Mettl3-mediated m6A disruption of olfactory bulb glutamatergic metabolism homeostasis via the Wnt pathway in aging leads to olfactory dysfunction

Abstract

Olfactory dysfunction not only diminishes quality of life but also serves as an early biomarker for neurodegenerative diseases. However, the molecular mechanisms underlying age-related olfactory dysfunction remain poorly understood. In this study, we profiled the dynamics of m⁶A epitranscriptomics in mouse olfactory bulbs (OBs) throughout postnatal development, adulthood, and aging, revealing the dynamic remodeling of m⁶A methylation during the aging process. Our findings indicate that m⁶A modifications regulate various physiological processes in a stage-specific manner. Notably, in aged OBs, m⁶A methylation is enriched in genes associated with the Wnt signaling pathway. Furthermore, we identified a Wnt pathway involving Wnt, Ror2, and Nkd1 that may contribute to disrupted glutamate metabolism responses linked to age-related alterations in glutamatergic neuron function. This disruption is accompanied by significant reductions in glutamate and its related metabolites, suggesting an imbalance in neurotransmitter levels and impaired mitochondrial function—factors that may lead to olfactory dysfunction. Additionally, we identified METTL3 as a key m⁶A methyltransferase that drives age-related declines in olfactory function. Conditional knockdown of Mettl3 in aged OBs restores both olfactory function and activity within glutamatergic neurons. Our study provides valuable insights into the m⁶A-mediated mechanisms governing OB aging and highlights potential therapeutic targets for mitigating neur odegenerative-associated olfactory deficits.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00018-025-06069-x.

Introduction

Olfactory function is one of the five essential senses and plays critical roles in behavior, social interaction, and daily life [1]. It influences brain activities such as learning and memory and is closely associated with the normal development of cognition, emotion, and affect in children [2]. The prevalence of self-reported olfactory disorders among the elderly population has been reported to be as high as 40%, with an increase observed with advancing age [3]. Olfactory dysfunction not only impacts quality of life, social interactions, and nutrient intake but also poses significant threats to life safety and can lead to depression and other mental health issues [1, 4]. Moreover, olfactory dysfunction is a common early symptom of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) [5]. In older adults, a decline in olfactory function can even predict a greater risk of mortality within five years [6]. Therefore, olfactory dysfunction represents an important public health issue, and exploring its underlying mechanisms can provide valuable insights for developing prevention strategies and therapeutic targets.

The olfactory bulb (OB) represents a pivotal component within the olfactory system that is responsible for processing olfactory signals from the olfactory cortex and subsequently relaying them to higher centers. Notably, the mouse olfactory bulb continues to undergo developmental changes even after birth. The acinar dendrites of Mitral/Tufted (M/T) cells in the olfactory bulb undergo extensive morphological modifications and significant functional development [7, 8]. Neuron production peaks in the first few weeks of postnatal life and continues through adult neurodevelopment throughout life. The ability to detect and discriminate odors decreases with age in older populations and in older rodents [9, 10]. In the aging olfactory bulb, neuronal morphology and synaptic density change with age [11]. However, the mechanisms underlying the cellular and molecular regulation of neuronal changes throughout the life cycle of the olfactory bulb remain unclear.

N6-methyladenosine (m⁶A) is a dynamic and reversible posttranscriptional modification whose functions are determined primarily by RNA methyltransferases (writers), RNA demethylases (erasers), and m⁶A-specific binding proteins (readers) [12]. As one of the most common mRNA modifications, m⁶A plays a crucial role in regulating gene expression, splicing, and degradation [13]. Its enrichment in the brain and its pivotal role in governing nearly every stage of neural development and function have established m⁶A as a master regulator of the central nervous system. In the brain, m⁶A is essential for developmental and physiological processes such as neurogenesis, axon growth, and synaptic plasticity [14]. Recent studies have revealed that m⁶A plays a significant role in the onset and progression of aging-related neurodegenerative diseases. In PD mice, the m⁶A modification level in the striatum is significantly reduced, inducing glutamate receptor expression and promoting neuronal apoptosis [15]. Changes in m⁶A levels have also been observed in the brains of patients with AD and AD model mice [16]. Furthermore, aging and metabolic changes in organisms and cells are interrelated. Recent research has shown that m⁶A modifications can significantly participate in metabolic changes by targeting metabolic enzymes or related pathways [17]. These findings provide a theoretical foundation for the potential functions and mechanisms of m⁶A in maintaining developmental and aging homeostasis. Despite these advances, the role of m⁶A in the olfactory system remains poorly understood. The OBs, the first central relay for odor processing, is highly sensitive to aging and contributes to age-related olfactory decline. Given the established roles of m⁶A in neural plasticity and aging, m⁶A-mediated post-transcriptional regulation is likely important for OB function. However, how the m⁶A landscape of the OB changes across development and aging, and whether m⁶A dysregulation contributes to aging-associated olfactory impairment, remain unknown.

In this study, we used high-throughput sequencing to characterize the dynamic changes in the m⁶A epitranscriptome of OBs in the newborn, adult, and aged stages. We revealed the connections among m⁶A modifications, gene expression, and glutamate metabolism and further identified the potential role of METTL3 knockdown in rescuing olfactory function. Our study characterized m⁶A modification patterns during olfactory bulb aging and provides a theoretical basis for the treatment and prevention of olfactory disorders.

Results

Stage-specific m⁶A switching patterns in the olfactory bulb (OB)

The ability to detect and discriminate odors decreases in aging rodent models [9, 18]. We selected mice at postnatal 10 days, 3 months and 16 months, which were matched to newborn, adult and aged individuals, respectively, to explore olfaction changes with age. To understand the roles of m⁶A methylation in the mouse OB, Methylated RNA immunoprecipitation sequencing (MeRIP-seq) was used to determine the landscape of methylation modifications in newborn, adult and aged OBs. Aging drives dynamic remodeling of the m⁶A epitranscriptome, as evidenced by 25,603 m⁶A peaks in newborn OBs, which decreased in adults (22,965 peaks) and rebounded in aged mice (23,597 peaks) (Fig. S1 A). Among these, 12,198 peaks were common to all groups, 2,032 were shared between newborn and adult OBs, 1,800 were shared between newborn and aged OBs, and 1,695 were shared between adult and aged OBs (Fig. S1 E). A GUITAR plot revealed that m⁶A peaks were predominantly enriched in the coding sequences (CDSs) and 3′ untranslated regions (3′UTRs) across all groups (Fig. 1A). Notably, the proportions of peaks in the 5′ and 3′UTRs increased with age (26.82% vs. 27.96% vs. 29.53% for the 5′UTR; 31.04% vs. 32.31% vs. 32.58% for the 3′UTR) (Fig. S1 B). Most of the mRNA transcripts in all the groups contained a single m⁶A peak, with the number of modification sites ranging from 1 to 19 per transcript (Fig. 1B). Motif analysis revealed that GGACU was the most conserved sequence among all the m⁶A peaks (Fig. S1 C). These findings indicate that OBs undergo dynamic changes in m⁶A methylation across the lifespan.

