4930533K18Rik 10 4.77 0.014 3’UTR up
Igbp1b 6 -3.31 0.029 CDS down
Rassf10 7 -2.88 2.31E-05 CDS down
Rhoh 5 -2.59 0.037 3’UTR down
N4bp2 5 -2.48 0.006 CDS down
Txndc2 17 -2.46 0.020 5’UTR down
Rab7b 1 -2.40 0.039 3’UTR down
Gm5093 17 -2.40 4.31E-05 CDS down
Hist2h3c2 3 -2.27 0.002 3’UTR down
P2ry1 3 -2.24 0.039 CDS down
Erich5 15 -2.17 0.013 CDS down
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