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. 2026 May 18;47(3):765–784. doi: 10.24272/j.issn.2095-8137.2025.430

miR-449a modulates Alzheimer’s disease pathology through regulation of Navβ2

Guo-Ji Yan 1,#, Xu-Yang Wang 2,#, Xin-Ying Shi 1,#, Min-Nan Lu 1,3, Dan Wang 1, Chen-Jun Ye 1, Shan-Shan Li 4, Chen-Di Yang 1, Xi-Wen Liang 1, Yi-Jian Zhang 1, Xin-Wang Yang 5,*, Tao Hu 6,*, Yan-Bin Xiyang 1,*
PMCID: PMC13647049  PMID: 42267564

Abstract

Progression of Alzheimer’s disease (AD) from mild cognitive impairment (MCI) to dementia occurs gradually and remains mechanistically unresolved, limiting development of effective interventions. This study investigated the contribution of Navβ2 and its upstream regulatory microRNA, miR-449a, to MCI- and AD-associated neuropathology in multiple mouse models. Results revealed that Navβ2 expression was dysregulated in MCI and AD mice, accompanied by synaptic abnormalities, aberrant amyloid precursor protein (APP) processing, and disrupted Nav1.1α expression. Importantly, manipulation of miR-449a markedly influenced synaptic function and cognition through regulation of Navβ2, with miR-449a overexpression alleviating and miR-449a inhibition exacerbating cognitive impairment. Co-transfection assays further demonstrated that the effects of miR-449a on APP metabolism were mediated by Navβ2. Electrophysiological analyses provided additional evidence that the miR-449a/Navβ2 axis participates in regulation of cognitive function. Together, these findings provide new mechanistic insight into synaptic dysfunction and cognitive decline in AD and identify the miR-449a/Navβ2 axis as a candidate therapeutic target for cognitive impairment. Further investigation of the miR-449a/Navβ2 axis may facilitate development of targeted strategies to preserve synaptic integrity and cognitive function during AD progression.

Keywords: Alzheimer’s disease, Voltage-gated sodium channel beta 2 (Navβ2) subunit, miR-449a, Cognitive impairment

INTRODUCTION

Population aging is driving a rapid increase in the burden of Alzheimer’s disease (AD) and related dementias, with global prevalence projected to nearly triple over the next three decades, imposing escalating clinical, societal, and economic costs worldwide (Alzheimer’s Association, 2024; Gale et al., 2018). The pathological hallmarks of AD—including the accumulation of amyloid-beta (Aβ) plaques and tau tangles—drive progressive cognitive decline and memory impairment (Villemagne et al., 2013). Despite extensive research, therapies capable of curing AD or substantially delaying disease progression remain elusive.

Mild cognitive impairment (MCI) represents an intermediate stage between normal aging and dementia and is characterized by measurable cognitive decline that exceeds the level expected for age without fulfilling diagnostic criteria for dementia (Katz & Peters, 2008; Knopman et al., 2021). Although progression from MCI to AD is not inevitable, a considerable proportion of affected individuals ultimately develop AD, underscoring the need to define biomarkers and therapeutic targets that enable intervention at an early stage (Bruscoli & Lovestone, 2004; Ward et al., 2013). Elucidation of the molecular events that drive progression from MCI to AD is therefore essential for developing effective preventive and therapeutic strategies (Zhang et al., 2023).

Recent studies have linked genes associated with aging to cognitive decline and memory impairment observed in AD (Cai et al., 2017; Hane et al., 2017). Among these candidates, the voltage-gated sodium channel 2 beta subunit (Navβ2), encoded by SCN2B, has attracted increasing attention due to its involvement in neuronal excitability and synaptic transmission (Cai et al., 2017; Lu et al., 2004). Navβ2 is a transmembrane glycoprotein required for the stabilization of the Nav1 α subunit (Nav1.1α) at the cell surface (Jansson et al., 2014). In addition to this structural role, Navβ2 contributes to voltage-dependent activation and inactivation, sodium channel signal conduction, regulation of neuronal excitability, control of Nav1.1α surface expression, and inactivation of Nav1.8 (Vijayaragavan et al., 2004). Navβ2 also participates in interactions with other signaling components, including cell exostroma and cytoskeletal elements (Chen et al., 2002; Kim et al., 2007; Lopez-Santiago et al., 2006; Lu et al., 2004).

Aberrant Navβ2 expression and function have been linked to a range of pathological conditions, including neuropathic pain (Lopez-Santiago et al., 2006), epilepsy (Ito et al., 2006), multiple sclerosis, and acute experimental viral encephalitis (O'Malley et al., 2009). Previous research has revealed age-associated up-regulation of SCN2B in the frontal lobe and hippocampus of senescence-accelerated mouse prone 8 (SAMP8), implicating this gene in cognitive impairment associated with brain aging (Hao et al., 2007; Liu et al., 2015; Xiyang et al., 2016). Navβ2 has also emerged as a novel substrate of beta-site APP cleaving enzyme 1 (BACE1) and γ-secretase, further suggesting potential involvement in AD pathogenesis (Kim et al., 2005; Wong et al., 2005). Nevertheless, the relationship between Navβ2 and cognitive dysfunction during MCI and AD progression remains poorly defined.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level through binding to the 3' untranslated region (3'-UTR) of target mRNAs. Base pairing between mature miRNAs and complementary sequences within target 3'-UTRs recruits the RNA-induced silencing complex (RISC), leading to mRNA degradation or translational repression and suppression of target gene expression across diverse cell types (Satoh, 2012). Given the crucial roles of miRNA-mRNA interactions in many human diseases and their substantial impact on various physiological and pathological processes, identification and validation of physiologically relevant miRNA targets may inform both disease diagnosis and therapeutic development (Wang et al., 2010; Wei et al., 2025). Altered miRNA expression has been documented in multiple neurodegenerative diseases, including AD (Hébert & De Strooper, 2009; Lukiw, 2007; Nelson et al., 2007). However, whether miRNAs regulate Navβ2 expression during AD pathophysiology has not yet been established.