Fig. 1.

Dynamic m⁶A modification across three stages revolved in the different biological process. (A) Position of detected m⁶A peaks on all methylated transcripts in olfactory bulb. (B) Numbers of transcripts containing different numbers of m⁶A peaks per transcript. (C) KEGG analysis of enriched pathways of different methylated genes between pairwise comparisons. Depicted are the top 5 significantly enriched pathways from each pairwise comparison. (D) Numbers of ‘ON’ and ‘OFF’ genes across three stages. (E) Distribution of each type of ON and OFF m⁶A switch along the whole mRNA transcripts. (F) Giological process terms of the methylated RNAs containing ON or OFF m⁶A switches across the three stages

To assess the biological impact of m⁶A methylation, the enrichment levels were compared among the three age groups. The mean logarithmic fold enrichment levels relative to input samples were 3.46 for newborn OBs, 3.65 for adult OBs, and 3.55 for aged OBs (Fig. S1 D). The proportion of methylated transcripts was highest in aged OBs (67.9%), followed by newborn OBs (64.5%) and adult OBs (63.5%) (Fig. S1 A). Differentially methylated genes (DMGs) were identified through pairwise comparisons (Fig. 1F). Genes involved in brain development, such as Gas213 and Sdk2, were downregulated in adult OBs compared with newborn OBs, whereas Scd1 (metabolism-related) and Rimbp2 (synaptic transmission-related) were upregulated. In aged OBs, Gpr26, an epigenetic silencing marker observed in neurodegenerative disease [19], was downregulated, whereas cell cycle regulation genes such as Ccna1 were upregulated compared with those in adult OBs. Functional enrichment analysis using KEGG revealed that DMGs were enriched in pathways regulating neuronal activity, including calcium signaling, axon guidance, neuroactive ligand‒receptor interaction, and cAMP signaling pathways. Compared with both newborn and adult OBs, downregulated DMGs in aged OBs were specifically enriched in the Wnt signaling pathway (Fig. 1C). These findings suggest that aging-associated differences in m⁶A methylation are linked to genes involved in neural activity and inflammation.

To characterize time-specific m⁶A changes, we identified m⁶A peaks that emerged (“ON”) or were lost (“OFF”) between stages. A total of 5,883 ON peaks and 11,576 OFF peaks were identified (Fig. 1D, S1G). The m⁶A ON and OFF switches clearly differed in their distribution patterns. ON peaks were predominantly enriched in CDS and 3′UTR regions, whereas OFF peaks were more enriched in stop codon regions (Fig. 1E). These data indicate that the distribution of m⁶A marks at different stages of the OB is distinct from that of the entire mRNA transcript. We next performed KEGG analysis for these genes with ON/OFF switches. The genes at different stages appear to have different functions (Fig. 1F). The genes containing ON switches from newborns to adults were enriched mostly in the axon guidance and Ras signaling pathways. Newly occurring methylation in aged OBs was detected in genes involved in protein processing in endoplasmic reticulum pathways. These pathways are closely linked to neurotransmitter transport. Additionally, the genes encoding OFF switches in adult and a OBs were more likely to be enriched in the Wnt signaling pathway, glutamatergic synapses and axon guidance. These findings highlight the dynamic nature of m⁶A methylation and its association with neuronal activity.

Aging-promoted m⁶A methylation regulates the expression levels of genes involved in the wnt pathway

In view of the pivotal role of methylation alterations in the regulation of gene expression, further investigation of the mRNA expression levels of methylated genes was conducted. Methylation exerts a regulatory influence on mRNA transcripts. To evaluate these differences, we combined MeRip-seq and RNA-seq data. The results indicate that temporal characteristics were exhibited by mRNA expression, demonstrating divergent regulatory patterns across the three stages (Fig. 2A). A significant difference in the expression of DMGs was evident between adult OBs and newborns. This pattern was also observed in aged OBs but to a slightly lesser extent than in newborn OBs. However, changes in methylations in aged OBs had a much smaller effect on gene mRNA expression levels than those in adult OBs did. As a further illustration, a nine-quadrant chart of the genes with both differential methylation and differential expression was constructed (Fig. 2B). The methylation levels of genes (Wnt4, Ror2, and Nkd1) involved in the Wnt signaling pathway were significantly upregulated in the aged OBs compared with those in the newborn and adult OBs (Fig. 2E), suggesting that aging increases the number of methylation marks in these three genes. Conversely, the transcript expression levels of these genes were downregulated at this stage (Fig. 2F). This may indicate that genes involved in the Wnt pathway are downregulated by the process of aging-mediated m⁶A methylation. The methylation and expression levels of these genes were examined by MeRip-qPCR and RT‒qPCR, respectively (Fig. 2E, F). These findings suggest that m⁶A methylation may play an epigenetic regulatory role in the expression of Wnt4, Ror2 and Nkd1 in the Wnt signaling pathway in aging OBs.

Fig. 2.

m⁶A methylation participate in regulation of mRNAs expression level in olfactory bulb. (A) Cumulative frequency plots showing the correlation between methylation and mRNA expression level between pairwise comparisons. (B) Nine quadrant graphs showing the DEGs containing differential methylation. (C) Cumulative frequency plots showing how methylation status correlates with mRNA expression levels among three conditions. (D) Nine quadrant graphs exhibiting the DEGs containing differently methylations. (E) MeRip-qPCR assay indicating the m⁶A methylation levels of Wnt pathway genes Wnt4, Ror2 and Nkd1analyzed, as determined by Student’s t-test. (n = 3 per group) (F) RT- qPCR assay indicating the mRNA expression level of Wnt4, Ror2 and Nkd1, as determined by Student’s t-test. (n = 3 per group)

m⁶A alterations are associated with glutamatergic neuron regulation via the wnt pathway

The results presented above suggest that m⁶A methylation is involved in biological functions, including neurons and synapses (Figs. 1 and 2). To investigate the functional changes across the three states in OBs, we focused on examining the changes in the expression levels of markers related to neurons. Dendritic-to-dendritic synaptic connections between olfactory bulb neurons are the basis of odor discrimination. Immunohistochemistry (IHC) was used to examine the expression level of MAP2, which revealed that adult OBs presented a greater number of dendrites than both newborn and aged OBs did (Fig. 3A, C). This finding is consistent with the olfactory function changes observed at the three stages. Additionally, PCNA staining revealed a significant decrease in the number of proliferative cells within the aged OBs (Fig. 3B, D). These data suggested that the number of neurons related to olfaction changed throughout the aging process.

Fig. 3.

m⁶A may be involved in changes in the function of olfactory bulbs at different status. (A) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the MAP2, as determined by ANOVA. (n = 5 per group) (B) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the PCNA, as determined by ANOVA. (n = 5 per group) (C) Quantification of MAP2 IOD/Area of positive cells. (D) Quantification of PCNA IOD/Area of positive cells. (E) Cell marker analysis of enriched cell types of differentially methylated genes that are hypomethylated or hypermethylated between pairwise comparisons. (F) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the VLGUT1, as determined by ANOVA. (n = 5 per group) (G) Quantification of VLGUT1 IOD/Area of positive cells

To determine whether the change in neurons was associated with m⁶A methylation, we investigated in which cell the DMGs were enriched. The results of the enrichment analysis revealed that the DMGs were enriched mainly in neurons and glial cells (Fig. 3E). Compared with newborn OBs, the downregulated DMGs in adult OBs were enriched in neuroblasts, and the upregulated DMGs were enriched in oligodendrocyte cells. Compared with newborn OBs, methylation-upregulated DMGs were enriched predominantly in GABAergic neurons, oligodendrocytes, and quiescent neural stem cells. Conversely, downregulated DMGs were enriched mainly in oligodendrocyte precursor cells and neuronal stem cells. Notably, compared with those in adult OBs, upregulated DMGs in aged OBs were enriched primarily in monocytes, whereas downregulated DMGs were enriched mainly in glutamatergic neurons. We further investigated the activity of glutamatergic neurons by IHC. Vlugt1 staining revealed a reduction in the activity of glutamatergic neurons within aged OBs (Fig. 3F, G). These findings suggest the potential involvement of methylation in age-related glutamatergic neuron dysregulation. A previous study demonstrated that Ror2, which is involved in the Wnt pathway, is the key molecule that regulates glutamatergic synaptic transmission [20].

To determine the cell type-specific impacts of m⁶A modifications in OBs, we performed single-cell RNA sequencing (scRNA-seq) on 49,215 cells from newborn, adult, and aged mice. After stringent quality control, we retained 47,075 cell transcriptomes, including 14,879 cells from newborn OBs, 19,059 cells from adult OBs, and 13,137 cells from aged OBs. Each group generated an average of approximately 15,692 cells (Fig. S2A). Uniform manifold approximation and projection (UMAP) and unsupervised clustering based on canonical marker genes revealed nine major lineages: granule cells (GCs), mitral/tufted (M/T) cells, immature neurons (imNeurons), short axon cells (SACs), periglomerular cells (PGCs), external tufted cells (ETCs), oligodendrocytes (OLs), microglia, and vascular and leptomeningeal cells (VLMC) (Fig. 4A and B). UMAP visualization revealed pronounced spatial segregation between the glial and neuronal populations (Fig. 4A). Furthermore, significant transcriptional disruptions were observed in M/T cells and immature neurons across different aging stages (Fig. 4A). Using an m⁶A activity scoring system (integrating methyltransferase, demethylase, and reader gene expression), we detected elevated m⁶A scores in M/T cells, microglia, and immature neurons (Fig. 4C). Notably, the m6A score of M/T cells—the principal glutamatergic neurons in OBs—progressed with age, suggesting intensified epitranscriptomic regulation in aged glutamatergic neurons (Fig. 4C). Given the critical role of Wnt signaling in OB aging, we quantified pathway activity across cell types (Fig. 4D). Consistent with m⁶A dynamics, M/T cells presented the highest Wnt scores, indicating that m⁶A-mediated Wnt regulation is involved in glutamatergic dysfunction. Gene set variation analysis (GSVA) of M/T cells revealed enrichment of genes related to olfactory transduction, lipoic acid metabolism, and cytokine‒cytokine receptor interactions (Fig. 4E). Collectively, these findings suggest a potential regulatory role for methylation in glutamatergic neurons via the Wnt pathway.

Fig. 4.

scRNA-seq reveals m⁶A involved in dysfunction of glutamatergic neurons via Wnt pathway. A) UMAP plot of olfactory bulb cells from three stages mouse showing nine major clusters. B) Heatmap showing the expression level of representative genes in the different cell types. C)Violin plot showing the m⁶A score in different cell types (Left) and M/T cells across three stages (Right). D) Violin plot showing the wnt score in different cell types. E) Heatmap showing the activity of the M/T cell signaling pathway obtained based on GSVA analysis in the olfactory bulb at different status

Metabolic profiling reveals age-related changes in glutamate and associated pathways

Glutamatergic neurons play a pivotal role in functional brain activities by releasing glutamate, a major excitatory neurotransmitter essential for synaptic transmission and cellular metabolism [21]. Additionally, glutamic acid metabolism is closely associated with the Wnt pathway. To investigate the age-related changes in glutamate levels and its associated metabolic pathways, we performed untargeted metabolomics across the three groups. PCA revealed clear separation among the three groups, indicating significant differences in aging-associated metabolites (Fig. S3A). A total of 44 distinct classes of metabolites were identified, including carboxylic acids and derivatives, fatty acyls, glycerophospholipids, and others (Fig. 5A). The levels of these metabolite classes exhibited dynamic and age-specific alterations. Among fatty acyls, metabolites such as 3-hydroxyisovaleric acid, mevalonic acid, and pantoic acid were significantly elevated in newborn OBs, whereas pimelic acid, goshuyic acid, and citramalic acid were most abundant in adult OBs. Fatty acyl metabolites are known to participate in maintaining energy homeostasis, neuroprotection, and inflammation balance by directly providing energy and regulating immune and neural signaling pathways. Similarly, in the category of indoles and derivatives, tryptophan and related metabolites were markedly reduced in both adult and aged OBs, indicating potential impairments in pathways crucial for neuromodulation. A comparative analysis of differentially abundant metabolites across group pairs revealed 89, 8, and 63 differentially expressed metabolites for the adult/newborn, aged/adult, and aged/newborn comparisons, respectively (Fig. S3B). Most of these differentially abundant metabolites were downregulated in aging OBs (Fig. S3C). Notably, glutamic acid compounds showed the most pronounced alterations with aging (Fig. S3D).