Previous work has shown that hippocampal expression of miR-9 and miR-9* is markedly reduced in SAMP8, and SCN2B, which encodes Navβ2, has been identified among the predicted targets of miR-9 (Liu et al., 2015). The same study also revealed that miR-9 and miR-9* contribute to accelerated brain aging in SAMP8 through regulation of cell cycle-related proteins, MAP3K3, and CDKn1c (Liu et al., 2015). In parallel with the age-related increase in SCN2B expression observed in SAMP8, previous work from our group demonstrated that miR-449a is down-regulated in the prefrontal cortex and hippocampus of SAMP8 and may participate in brain aging through targeting of SCN2B (Tan et al., 2020). More recently, mesenchymal stem cells (MSCs) have been reported to exert neuroprotective effects in Aβ-treated rats through modulation of the lncRNA TUSC7/miR-449a/PPARγ axis and CD36 expression (Pezeshki et al., 2023). Despite these observations, direct evidence linking miR-449a to MCI and AD pathogenesis, particularly cognitive dysfunction, remains lacking.

On the basis of this rationale, aberrant up-regulation of Navβ2 in MCI and AD may be associated with dysregulation of miRNAs, including miR-449a. To test this possibility, MCI and AD mouse models representing different degrees of cognitive decline were used to define the contribution of the miR-449a/SCN2B/Navβ2 axis across the early, progressive, and advanced stages of disease-related cognitive impairment. A multi-lentivirus co-transfection strategy was applied in both models, and the consequences of modulating Navβ2 and miR-449a were evaluated with respect to synaptic structure and function, APP cleavage, and cognitive performance. Clarification of these mechanisms may advance understanding of molecular events that drive cognitive deterioration and may identify candidate targets for prevention and early intervention in AD.

MATERIALS AND METHODS

Animal grouping

Mouse model

The experimental timeline is shown in Supplementary Figure S1. To investigate the potential contribution of Navβ2 across different stages of cognitive decline, three well-established mouse models were employed: A cholinergic lesion-induced MCI model, an amyloid-β oligomer injection model, and a transgenic model of AD (Wang et al., 2024). Specifically, 6-month-old C57BL/6J mice, obtained from Laboratory Animal Center of Kunming Medical University and maintained under standard housing conditions, were used to generate the AF64A-induced MCI and Aβ1-42-injured mouse models, while 12-month-old APPswe/PSΔE9 (APP/PS1) mice served as the AD dementia model. C57BL/6J mice were randomly divided into two cohorts, namely the MCI group and Aβ1-42-injured group. All surgical procedures, including lateral ventricular and bilateral hippocampal injections, were performed as previously described (Li et al., 2022).

Mice in the MCI cohort received the following treatments: Sham surgery (Sham control); MCI model induction (MCI; bilateral hippocampal injection of AF64A (Aladdin, Shanghai, China) at 1 μL/min at a concentration of 0.5 nmol/μL); artificial cerebrospinal fluid (ACSF, Lianmai Reagent, Shanghai, China) injection (ACSF control; bilateral hippocampal ACSF injection at 1 μL/min using the same volume as in the AF64A group); empty lentiviral vector (Empty control; GeneChem, China); Navβ2 overexpression lentivirus (Navβ2-ov; GeneChem, China); Navβ2 siRNA (Navβ2-kd; GeneChem, China); and acetylcholinesterase inhibitor donepezil (DNP) (DNP control; 5 mg/tablet, Pfizer Pharmaceuticals, USA), administered by gavage at 0.4 mg/kg per mouse for 7 days. All Navβ2-related lentiviral constructs were delivered by bilateral intracerebroventricular injection.

Mice in the Aβ1-42-injured group received dimethyl sulfoxide (DMSO control; Solarbio, China), Aβ1-42 oligomer (RoyoBiotech, China; bilateral hippocampal injection of 2 μL of 6 μg/μL Aβ1-42 oligomer solution at 0.5 μL/min), empty lentiviral vector (Aβ1-42+Empty control), Navβ2 overexpression lentivirus (Aβ1-42+Navβ2-ov), and/or Navβ2 siRNA (Aβ1-42+Navβ2-kd).

In the AD cohort, mice were assigned to five groups: Untreated APP/PS1 mice (APP/PS1), age-matched wild-type littermates (WT control), APP/PS1 mice treated with empty lentiviral vector (APP/PS1+Empty control), APP/PS1 mice treated with Navβ2 overexpression lentivirus (APP/PS1+Navβ2-ov), and APP/PS1 mice treated with Navβ2 siRNA (APP/PS1+Navβ2-kd).

To determine whether miR-449a modulates cognitive impairment induced by MCI and AD pathology through targeting Navβ2, additional interventions were performed using lentiviral vectors and/or miRNA sponges, including miR-449a overexpression lentivirus (plenti-miR; Ribobio, China), miR-449a sponge (miR-sponge, miR-RibTM miRNA; Ribobio, China), Navβ2 overexpression lentivirus (Navβ2-ov), and/or Navβ2 siRNA (Navβ2-kd). The group co-treated with miR-449a overexpression lentivirus and Navβ2 overexpression lentivirus was designated plenti-miR+Navβ2-ov. The group co-treated with miR-449a sponge and Navβ2 siRNA was designated miR-449a sponge+Navβ2-kd.

Behavioral tests

Behavioral assessments commenced 7 days after lentiviral injection to evaluate cognitive function.

Novel object recognition (NOR) test

The NOR task was used to assess object recognition and memory, as described previously (Han et al., 2025; Li et al., 2022). The protocol consisted of three phases: Habituation to the testing arena, then a training phase during which mice were exposed to two identical objects for exploration and memorization, and a testing phase in which one familiar object was randomly replaced with a novel object. A 24 h interval was maintained between consecutive phases. Memory performance was quantified using the recognition index (RI) and discrimination index (DI).

Y-maze test

The Y-maze test was conducted to assess spontaneous alternation behavior as an indicator of spatial working memory, according to a previously described method (Gao et al., 2021). Mice were allowed to explore a Plexiglass Y maze freely, and movement trajectories were recorded using the ANY-maze tracking system v.7.0 (Stoelting, USA). The spontaneous alternation ratio, reflecting the frequency of alternating entries into each arm, was calculated automatically.

Morris water maze (MWM)

The MWM task was used to evaluate spatial learning and memory, following established protocols (Xiyang et al., 2016). Briefly, mice underwent six consecutive days of training to locate a hidden platform, and escape latency was recorded daily. On day 7, a 60 s probe trial was conducted after removal of the platform, and time spent in the target quadrant together with the number of platform crossings was analyzed using the Supermaze system v.2.1 (Xinruan, China).