Fig. 5.

Metabolism imbalance of glutamic acid at different stages in olfactory bulbs. (A) Heatmap showing the change of metabolisms in different stages. (B) Box plot showing the level of gama-aminobutyric acid, glutamic acid, glutamine and glu/gaba at different stages. (C) Heatmap showing the expression level of glutamate related metabolisms in mitochondria

Focusing on glutamate-related pathways, we examined the levels of glutamate (Glu) and its key metabolites, glutamine and gamma-aminobutyric acid (GABA), which are critical for maintaining the excitatory–inhibitory balance in the brain (Fig. S3E). Glutamate levels were highest in adult OBs, whereas both newborn and aged OBs presented significantly reduced glutamate levels. Conversely, glutamine, the precursor for glutamate synthesis, was elevated in adult and aged OBs, a change that may represent a metabolic adjustment in response to altered glutamate turnover.In contrast, GABA levels were significantly reduced in both adult and aged OBs, suggesting an age-related impairment in the conversion of glutamate to GABA. The Glu/GABA balance in the brain is crucial for maintaining the normal function of neurons and synapses [22]. The ratio was significantly elevated in adult OBs, potentially reflecting heightened excitatory tone associated with olfactory function during this stage. However, a notable decline in the Glu/GABA ratio was observed in aged OBs, indicating a potential disruption in neural homeostasis with aging.

Given the central role of mitochondria in glutamate metabolism, we further investigated mitochondrial metabolites associated with glutamate biosynthesis and degradation (Fig. 5B). Our analysis revealed significant declines in both glutamate and its related metabolites within the mitochondrial metabolic cycle in aged OBs, suggesting an age-related dysregulation of mitochondrial function. These declines were accompanied by reductions in other metabolic intermediates, suggesting impaired mitochondrial energy metabolism and a reduced capacity for neurotransmitter synthesis. Together, these findings indicate that aging is associated with significant alterations in glutamate-related metabolic pathways, particularly within mitochondria. The observed changes in the Glu/GABA balance and mitochondrial metabolism may contribute to the neural dysfunctions underlying olfactory decline during aging.

Mettl3 is the essential methylase that regulates wnt pathway gene expression by m⁶A methylation

Given that m⁶A enzymes are determinants of m⁶A modifications, we analyzed the transcription and protein levels of the methyltransferase Mettl3, the demethylase Fto, and the “reader” YTHDF2 by RT‒qPCR and IHC, respectively; these genes are known to be ubiquitously expressed (Fig. 6A‒G). The levels of METTL3, FTO and YTHDF2 were confirmed to change across the three stages. METTL3 was clearly expressed at higher levels at both the transcriptional and protein levels in aged OBs than in newborn and adult OBs (Fig. 6A, B, G). However, FTO was more highly expressed in adult OBs (Fig. 6C, D, G). YTHDF2 was highly expressed in newborn OBs (Fig. 6E, F, G). Together, these data suggest that m⁶A methylation may participate in regulating the function of olfaction. We also investigated the relationship between the expression levels of m⁶A enzymes and Wnt pathway-related genes using correlation analysis. The expression levels of Wnt4, Ror2 and Nkd1 were significantly negatively correlated with Mettl3. These results suggest that m⁶A methylation regulates the expression level of Wnt pathway genes by Mettl3.

Fig. 6.

Dynamic changes of m⁶A enzyme in the olfactory bulb at different stages. (A) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the METTL3.(n = 5 per group) (B) Quantification of METTL3 IOD/Area of positive cells, as determined by ANOVA. (C) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the FTO. (n = 5 per group) (D) Quantification of FTO IOD/Area of positive cells, as determined by ANOVA. (E) Immunohistochemistry of olfactory bulb at three conditions using antibodies against the YTHDF2.(n = 5 per group) (F) Quantification of YTHDF2 IOD/Area of positive cells, as determined by ANOVA. (G) RT-qPCR assay indicating the mRNA expression level of Mettl3, Fto and Ythdf2, as determined by Student’s t-test.(n = 3 per group) (H) The correlation of expression level between m⁶A enzyme and Wnt pathway involved genes

Mettl3 knockdown rescues age-related olfactory reduction and glutamatergic neuron function

To investigate the potiential role for Mettl3 in olfaction, we conditionally knocked down Mettl3 by injecting a lentivirus plasmid into the olfactory bulbs of 18-month-old mice (Fig. 7A). Ten days after injection, immunofluorescence examination indicated that the mouse olfactory bulbs had been successfully infected with lentivirus (Fig. 7B). We checked the efficacy of the knockdown by examining the level of METTL3, which was successfully knocked down (Fig. 7C, D). Olfactory behavioral tests, including the food deprivation test, olfactory avoidance test and olfactory habituation-dishabituation test, were performed to evaluate olfactory function on the 10th day after injection. The food burial test is indicative of the ability to detect odors. No discernible alterations were observed between the two groups (Fig. 7E). However, the results of the olfactory avoidance test revealed an increase in the amount of time spent in the blank area to avoid odor disgust in the shMettl3 group (Fig. 7F). In addition, we investigated odor discrimination in an olfactory habituation–dishabituation test. ShMettl3-injected mice exhibited a marked increase in the duration of exploration of a proximate area containing an unfamiliar odor, a pattern that is consistent with a potentially improved olfactory sensitivity (Fig. 7G). This finding suggests a reduced olfactory threshold. Additionally, we excluded differences in olfactory test performance caused by body weight due to impaired olfactory function (Fig. 7H). These data suggest that the reduction in METTL3 expression in aged mice could improve olfactory function. In addition, we investigated whether the methylation level and transcript expression levels of Wnt pathway genes are altered in shMettl3 mice. The methylation levels of genes (Wnt4, Ror2, and Nkd1) were significantly downregulated in the shMettl3 mice OBs compared with control group. While the genes related to the Wnt pathway were obviously upregulated in the OBs of shMettl3 mice (Fig. 7J). We further investigated the activity of glutamatergic neurons in the shMettl3 and control groups. IHC staining was utilized to assess the expression of Vesicular glutamate transporter 1(VGULT1). Quantitative analysis revealed a significant increase in VGULT1 expression after shMettl3 injection (Fig. 7K, L). Consequently, these data indicate that Mettl3-targeted therapy may rescue aging-induced olfactory dysfunction by limiting excessive levels of Mettl3 and Wnt pathway genes.

Fig. 7.

Mettl3 knockdown rescue the age-related olfactory dysfunction. (A) The flowchart of intervention experiment. (B) GFP (red) immunostaining in old mouse injected with lentivirus after 10 days. (C) Representative immunohistochemistry of shMettl3-injected mice and control mice using antibodies against the METTL3. (D) Quantification of METTL3 IOD/Area of positive cells, as determined by Student’s t-test. (E) Box plot showing the latency time of shMettl3 and control group in buried food seek buried test, as determined by Student’s t-test. (n = 5 per group) (F) Box plot showing the avoid index of shMettl3 and control group in olfactory avoidance test, as determined by Student’s t-test. (n = 5 per group) (G) The investigation time of shMettl3 and control group in olfactory habituation-dishabituation test, as determined by Student’s t-test. (n = 5 per group) (H) Box plot showing the weight of shMettl3 and control group, as determined by Student’s t-test. (I) RT-qPCR assay indicating the mRNA expression level of Wnt4, Ror2 and Nkd1 in shMettl3 and control group, as determined by Student’s t-test.(n = 3 per group) (J) Representative immunohistochemistry of shMettl3-injected mice and control mice using antibodies against the VGLUT1. (n = 5 per group) K) Quantification of VGLUT1 IOD/Area of positive cells, as determined by Student’s t-test

Discussion

Although m⁶A methylation is known to be involved in brain development and aging [23], its function and molecular regulation in OBs are still unknown. To address this gap in the field, we profiled the m⁶A epitranscriptome landscape in OBs from newborn to aged individuals and revealed a unique methylation model and biological function. Our investigation revealed that m⁶A is the pivotal factor that regulates the postnatal development and aging of OBs. Methylation of OBs is temporally specific for the regulation of gene expression, a phenomenon that correlates with age. DMGs in newborns presented greater expression differences, whereas those in OBs presented lower expression differences. During the OB aging process, m⁶A regulates glutamate metabolism via the Wnt pathway, affecting glutamatergic neurons and ultimately leading to olfactory dysfunction. Additionally, we found that aberrant expression of METTL3 contributes to olfactory dysfunction associated with olfactory bulb aging via the Wnt pathway. METTL3 knockdown can rescue olfactory deficits.

Given the important role of m⁶A in determining developmental fate and the aging process [24, 25], we hypothesized that m⁶A is also involved in the proliferation, differentiation, and dysfunction of cells in mouse OBs. In this study, we demonstrated that a significant portion of mRNAs in mouse OBs undergo m⁶A modification, with methylation patterns regulated by both development and aging. GO and KEGG analyses revealed that these genes are involved in neuronal differentiation, synapse transmission, and aging-related pathological processes. These genes acquire methylation marks in a temporally specific manner. These findings imply that m⁶A is dynamically regulated in response to numerous physiological processes in OBs. This dynamic regulation of m⁶A observed in mice is conserved in human studies [26], highlighting the functional importance of this modification across species. Notably, we found that m⁶A modifications exhibit temporal specificity in the regulation of gene expression in OBs. Specifically, the differential expression of DMGs was much more pronounced in newborn OBs than in aged OBs. These findings suggest that m⁶A modifications play a critical role in the developmental regulation of OBs and that changes in m⁶A during aging could contribute to both structural and functional impairments in aged OBs. Notably, this temporal shift in m⁶A regulation aligns with previous studies that highlighted dysregulated m⁶A modifications in cellular aging and age-related diseases, further supporting the notion that m⁶A could be a key player in aging-associated dysfunctions [27, 28]. Together, these findings suggest that m6A modifications not only regulate key developmental processes in OBs but also contribute to age-related dysfunctions, highlighting their potential as critical molecular players in the aging of the olfactory system.

Glutamate is the primary excitatory neurotransmitter in the mammalian brain and plays a crucial role in OB function [21]. The local neuronal microcircuitry of the OB comprises glutamatergic neurons (mitral/tufted cells, M/T cells) and interneurons, which are essential for encoding sensory information [29]. In our study, we observed a significant decrease in glutamate levels in aged OBs, a finding that is consistent with previous reports of glutamate reduction in the aging brain [30]. This decline likely represents a key factor contributing to impaired OB function. To further elucidate this phenomenon, we analyzed glutamate-related mitochondrial metabolic intermediates and detected an overall reduction in their expression levels in aged OBs. These findings suggest that mitochondrial dysfunction may disrupt glutamate metabolism, impairing the maintenance of glutamatergic neuronal function and ultimately leading to OB dysfunction. This highlights the intricate interplay between metabolic dysregulation and neuronal function during aging. Glutamate metabolism is tightly regulated by glutamine synthetase (GS), a key enzyme whose expression and activity are controlled by the Wnt signaling pathway. The Wnt pathway is well known for its roles in development, differentiation, and tissue homeostasis and has emerged as a critical regulator of cellular metabolism. Previous studies have identified GS as a direct transcriptional target of the Wnt pathway, linking Wnt dysregulation to impaired GS expression and disrupted glutamate metabolism [31]. However, the molecular mechanisms underlying Wnt pathway dysregulation during aging and its impact on OB function remain poorly understood. Our study provides novel evidence that m⁶A modification contributes to the suppression of the Wnt signaling pathway during OB aging. Specifically, we revealed that m⁶A regulates the expression levels of key Wnt signaling-related genes, including Ror2, Wnt4, and Nkd1, which are essential for maintaining cellular and synaptic homeostasis. Notably, previous studies have demonstrated that Ror2 influences NMDAR-mediated glutamatergic synaptic transmission [20], a process critical for OB function. Additionally, differential m⁶A methylation in aged OBs was enriched in glutamatergic neurons, supporting the hypothesis that m6A modifications disrupt glutamate metabolism and neuronal function via the Wnt pathway. Although the regulatory role of m⁶A in the Wnt pathway has been extensively studied in cancer biology [32], our findings are the first to establish that this mechanism is conserved in the context of aging. These results underscore the potential for targeting m⁶A modifications or the Wnt pathway to mitigate aging-associated OB dysfunction and its broader implications for age-related neurodegenerative diseases.