Tissue collection and processing

Seven days after lentiviral injection and prior to behavioral assessment, three mice from each group were randomly selected for preparation of acute hippocampal slices according to established protocols, followed by electrophysiological recording and analysis. An additional five mice per group were randomly assigned to electroencephalographic recording. All remaining mice were subjected to behavioral testing. After completion of behavioral assessment, mice were euthanized by intraperitoneal injection of an overdose of sodium pentobarbital (200 mg/kg, provided by the Laboratory Animal Department of Kunming Medical University), and brains were divided along the sagittal plane for downstream analyses. One hemisphere was immersion-fixed for Golgi staining. From the other hemisphere, hippocampal tissue was rapidly dissected on ice, snap-frozen in liquid nitrogen, and stored at −80℃ for protein and RNA extraction.

Electrophysiological recordings

Electrophysiological recording was performed in hippocampal slices to evaluate synaptic plasticity and neuronal activity patterns in mice subjected to MCI induction, Aβ1-42-induced injury, and/or APP/PS1-related pathology (Gao et al., 2021). Acute transverse hippocampal slices (350 μm) were prepared in ice-cold oxygenated cutting solution. Field excitatory postsynaptic potentials (fEPSPs) were recorded in the stratum radiatum of the CA1 region. After establishment of a stable baseline for 20 min, long-term potentiation (LTP) was induced with a theta-burst stimulation paradigm consisting of four pulses at 100 Hz repeated 10 times at 200 ms intervals.

Electroencephalogram (EEG) recordings

High-resolution EEG recordings were carried out with nanofabricated polyimide-based microelectrode arrays (PBM-array), as described previously (Tan et al., 2020). Under anesthesia, the microelectrode array was implanted epidurally. Following a 7 day recovery period, continuous EEG activity was recorded for 2 h. Power spectral density was then analyzed, and the proportion of time spent in high-frequency activity (>6 Hz) was calculated as described previously (Corbett et al., 2013; Hu et al., 2017).

Golgi staining

Brain tissue from each mouse was processed for Golgi staining with the FD Rapid GolgiStain Kit (FD NeuroTechnologies, USA), following established protocols (Gao et al., 2021; Xiyang et al., 2016). Coronal sections (100 μm) containing the hippocampus were selected for imaging, with three independent sections analyzed per mouse. From each section, one neuron with optimal staining quality and clearly resolved synaptic morphology was selected for analysis. The mean value derived from these three neurons was used as the final measurement for each mouse. Secondary and tertiary dendrites were chosen for quantitative analysis, and Z-stack images of Golgi-stained dendrites were acquired from each mouse at low magnification or at 63× magnification.

Biochemical and molecular analyses

Western blot assay: Western blotting was performed as described previously (Wu et al., 2024). Hippocampal tissue was homogenized in RIPA buffer supplemented with protease inhibitors. Protein samples (30 μg per lane) were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to nitrocellulose membranes, and probed with primary antibodies against Nav1.1α (1:800; cat. no. ASC-001; Alomone, Israel) or Navβ2 (1:500; cat. no. ASC-007; Alomone, Israel). GAPDH (1:5 000; cat. no. 2118; Cell Signaling Technology, USA) served as the loading control. Band intensities were quantified using the Bio-Rad Gel Imaging System (ChemiDoc™ XRS+; Bio-Rad Laboratories, USA) with Quantity One v.4.6.6 (Bio-Rad Laboratories, USA).

Cell-surface biotinylation analysis

Cell-surface biotinylation was performed to determine surface Nav1.1α abundance according to established protocols (Corbett et al., 2013; Hu et al., 2017; Xiyang et al., 2020). Biotinylated proteins were captured with NeutrAvidin-agarose beads (Pierce, USA) and eluted by incubation with SDS-PAGE sample buffer at 37°C for 60 min. Eluted proteins were subsequently analyzed by SDS-PAGE and western blotting, as described above. Bead-bound biotinylated fractions were used to assess extracellular protein expression, while the remaining non-biotinylated lysate was used for analysis of intracellular proteins.

Enzyme-linked immunosorbent assay (ELISA)

ELISA was performed following previous research (Li et al., 2024). Levels of Aβ40, Aβ42 (cat. no. JP27718 for Aβ40, cat. no. JP27719 for Aβ42; Demeditec Diagnostics GmbH, Germany), sAPPα, and sAPPβ (cat. no. K15120E; 96-well MULTI-SPOT sAPPα/sAPPβ Assay; Meso Scale Discovery, USA) were measured in hippocampal homogenates according to the manufacturer’s protocols.

RT-qPCR assay

miR-449a was quantified by RT-qPCR (Li et al., 2025). Total RNA was extracted from tissue samples with TRIzol® reagent (Gibco; Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. RNA concentration was determined with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA). cDNA was synthesized with gene-specific primers using the TaqMan MicroRNA Assay (Applied Biosystems; Thermo Fisher Scientific, USA). Each 20 μL PCR reaction contained 2×PCR Master Mix (Roche Diagnostics, Switzerland), 0.25 μL of primer (10 pmol/L), 1 μL of cDNA, and 8.5 μL of nuclease-free water. Primer sequences were as follows: miR-449a forward, 5'-AATCCGTCTCCCTAACTCTAGGCTT-3'; reverse, 5'-GCTGTCAACGATACGCTACGTAACG-3' (Takara Biotechnology, Japan). Thermocycling was performed at 95℃ for 3 min, followed by 40 cycles at 94℃ for 35 s, 56.5℃ for 30 s, and 72℃ for 30 s. U6 snRNA was used as the endogenous control. Relative gene expression was determined using the 2-ΔΔCq method. Each experiment was repeated three times.

Statistical analysis

Data are presented as mean±standard deviation (SD). Statistical analyses were performed using Student’s t-test, one-way analysis of variance (ANOVA), or two-way ANOVA in GraphPad Prism v.9.5 (https://www.graphpad.com/updates/prism-950-release-notes, GraphPad Software, USA). Statistical significance was defined as P<0.05. Detailed information for each analysis is provided in the corresponding figure legends.