A growing body of research has demonstrated that METTL3 plays crucial roles in regulating both physiological and pathological events within the nervous system [33]. METTL3 has emerged as a potential diagnostic biomarker and therapeutic target for neurological disorders [33, 34]. METTL3 plays a pivotal role in the aging of OBs through its involvement in m⁶A-dependent regulation of key genes. Our study revealed that aberrant upregulation of METTL3 in aged OBs is correlated with olfactory dysfunction, a finding that aligns with previous reports of increased METTL3 levels in the hippocampi of patients with Alzheimer’s disease (AD) and AD animal models [35]. Considering that age-related olfactory dysfunction is a well-recognized precursor symptom of AD, the consistent upregulation of METTL3 in both the OBs of individuals with olfactory impairment and the hippocampi of patients with AD suggests a shared pathological mechanism. These observations highlight METTL3 dysregulation as a potential molecular link between olfactory dysfunction and the neurodegenerative processes associated with aging and AD. Our study specifically investigated the impact of METTL3 knockdown in aged mice to assess its role in olfactory dysfunction associated with aging. Notably, METTL3 knockdown selectively restored olfactory discrimination ability in aged mice, highlighting its role in age-related OB dysfunction. Mechanistically, this may involve the regulation of RNA stability and the translation of Wnt signaling-related genes, such as Ror2 and Wnt4, which are crucial for synaptic maintenance and glutamate metabolism. Overall, these findings establish METTL3 as a key mediator of OB aging and a potential target for addressing age-related olfactory dysfunction and broader neurodegenerative conditions.

Despite the comprehensive insights provided by our study, several limitations remain that warrant further investigation. First, although we identified METTL3 as a key regulator of m⁶A modifications in OBs and its role in aging, the potential contributions of other m⁶A regulatory factors, such as METTL14, FTO, and ALKBH5, were not explored. These factors could interact with METTL3 or play distinct roles in the aging process, which future studies should address. Additionally, future research should also extend the findings to human studies to establish clinical relevance and validate the conservation of these mechanisms across species. Furthermore, although our study demonstrated the therapeutic potential of targeting METTL3 to ameliorate olfactory dysfunction, additional preclinical studies are necessary.

This study advances our understanding of the molecular mechanisms underlying olfactory bulb aging by highlighting the pivotal role of m⁶A modifications, particularly those mediated by METTL3, in regulating glutamatergic neuronal function and glutamate metabolism. The temporal specificity of m⁶A modifications underscores their dual roles in both development and aging, offering a framework for understanding how epitranscriptomic dysregulation contributes to age-related dysfunctions in OBs. METTL3 could serve as an early diagnostic marker or therapeutic target. Our study underscores the therapeutic potential of targeting METTL3 to mitigate olfactory dysfunction during aging and to explore broader applications for neurodegenerative conditions, including AD.

Methods

Animal

C57BL/6J mice were purchased from Charles River. All animals were bred and maintained in a temperature-controlled environment (23 ± 2℃) with a light/dark cycle of 12 h. All experiments were approved by the Capital Institute of Pediatrics Experimental Animal Administrative Committee.

RNA isolation, methylated RNA immunoprecipitation sequencing (MeRIP-seq), RNA-seq and data analysis.

Mice were sacrificed by cervical dislocation, and bilateral olfactory bulbs from each mouse were rapidly dissected and homogenized as a whole without layer separation. 7 OBs are mixed into one sample, three samples were detected in each group. Total RNA was purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Using NanoDropND-1000 (Thermo Fisher Scientific, Waltham, MA, USA) to determine the RNA concentration of each sample, and samples with OD260/OD280 ratios between 1.9 and 2.1 were used for further experiments. Denatured agarose gel electrophoresis was used to measure the RNA integrity and gnomic DNA contamination.

Total RNA was subjected to immunoprecipitation with the GenSeq^® m⁶A-IP Kit (GenSeq Inc.) by following the manufacturer’s instructions as previous described [36]. RNA was randomly fragmented to ~ 200nt by RNA Fragmentation Reagents. Protein A/G beads were coupled to the m⁶A antibody by rotating at room temperature for 1 h. The RNA fragments were incubated with the bead-linked antibodies and rotated at 4 °C for 4 h. After incubation, the RNA/antibody complexes were washed for several times, and then, captured RNA was eluted from the complexes and purified. RNA libraries for IP and input samples were then constructed with GenSeq^® Low Input Whole RNA Library Prep Kit (GenSeq, Inc.) by following the manufacturer’s instructions. Libraries were qualified using Agilent 2100 bioanalyzer and then sequenced in a NovaSeq platform (Illumina).

Total RNA was used for removing the rRNAs with GenSeq^® rRNA Removal Kit (GenSeq, Inc.). Then, the rRNA-depleted samples were subjected to library construction with GenSeq^® Low Input RNA Library Prep Kit (GenSeq, Inc.) according to the manufacturer’s instructions. Libraries were controlled for quality and quantified using the BioAnalyzer 2100 system (Agilent Technologies, Inc., USA). Library sequencing was performed on an Illumina Novaseq instrument with 150bp paired end reads.

Paired-end reads were obtained with the Illumina Novaseq 6000 sequencer, and their quality was assessed based on the Q30 score. Post 3’ adaptor trimming and removal of low-quality reads using Cutadapt software (v1.9.3), clean reads from all libraries were aligned to the reference genome (mm10) using HISAT2 software. To identify methylated sites on RNAs, the MACS program was employed, and DiffReps was used to detect differentially methylated sites. Overlapping peaks identified by both tools were matched with mRNA exons using custom scripts. Motif enrichment in m6A peaks was analyzed with MEME software (version 5.1.1; available at https://meme-suite.org/meme/), and p-value heatmaps for motifs were produced with the pheatmap R package. For a comprehensive view of m6A peaks across the entire transcript, the Integrative Genomics Viewer was used. Differentially methylated peaks (DMPs) were identified using DiffReps, with a threshold of an adjusted p-value (Benjamini-Hochberg FDR) < 0.05 and an absolute fold-change > 2.0. Genes associated with these significant DMPs were defined as differentially methylated genes (DMGs) for downstream functional enrichment analysis.

ClusterProfiler (version 4.2.2; available at https://github.com/YuLab-SMU/clusterProfiler) was utilized for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The ggplot2 R package was employed to generate plots based on GO/KEGG terms and cell markers.

As previous described [37], using the “-s”, “-c”, and “-o” parameters in BEDTools’ mergeBed, we combined m⁶A peaks from all stages to identify dynamic “ON” and “OFF” m⁶A switches. “ON” switches were defined as m⁶A peaks present in a later stage but not in the preceding one, whereas “OFF” switches referred to peaks showing the opposite pattern.

Untargeted metabolomics

Untargeted metabolomics were performed at LipidALL Technologies. Polar metabolites were extracted using methanol: water (4:1, v/v) with phenylhydrazine. Tissues were minced and homogenized, then kept at −20 °C for 30 min for alpha-keto acid derivatization. Samples were incubated at 1500 rpm at 4 °C and centrifuged at 12,000 rpm. The supernatant was collected and the extraction process was repeated, after which the extracts were pooled and dried. Reconstitution occurred in 5% acetonitrile before LC-MS analysis using an Agilent 1290 II UPLC with Sciex 5600 + quadrupole-TOF MS. Polar metabolites separation used a Waters ACQUITY HSS-T3 column. MS settings included a negative ion mode voltage of − 4.5 kV and a scan range of m/z 70–700. Data was analyzed using Analyst^® TF 1.7.1 and MarkerView 1.3. Metabolite identities were annotated using PeakView 2.2 and databases such as HMDB. A cocktail of isotopically-labeled standards was used for quantification. Endogenous metabolite levels were normalized to these standards, with an algorithm for selecting optimal internal standards based on minimal COVs.

Olfactory assessment

Buried food-seeking test

As previous described [38], begin by removing chow pellets 24 h before testing, leaving only water in the home cage. On day 2, acclimate the mouse to the test cage conditions before proceeding. For the buried food-seeking test, hide a 2 g chow pellet 8 cm beneath fresh bedding in one corner of the test cage. Place the mouse in the opposite corner and start the timer. Record the latency as the time it takes for the mouse to find the pellet, stopping the timer once the mouse touches it. If the mouse fails to locate the pellet within 5 min, record the latency as 300 s. Use a clean cage, fresh bedding, and a new pellet for each mouse. Additionally, use the unburied food-seeking test to assess motivation, differing only by placing the pellet on the bedding’s surface.

Olfactory Avoidance Test

This experiment was modified from a previously reported test procedure [39]. The experiment was conducted at 5–6 p.m. The test cage was divided into two equal areas, with a piece of filter paper (2*2 cm) containing methyl salicylate placed on one side, and a digital video camera was placed above the test cage to record the mice’s behaviour in the test cage. The duration of the test was 10 min. The time the mice stayed in the area of the filter paper without the scent of methyl salicylate was analysed. Avoidance behaviour was expressed by the avoidance index (avoidance index = [P-50]/50, where P is the percentage of time spent avoiding during the 10-min test period). Analyses were done by non-testers.

Olfactory habituation-dishabituation test

This experiment was performed only once per mouse to avoid confounding data based on learning and memory. As previous described [39], each mouse was transferred to the test cage for 15 min prior to the start of the experiment for familiarisation, during which time care was taken to clean the cage floor at regular intervals to prevent the spread of such odours in the test cage. A digital video camera was used to record mouse behaviour during the first and second experiments. Filter paper without any odour was placed in the test cage during the first time, and filter paper containing eugenol odour was placed in the test cage during the second time. The test cage was cleaned with alcohol and ventilated for 5 min after the second experiment. The time at which the mice entered the 2-cm area near the filter paper was recorded each time. The ‘time of investigation’ is the time difference between the first and second experiments.

The experimental procedures and statistical analyses were performed by two different individuals in all the above behaviral test. In order to prevent any interference between experiments, certain mice were exclusively monitored for behavioral tests.

Immunohistochemistry and immunofluorescence analysis

The tissue antigen was repaired, and sections were blocked with 5% goat serum protein. They were then incubated overnight at 4 °C with primary antibodies and followed by a PBS wash and incubation with peroxidase-coupled secondary antibodies for 30 min at 37 °C. Additionally, sections were incubated with primary antibodies against GFP, also overnight at 4 °C, followed by incubation with Cy5-labeled secondary antibodies for 30 min at 37 °C. Nuclei were stained with DAPI, and images were observed under a Leica fluorescence microscope at 400× magnification and analyzed using ImageJ software.

Antibodies

Primary antibodies used for immunohistochemistry and immunofluorescence are as follows: Rabbit anti-VGLUT1 (CST, Cat#47181), Rabbit anti-METTL3 (CST, Cat#86132), Rabbit anti-FTO (Abcam, Cat#ab280082), Rabbit anti-YTHDF2 (Abcam, Cat#ab246514), Rabbit anti-MAP2 (Abcam, Cat#ab183830), Rabbit anti-PCNA (CST, Cat#13110), Mouse anti-GFP (CST, Cat#55494).

MeRIP-qPCR, RT-qPCR

Total RNA was extracted from olfactory bulb tissue using RNeasy Mini Kit (QIAGEN) following to the manufacturer’s instructions. The purity and concentration of extracted RNA were determined by NanoDrop ND-1000 instrument. RNAs were reversed into cDNA using an Omniscript Reverse Transcription Kit (Qiagen). After reverse transcription, Quantitative real-time PCR was performed using SYBR Green (Applied Biosystems, Foster City, CA). GAPDH was used as an internal control. Each experiment was repeated at least three times, and all data were presented as mean ± standard deviation (SD). The primer sequences are shown in Supplementary Table 1. Three differentially methylated RNA sites were selected to design specific primers for MeRIP-qPCR using NCBI Primer-Blast [40]. The forward primer and reverse primer were synthesized by Sangon Biotech Co. (Shanghai, China). The primer sequences are shown in Supplementary Table 1. Then reverse transcribed IP RNA and input RNA by PrimeScriptTM RT Reagent Kit and gDNA Eraser Kit (TAKARA, Shiga, Japan) to get cDNA, and qPCR was performed on QuantStudio™ 5 System.