RESULTS

Lentivirus-mediated modulation of Navβ2 and Navβ2-CTF protein levels in MCI and AD model mice

To confirm effective lentiviral transduction and determine its impact on Navβ2 and the corresponding C-terminal fragment (Navβ2-CTF), hippocampal protein abundance was measured by western blot 7 days post-transfection in MCI, APP/PS1, and Aβ1-42-injured mice. No significant difference in Navβ2 or Navβ2-CTF abundance was detected between the Sham and ACSF control groups (P>0.05, Figure 1A, D). In contrast, the three disease-model groups (MCI, APP/PS1, and Aβ1-42) together with the matched Empty vector controls exhibited significantly higher Navβ2 and Navβ2-CTF levels compared to their respective non-pathological controls (ACSF, WT, and DMSO; P<0.05, Figure 1A–F). Lentiviral-mediated knockdown of Navβ2 (Navβ2-kd) markedly reduced both proteins relative to the respective Empty control groups (P<0.05, Figure 1A–F), whereas Navβ2-ov further increased their abundance (P<0.05, Figure 1A–F). In MCI mice, DNP treatment also significantly reduced hippocampal Navβ2 and Navβ2-CTF levels (P<0.05, Figure 1A, D). These results confirm stable lentivirus-mediated up-regulation or down-regulation of Navβ2 and the associated proteolytic fragment across all three AD-related models.

Figure 1.

Figure 1

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Lentiviral modulation of Navβ2 and Navβ2-CTF protein levels in AD mice

A–C: Representative immunoblots showing Navβ2 and Navβ2-CTF expression in hippocampal tissue from MCI (A), APP/PS1 (B), Aβ1-42 (C) model mice after lentiviral transfection (n=14). D–F: Quantification of Navβ2 and Navβ2-CTF protein levels in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after lentiviral transfection (n=14). MCI group: Navβ2 F=252.167, Navβ2-CTF F=74.454, df=97; APP/PS1 group: Navβ2 F=167.856, Navβ2-CTF F=141.832, df=69; Aβ1-42 group: Navβ2 F=327.777, Navβ2-CTF F=92.488, df=69. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; ***: P<0.001. Empty control: Mice received empty lentiviral vector; Navβ2-ov: Navβ2 overexpression; Navβ2-kd: Navβ2 knockdown.

Effects of Navβ2 modulation on cognition

Cognitive performance was assessed using the NOR test, Y-maze spontaneous alternation test, and MWM task. Before lentiviral manipulation, all disease-model groups (MCI, APP/PS1, and Aβ1-42), together with the corresponding Empty control groups, displayed marked cognitive deficits relative to the matched healthy controls. These impairments were reflected by lower RI and DI values in the NOR test (P<0.05, Figure 2A–C), reduced spontaneous alternation in the Y-maze test (P<0.05, Figure 2D–F), prolonged escape latencies during MWM training (P<0.05, Figure 2G–I), and decreased target-quadrant residence time and platform-crossing number during the probe trial (P<0.05, Figure 2G–I).

Figure 2.

Figure 2

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Effects of Navβ2 on cognitive function in AD model mice

A–C: Recognition index (RI) and discrimination index (DI) in novel object recognition (NOR) test in MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after Navβ2 lentiviral transfection (n=14). MCI group: F=127.061, df=97; APP/PS1 group: F=173.428, df=69; Aβ1-42 group: F=126.264, df=69. D–F: Spontaneous alternation rate in Y-maze test in MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after Navβ2 lentiviral transfection (n=14). MCI group: F=43.765, df=97; APP/PS1 group: F=59.894, df=69; Aβ1-42 group: F=79.060, df=69. G: Escape latency, time in target quadrant, and number of target crossings in Morris water maze (MWM) task in MCI mice after Navβ2 lentiviral transfection (n=14). Time in target quadrant: F=28.793; number of target crossings: F=8.553, df=97. *: Compared to ACSF control, P<0.05; #: Compared to MCI+Empty control, P<0.05; &: Compared to MCI+Navβ2-ov, P<0.05. H: Escape latency, time in target quadrant, and number of target crossings in MWM task in APP/PS1 mice after Navβ2 lentiviral transfection (n=14). Time in target quadrant: F=53.757; number of target crossings: F=7.488, df=69. *: Compared to WT control, P<0.05; #: Compared to APP/PS1+Empty control, P<0.05; &: Compared to APP/PS1+Navβ2-ov, P<0.05. I: Escape latency, time in target quadrant, and number of target crossings in MWM task in Aβ1-42 mice after Navβ2 lentiviral transfection (n=14). Time in target quadrant: F=17.819; number of target crossings: F=10.817, df=69. *: Compared to DMSO control, P<0.05; #: Compared to Aβ1-42 group, P<0.05; &: Compared to Aβ1-42+Navβ2-ov, P<0.05. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test. Escape latency data (G–I) were analyzed using a general linear model for repeated-measures data followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Navβ2 knockdown (Navβ2-kd) markedly attenuated these behavioral deficits. In the NOR test, RI and DI values were significantly increased in Navβ2-kd groups relative to the respective Empty controls (P<0.05, Figure 2A–C). In the Y-maze test, spontaneous alternation was significantly elevated in the APP/PS1+Navβ2-kd and Aβ1-42+Navβ2-kd groups compared to their corresponding Empty controls, whereas the MCI+Navβ2-kd group showed a significant increase relative to the MCI group (P<0.05, Figure 2D–F). In the MWM task, Navβ2-kd treatment shortened escape latency and improved spatial memory performance (P<0.05, Figure 2G–I). In contrast, Navβ2-ov aggravated cognitive dysfunction and further impaired performance across all behavioral paradigms (P<0.05, Figure 2A–I). Notably, the degree of cognitive improvement observed in the MCI+Navβ2-kd group was comparable to that observed in the DNP-treated positive control group (Figure 2).