Plasmid constriction

This study employed a lentiviral knockdown system consisting of pLV3-CMV-GFP-Puro, PG-P1-VSVG, PG-P2-REV, and PG-P3-RRE. Within the LV3 vector, mouse METTL3 shRNA was placed between the HIV 3 L and the H1 promoter. To titrate the lentivirus, transduction of 293T cells was performed using a tenfold serial dilution. The lentivirus used in this study generally had a titer ranging from 1,108 to 11,010 TU/ml. The knockdown efficiency in N2a cells was evaluated through Western blot analysis. The oligonucleotide sequences for METTL3 shRNA were as follows: mouse METTL3-shRNA: 5′-GCACACTGATGAATCTTTAGG-3′; scramble control: 5′-TTCTCCGAACGTGTCACGT-3′.

Lentivirus infection of OBs

The injection procedure followed following the previous reports [36, 41]. Male mice, aged 18 months, were placed in a stereotaxic instrument after anesthesia with isoflurane. The isoflurane was used at a concentration of 4% for induction and 1.2% for maintenance during the lentiviral injection. A microsyringe was employed to inject polybrene (5 g/ml) and 1 µl of lentivirus into each side of the olfactory bulbs (coordinates: 4.5 mm anterior/posterior, 1 mm medial/lateral, and 2 mm dorsal/ventral from bregma) at a rate of 0.25 µl per minute. The syringe was withdrawn gently five minutes post-infusion. Once the mice were awake from anesthesia, they were returned to their cages, maintained at 37 °C. Behavioral assessments and other analyses were conducted at least 10 days following the virus injection. All behavioral tests concluded with an assessment of lentivirus infection via RT-qPCR or immunohistochemical analysis.

Cell-type annotation

According to the existing annotations of the brain in the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/) [42], DEGs, DMGs-enriched cell types were marked.

Single-cell RNA sequencing and data analysis

This study utilized the DNBelab4C [43] technology to prepare single-cell/nucleus suspensions from the mouse olfactory bulb. Quality control assessments were performed on the prepared cell/nucleus suspensions. Qualified suspensions were processed using the DNBelab C4 droplet microfluidics system to generate droplets, enabling single-cell isolation. Following droplet generation, the emulsion was broken to recover mRNA molecules, which were then reverse-transcribed into complementary DNA (cDNA). The synthesized cDNA was subsequently used for library construction and sequencing.

The sequencing data were further processed for quality control using the Seurat package [44] (version 4.0.0). Theoretically, the number of genes expressed per cell, the number of unique molecular identifiers (UMIs), and the proportion of mitochondrial transcripts should exhibit a concentrated distribution within a specific range. Therefore, we filtered low-quality cells based on the distribution of nUMI, nGene, and percent.mito. The specific quality control criteria included retaining cells with a gene count > 200, UMI count > 1000, log10GenesPerUMI > 0.7, mitochondrial UMI proportion < 10%, and erythrocyte gene proportion < 5%. Additionally, the DoubletFinder package [45] (version 2.0.3) was used to remove doublets.

After quality control, the NormalizeData function in Seurat was used for data normalization. Highly variable genes (HVGs) were identified using the FindVariableGenes function (mean.function = FastExpMean, dispersion.function = FastLogVMR) in Seurat, selecting the top 2000 HVGs. Principal component analysis (PCA) was then performed based on the expression profiles of these highly variable genes. The results were visualized in a two-dimensional space using uniform manifold approximation and projection (UMAP) for nonlinear dimensionality reduction. To identify marker genes, the FindAllMarkers function in Seurat (test.use = presto) was used to detect differentially upregulated genes in each cell type compared to other clusters. These genes were considered potential marker genes for each cell type and validated with canonical markers (Table S2) at a Louvain resolution of 0.4.

Addmodulescore analysis

To quantitatively assess the pathway activity at the single-cell level, we computed an ‘m6A activity score’ and a ‘Wnt pathway activity score’ for each cell using the AddModuleScore function in the Seurat R package (v4.3.0). Briefly, the function compares the average expression of a predefined gene set (Table S3) against a set of control genes. These control genes are automatically selected to have similar expression levels to the target genes across the entire dataset, which helps to normalize for background variation. The final score for a cell is the average expression of the target gene set minus the average expression of the control sets. A positive score indicates higher-than-baseline expression of the gene program, while a negative score indicates lower-than-baseline expression. These scores were then used to compare activity across different cell types and conditions.

GSVA enrichment analysis

Background gene set files were downloaded and curated from the KEGG database (https://www.kegg.jp/) using the GSEA Base package (version 1.44.0). The GSVA package [46] (version 1.30.0) was employed to score pathway activity at the single-cell level. Finally, the LIMMA package (version 3.38.3) was used to calculate differences in pathway activities between different groups.

Statistical analysis

Each experiment was conducted independently a minimum of three times, with the results expressed as Mean ± SD. The results from two groups were analyzed using Student’s t-test, while comparisons between more than two groups utilized one-way ANOVA. ANOVA was followed by Bonferroni’s post hoc test for multiple comparisons. A p-value of less than 0.05 was considered statistically significant, represented as *p < 0.05, **p < 0.01, or ***p < 0.001.

Supplementary Information

Below is the link to the electronic supplementary material.

Authors’ contributions

Wen Hu: Conceptualization; Data curation; Formal analysis; animal experiments; Writing - original draft. Xiao-Yi Wang: Methodology, Formal analysis; Xiao-Jun Zhan: Methodology. Pei Pei: RT-qPCR and MeRIP-qPCR; Ya-Ru Kong: Investigation. Jie Ji: animal breeding. Jia Liu: Literature retrieval. Shan Wang: Supervision; Visualization; Writing - review & editing. Jun Tai: Conceptualization; Funding acquisition; Project administration; Supervision; Writing - review & editing. All authors read and approved the final manuscript.

Funding

This work was supported by grants from Beijing Hospitals Authority’s Ascent Plan (DFL20221102), High Level Talents Cultivation Project for Public Health Leading Talent (Leading Talents-02-07) Beijing Municipal Health Commission, Public service development and reform pilot project of Beijing Medical Research Institute (BMR2021-3), Beijing Postdoctoral Science Foundation and New Quality Fund of Capital Institute of Pediatrics (XZYY-202506). The National Natural Science Foundation of China (82271193), Supported by New Quality Foundation of Capital Institute of Pediatrics（XZYB-04).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The IRB number is DWLL2021016, which was approved by Capital Institute of Pediatrics Experimental Animal Administrative Committee.

Consent for publication

No applicable.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.