Effects of Navβ2 on APP metabolism

ELISA-based quantification of APP metabolites in hippocampal tissues showed that the MCI, APP/PS1, and Aβ1-42 groups, together with the corresponding Empty controls, displayed a clear shift toward amyloidogenic processing. Relative to healthy controls, these groups exhibited reduced sAPPα levels and lower sAPPα/sAPPβ ratios, accompanied by increased sAPPβ abundance, Aβ42 levels, and Aβ42/Aβ40 ratios (P<0.05, Figures 3, 4). Navβ2-ov further intensified this amyloidogenic profile, as indicated by additional reductions in the sAPPα/sAPPβ ratio and further increases in the Aβ42/Aβ40 ratio (P<0.05, Figures 3, 4). In contrast, Navβ2-kd shifted APP processing in the opposite direction, increasing the sAPPα/sAPPβ ratio and decreasing the Aβ42/Aβ40 ratio (P<0.05, Figures 3, 4). In the APP/PS1+Navβ2-kd and Aβ1-42+Navβ2-kd groups, sAPPα levels were increased and Aβ40 levels were decreased (P<0.05, Figures 3, 4). No significant differences in these markers were detected between the MCI+Navβ2-kd and DNP control groups (P>0.05, Figures 3, 4).

Figure 3.

Figure 3

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Effects of Navβ2 on sAPPα and sAPPβ levels in AD model mice

A–C: Quantification of sAPPα levels in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice (n=14). MCI group: F=43.445, df=97; APP/PS1 group: F=296.444, df=69; Aβ1-42 group: F=44.156, df=69. D–F: Quantification of sAPPβ in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice (n=14). MCI group: F=200.458, df=97; APP/PS1 group: F=231.403, df=69; Aβ1-42 group: F=113.517, df=69. G–I: sAPPα/sAPPβ ratios in MCI (G), APP/PS1 (H), and Aβ1-42 (I) model mice (n=14). MCI group: F=122.169, df=97; APP/PS1 group: F=388.461, df=69; Aβ1-42 group: F=138.932, df=69. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Figure 4.

Figure 4

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Effects of Navβ2 on Aβ42 and Aβ40 levels in AD model mice

A–C: Quantification of Aβ42 levels in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice (n=14). MCI group: F=586.958, df=97; APP/PS1 group: F=2 212.798, df=69; Aβ1-42 group: F=988.460, df=69. D–F: Quantification of Aβ40 levels in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice (n=14). MCI group: F=66.999, df=97; APP/PS1 group: F=5 990.583, df=69; Aβ1-42 group: F=338.164, df=69. G–I: Aβ42/Aβ40 ratios in MCI (G), APP/PS1 (H), and Aβ1-42 (I) model mice (n=14). MCI group: F=723.010, df=97; APP/PS1 group: F=339.564, df=69; Aβ1-42 group: F=405.986, df=69. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; **: P<0.01; ***: P<0.001.

Effects of Navβ2 on dendrite morphology

Golgi staining was performed to evaluate dendritic architecture in hippocampal neurons. All disease-model groups and the matched Empty control groups exhibited marked reductions in both apical and basal dendritic arborization compared to healthy controls, consistent with synaptic structural loss (P<0.05, Figure 5A–F). Navβ2-ov further aggravated dendritic degeneration, with particularly prominent reductions in apical dendrite counts in the MCI and APP/PS1 models (P<0.05, Figure 5D, E). In contrast, Navβ2-kd promoted recovery of dendritic structure. Basal dendrite number increased significantly in the APP/PS1 and Aβ1-42 models (P<0.05, Figure 5E, F), and apical dendrite number increased in the Aβ1-42+Navβ2-kd group (P<0.05, Figure 5C, F). In the MCI+Navβ2-kd group, dendritic morphology did not differ significantly from that in the DNP-treated control group (P>0.05, Figure 5A, D).

Figure 5.

Figure 5

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Effects of Navβ2 on dendrite number in AD model mice

A–C: Representative Golgi-stained images of basal and apical dendrites in hippocampal neurons from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after Navβ2 lentiviral transfection (n=14). D–F: Quantification of basal and apical dendrites in hippocampal neurons from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after Navβ2 lentiviral transfection (n=14). MCI group: Basal dendrites F=15.454, apical dendrites F=19.006, df=97; APP/PS1 group: Basal dendrites F=17.235, apical dendrites F=20.815, df=69; Aβ1-42 group: Basal dendrites F=11.325, apical dendrites F=18.079, df=69. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Regulation of Nav1.1α surface expression by Navβ2

Cell-surface biotinylation combined with western blot analysis was used to distinguish surface-localized Nav1.1α from total Nav1.1α protein. In all disease-model groups, surface Nav1.1α levels were significantly reduced, whereas total Nav1.1α levels were increased relative to control groups (P<0.05, Figure 6A–F), indicating aberrant subcellular distribution of this channel subunit. Navβ2-ov further decreased surface Nav1.1α abundance and increased total Nav1.1α levels (P<0.05, Figure 6A–F). In contrast, Navβ2-kd restored Nav1.1α surface expression and reduced total cellular accumulation (P<0.05, Figure 6A–F). In the MCI+Navβ2-kd group, Nav1.1α levels were comparable to those in the DNP-treated control group (P>0.05, Figure 6A, D).

Figure 6.

Figure 6

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Effects of Navβ2 on surface and total Nav1.1α levels in AD model mice

A–C: Representative immunoblots and quantification of surface Nav1.1α in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after Navβ2 lentiviral transfection (n=14). MCI group: F=42.797, df=97; APP/PS1 group: F=189.845, df=69; Aβ1-42 group: F=85.683, df=69. D–F: Representative immunoblots and quantification of total Nav1.1α in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after Navβ2 lentiviral transfection (n=14). MCI group: F=49.129, df=97; APP/PS1 group: F=163.809, df=69; Aβ1-42 group: F=115.551, df=69. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Effects of Navβ2 on synaptic plasticity and network excitability

fEPSP slope was analyzed to evaluate LTP, and EEG recordings were used to evaluate neuronal network activity. All AD model groups exhibited marked impairment of LTP, as shown by reduced mean fEPSP slope change (P<0.05, Figure 7A–C). Navβ2-ov produced a further decline in LTP, whereas Navβ2-kd restored synaptic potentiation (P<0.05, Figure 7A–C). EEG analysis further revealed increased abnormal high-frequency activity in the AD models, reflected by elevated time in frequency (%) values (P<0.05, Figure 7D–F). This abnormal activity was intensified by Navβ2-ov and attenuated by Navβ2-kd (P<0.05, Figure 7D–F). In the MCI+Navβ2-kd group, both LTP and EEG profiles were comparable to those of the DNP-treated control group (P>0.05, Figure 7A, D).

Figure 7.

Figure 7

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Effects of Navβ2 on hippocampal LTP and EEG activity in AD model mice

A–C: Representative traces and quantification of changes in mean fEPSP slope (%) in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after Navβ2 lentiviral transfection (n=3). HFS: High-frequency stimulation. MCI group: F=314.354, df=20; APP/PS1 group: F=439.435, df=14; Aβ1-42 group: F=498.411, df=14. D–F: Quantification of time in frequency (%) in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after Navβ2 lentiviral transfection (n=6). MCI group: F=77.619, df=41; APP/PS1 group: F=96.977, df=29; Aβ1-42 group: F=32.851, df=29. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

miR-449a regulates Navβ2 expression

Co-transfection experiments were performed to validate the regulatory relationship between miR-449a and Navβ2. As shown in Supplementary Figure S2, endogenous miR-449a abundance was significantly reduced in the MCI, APP/PS1, and Aβ1-42 groups compared to their respective control groups (ACSF, WT, and DMSO) (P<0.05, Supplementary Figure S2). Furthermore, miR-449a overexpression (plenti-miR) significantly increased miR-449a levels and was accompanied by significant reductions in Navβ2 and Navβ2-CTF protein levels (P<0.05, Figure 8A–F). In contrast, miR-449a inhibition (miR-sponge) reduced miR-449a abundance and increased Navβ2/Navβ2-CTF levels (P<0.05, Figure 8A–F). These regulatory effects were bidirectionally reversible. The decrease in Navβ2 induced by plenti-miR was abolished by concomitant Navβ2-ov, whereas the increase induced by miR-sponge was reversed by concomitant Navβ2-kd (P<0.05, Figure 8D–F). Alteration of Navβ2 expression did not affect miR-449a abundance.

Figure 8.

Figure 8

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Changes in miR-449a, Navβ2, and Navβ2-CTF levels after multi-lentiviral co-transfection in AD model mice

A–C: Quantification of miR-449a expression in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after multi-lentiviral co-transfection (n=14). MCI group: F=193.727, df=97; APP/PS1 group: F=504.261, df=97; Aβ1-42 group: F=330.970, df=97. D–F: Representative immunoblots and quantification of Navβ2 and Navβ2-CTF expression in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after multi-lentiviral co-transfection (n=14). MCI group: Navβ2 F=406.284, Navβ2-CTF F=152.761, df=97; APP/PS1 group: Navβ2 F=487.273, Navβ2-CTF F=359.311, df=97; Aβ1-42 group: Navβ2 F=519.596, Navβ2-CTF F=202.408, df=97. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; ***: P<0.001. Mock control: Mice received empty viral vector; plenti-miR: miR-449a overexpression; miR-sponge: miR-449a inhibition.

miR-449a modulates cognitive function via Navβ2

Behavioral analyses after dual lentiviral co-transfection demonstrated that the cognitive effects of miR-449a were mediated through Navβ2. Notably, miR-449a overexpression improved performance in both the NOR and Y-maze tests, whereas miR-449a inhibition worsened behavioral performance. Specifically, compared with the corresponding Mock control, the plenti-miR groups showed significant increases in RI and DI values, and spontaneous alternation also increased in the APP/PS1+plenti-miR and Aβ1-42+plenti-miR groups. In contrast, RI and DI values were reduced in the MCI+miR-sponge group, and spontaneous alternation declined across all miR-sponge groups (P<0.05, Figure 9A–F). These behavioral effects were reversed when miR-449a modulation was combined with opposing manipulation of Navβ2, including plenti-miR+Navβ2-ov and miR-sponge+Navβ2-kd (P<0.05, Figure 9). Results from MWM training and probe trials further corroborated these findings, showing that the beneficial effects of plenti-miR and the detrimental effects of miR-sponge or Navβ2-ov were negated by concurrent modulation of the corresponding opposing component (Figure 9G–I). Across all behavioral measures, the combined treatment groups, plenti-miR+Navβ2-ov and miR-sponge+Navβ2-kd, did not differ significantly from the Mock control groups (P>0.05, Figure 9).

Figure 9.

Figure 9

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Changes in cognitive function after multi-lentiviral co-transfection in AD model mice

A–C: Recognition index (RI) and discrimination index (DI) in the NOR test in MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after multi-lentiviral co-transfection (n=14). MCI group: F=59.417, df=97; APP/PS1 group: F=39.257, df=97; Aβ1-42 group: F=37.838, df=97. D–F: Spontaneous alternation rate in the Y-maze test in MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after multi-lentiviral co-transfection (n=14). MCI group: F=35.268, df=97; APP/PS1 group: F=89.759, df=97; Aβ1-42 group: F=79.216, df=97. G: Escape latency, time in target quadrant, and number of target crossings in the MWM task in MCI mice after multi-lentiviral co-transfection (n=14). Time in target quadrant F=26.101; number of target crossings F=5.876, df=97. *: Compared to MCI+Mock control, P<0.05; #: Compared to MCI+plenti-miR+Navβ2-ov, P<0.05; &: Compared to MCI+miR-sponge+Navβ2-kd, P<0.05. H: Escape latency, time in target quadrant, and number of target crossings in the MWM task in APP/PS1 mice after multi-lentiviral co-transfection (n=14). Time in target quadrant F=21.983; number of target crossings F=6.702, df=97. *: Compared to APP/PS1+Mock control, P<0.05; #: Compared to APP/PS1+plenti-miR+Navβ2-ov, P<0.05; &: Compared to APP/PS1+miR-sponge+Navβ2-kd, P<0.05. I: Escape latency, time in target quadrant, and number of target crossings in the MWM task in Aβ1-42 mice after multi-lentiviral co-transfection (n=14). Time in target quadrant F=8.932; number of target crossings F=5.298, df=97. *: Compared to Aβ1-42+Mock control, P<0.05; #: Compared to Aβ1-42+plenti-miR+Navβ2-ov, P<0.05; &: Compared to Aβ1-42+miR-sponge+Navβ2-kd, P<0.05. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test. Escape latency data (G–I) were analyzed using a general linear model for repeated-measures data followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

miR-449a influences APP metabolism via Navβ2

The effects of miR-449a on APP processing were dependent on Navβ2. Notably, miR-449a overexpression shifted APP metabolism toward the non-amyloidogenic pathway, as reflected by an increased sAPPα/sAPPβ ratio and a reduced Aβ42/Aβ40 ratio, whereas miR-449a inhibition promoted the opposite pattern and favored amyloidogenic processing (P<0.05, Figures 10, 11). These metabolic changes were abolished by concurrent, opposing manipulation of Navβ2. Specifically, Navβ2-ov attenuated the favorable effects of plenti-miR, whereas Navβ2-kd counteracted the adverse effects induced by miR-sponge (P<0.05, Figures 10, 11). In both dual-treatment groups, plenti-miR+Navβ2-ov and miR-sponge+Navβ2-kd, APP metabolic profiles were indistinguishable from those of the Mock controls (P>0.05, Figures 10,11).

Figure 10.

Figure 10

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Changes in sAPPα, sAPPβ, and sAPPα/sAPPβ ratios after multi-lentiviral co-transfection in AD model mice

A–C: Quantification of sAPPα levels in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice (n=14). MCI group: F=94.924, df=97; APP/PS1 group: F=95.885, df=97; Aβ1-42 group: F=32.816, df=97. D–F: Quantification of sAPPβ levels in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice (n=14). MCI group: F=94.229, df=97; APP/PS1 group: F=83.107, df=97; Aβ1-42 group: F=66.426, df=97. G–I: sAPPα/sAPPβ ratios in MCI (G), APP/PS1 (H), and Aβ1-42 (I) model mice (n=14). MCI group: F=62.395, df=97; APP/PS1 group: F=65.989, df=97; Aβ1-42 group: F=37.502, df=97. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Figure 11.

Figure 11

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Changes in Aβ42, Aβ40, and Aβ42/Aβ40 ratios after multi-lentiviral co-transfection in AD model mice

A–C: Quantification of Aβ42 levels in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice (n=14). MCI group: F=588.286, df=97; APP/PS1 group: F=1 155.502, df=97; Aβ1-42 group: F=794.818, df=97. D–F: Quantification of Aβ40 levels in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice (n=14). MCI group: F=28.290, df=97; APP/PS1 group: F=161.429, df=97; Aβ1-42 group: F=21.984, df=97. G–I: Aβ42/Aβ40 ratios in MCI (G), APP/PS1 (H), and Aβ1-42 (I) model mice (n=14). MCI group: F=475.645, df=97; APP/PS1 group: F=270.492, df=97; Aβ1-42 group: F=76.031, df=97. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

miR-449a affects dendritic complexity via Navβ2

miR-449a overexpression increased dendritic complexity, whereas miR-449a inhibition reduced it. Specifically, all plenti-miR groups exhibited increased numbers of basal dendrites relative to the corresponding Mock controls. In addition, the APP/PS1+plenti-miR and Aβ1-42+plenti-miR groups showed increased apical dendrites. In contrast, all miR-sponge groups displayed significant reductions in apical dendrite number compared with the Mock controls (P<0.05, Figure 12A–F). Co-transfection experiments further demonstrated that these structural effects were mediated by Navβ2. The increase in dendrites induced by plenti-miR was diminished by concurrent Navβ2-ov (plenti-miR+Navβ2-ov), whereas the dendritic deficits induced by miR-sponge were partially reversed by simultaneous Navβ2-kd (miR-sponge+Navβ2-kd). Accordingly, all plenti-miR+Navβ2-ov groups showed fewer basal dendrites than the matched plenti-miR groups, and the APP/PS1+plenti-miR+Navβ2-ov and Aβ1-42+plenti-miR+Navβ2-ov groups also exhibited fewer apical dendrites. Conversely, the MCI+miR-sponge+Navβ2-kd and Aβ1-42+miR-sponge+Navβ2-kd groups displayed higher apical dendrite numbers than the corresponding miR-sponge controls (P<0.05, Figure 12A–F).

Figure 12.

Figure 12

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Changes in dendrite number after multi-lentiviral co-transfection in AD model mice

A–C: Representative Golgi-stained images of basal and apical dendrites in hippocampal neurons from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after multi-lentiviral co-transfection (n=14). D–F: Quantification of basal and apical dendrites in hippocampal neurons from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after multi-lentiviral co-transfection (n=14). MCI group: Basal dendrites F=9.623, apical dendrites F=15.549, df=97; APP/PS1 group: Basal dendrites F=11.919, apical dendrites F=21.295, df=97; Aβ1-42 group: Basal dendrites F=11.176, apical dendrites F=21.948, df=97. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

miR-449a regulates Nav1.1α distribution via Navβ2

miR-449a overexpression increased surface Nav1.1α expression in the MCI and APP/PS1 models while reducing total Nav1.1α levels, whereas miR-449a inhibition produced the opposite effect (P<0.05, Figure 13A–F). These changes were reversed by concomitant modulation of Navβ2. In the dual-treatment groups, plenti-miR+Navβ2-ov and miR-sponge+Navβ2-kd, both surface and total Nav1.1α levels returned to values indistinguishable from those in the Mock controls (P>0.05, Figure 13A–F).

Figure 13.

Figure 13

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Changes in surface and total Nav1.1α levels after multi-lentiviral co-transfection in AD model mice

A–C: Representative immunoblots and quantification of surface Nav1.1α in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after multi-lentiviral co-transfection (n=14). MCI group: F=275.989, df=97; APP/PS1 group: F=486.760, df=97; Aβ1-42 group: F=168.120, df=97. D–F: Representative immunoblots and quantification of total Nav1.1α in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after multi-lentiviral co-transfection (n=14). MCI group: F=341.232, df=97; APP/PS1 group: F=357.947, df=97; Aβ1-42 group: F=273.728, df=97. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; ***: P<0.001.

miR-449a modulates synaptic plasticity and EEG activity via Navβ2

The enhancement of LTP and the reduction in abnormal EEG activity induced by miR-449a overexpression were abolished by simultaneous Navβ2-ov (P<0.05, Figure 14). Conversely, the impairments induced by miR-449a inhibition were reversed by simultaneous Navβ2-kd (P<0.05, Figure 14). Electrophysiological parameters in the dual-treatment groups did not differ significantly from those in the Mock controls (P>0.05, Figure 14).

Figure 14.

Figure 14

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Changes in LTP and EEG activity after multi-lentiviral co-transfection in AD model mice

A–C: Representative traces and quantification of changes in mean fEPSP slope (%) in hippocampal tissue from MCI (A), APP/PS1 (B), and Aβ1-42 (C) model mice after multi-lentiviral co-transfection (n=3). HFS: High-frequency stimulation. MCI group: F=153.045, df=20; APP/PS1 group: F=50.325, df=20; Aβ1-42 group: F=176.549, df=20. D–F: Quantification of time in frequency (%) in hippocampal tissue from MCI (D), APP/PS1 (E), and Aβ1-42 (F) model mice after multi-lentiviral co-transfection (n=5). MCI group: F=36.597, df=34; APP/PS1 group: F=17.348, df=34; Aβ1-42 group: F=11.490, df=34. Data are presented as mean±SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s multiple-comparisons test; *: P<0.05; **: P<0.01; ***: P<0.001.

Collectively, results from single and dual lentiviral manipulation across molecular, structural, functional, and behavioral assays consistently indicate that miR-449a exerts neuroprotective effects in AD models primarily through direct post-transcriptional regulation of Navβ2.

DISCUSSION

The transition from MCI to established AD dementia unfolds progressively, underscoring the need to define the underlying molecular events that drive this pathological trajectory and to identify effective interventions (Chen & Zhang, 2022). The present study investigated the contribution of Navβ2 and the upstream regulatory miRNA miR-449a in multiple mouse models of MCI and AD, thereby providing mechanistic insight into the pathophysiology of cognitive decline.

Navβ2 expression was aberrantly regulated in the hippocampus of both MCI and AD model mice. This dysregulation was associated with structural and functional synaptic deficits, including reduced dendritic complexity, increased neuronal hyperexcitability, and impaired LTP, a core electrophysiological substrate of memory formation. In parallel, elevated Navβ2 redirected APP metabolism toward the amyloidogenic pathway, as evidenced by reduced sAPPα, increased sAPPβ and Aβ42, and dysregulated Nav1.1α distribution. These findings are consistent with previous studies implicating Navβ2 dysfunction in worsening cognitive decline during AD progression (Corbett et al., 2013; Hu et al., 2017; Xiyang et al., 2016).

Roles of miR-449a/Navβ2 axis in AD-associated pathology

Voltage-gated sodium channel subunits, including Navβ2, have been linked to neural hyperexcitability and cognitive dysfunction in AD (Corbett et al., 2013; Hu et al., 2019; Kim et al., 2007). In the AD setting, pathological elevation of Navβ2 is accompanied by excessive proteolytic processing by BACE1 and γ-secretase. The resulting cleavage products disrupt membrane localization of Nav1.1α, thereby promoting abnormal sodium influx and aberrant EEG activity (Bondy & Cheng, 2004; Corbett et al., 2013; Engel et al., 2006). Consistent with previous reports (Hu et al., 2019; Li et al., 2022), Navβ2-kd partially normalized EEG activity, shifted APP processing away from the amyloidogenic route, and improved learning and memory performance in AD models. Taken together, these observations support a model in which pathological Navβ2 elevation induced by cholinergic injury, APP/PS1 mutation, or Aβ1-42 exposure contributes directly to synaptic dysfunction, disturbed APP metabolism, neuronal hyperexcitability, and progressive cognitive decline.

Current findings further showed that the effects of miR-449a on synaptic integrity and cognitive function were largely mediated through Navβ2. Overexpression of miR-449a reduced Navβ2 abundance, improved dendritic architecture, enhanced LTP, and ameliorated behavioral impairment. In contrast, miR-449a inhibition increased Navβ2 expression and exacerbated both synaptic and cognitive deficits. Critically, dual-lentiviral transfection attenuated the protective effects produced by miR-449a overexpression and counteracted the adverse effects associated with Navβ2 dysregulation, indicating that the functional consequences of miR-449a in these models were predominantly executed through Navβ2. In parallel, miR-449a overexpression shifted APP metabolism toward the non-amyloidogenic pathway, whereas concomitant Navβ2 overexpression abolished this effect. EEG analyses provided additional support for a central role of the miR-449a/Navβ2 axis in the regulation of network excitability and cognition in AD models (Supplementary Figure S3). These results are consistent with previous reports showing widespread miRNA dysregulation in both AD and MCI (Yoon et al., 2022). To the best of our knowledge, this study provides the first evidence that concurrent reduction of miR-449a and elevation of Navβ2 contribute to synaptic impairment, altered APP processing, neuronal hyperexcitability, and cognitive deterioration across MCI and AD models.

Beyond neurodegeneration, miR-449a has been implicated in a broad spectrum of human disorders, including renal interstitial fibrosis, retinal degeneration, cancer, and transplant rejection, underscoring extensive regulatory capacity across pathological contexts (Cao et al., 2023; Fakılı et al., 2023; Fang et al., 2023; Guo et al., 2023; Komoto et al., 2023). miR-449a also participates in cellular senescence and age-related processes (Noureddine et al., 2023). Within this framework, the present study identified miR-449a down-regulation as a mechanistic contributor to synaptic and cognitive deficits through loss of repression of Navβ2, thereby highlighting the miR-449a/Navβ2 axis as a promising target for early intervention in MCI and AD.

The miR-449a/Navβ2 signaling axis identified here may intersect with APP metabolism through several biologically plausible mechanisms that converge on AD pathogenesis. First, Navβ2 is an established substrate of both BACE1 and γ-secretase (Kim et al., 2007). Pathological Navβ2 accumulation may therefore influence APP cleavage through substrate competition. Under AD-related conditions, increased BACE1 activity may enhance proteolysis of both Navβ2 and APP in parallel. Our finding that Navβ2-ov reduced sAPPα while increasing sAPPβ and Aβ42 is consistent with the possibility that excess Navβ2 may sequester or modulate BACE1 activity or localization in a manner that favors amyloidogenic APP processing. Conversely, suppression of Navβ2 by miR-449a may lessen substrate burden on BACE1, potentially freeing the enzyme or rebalancing proteolytic activity toward other substrates and favoring non-amyloidogenic APP processing. Second, as a regulatory transmembrane protein, Navβ2 may influence the composition and functional organization of membrane-associated protein complexes, including secretase-containing assemblies. Existing evidence suggests that sodium channel subunits and secretases can co-localize within specific membrane microdomains. Regulation of Navβ2 expression and localization by miR-449a may therefore modify the local membrane environment or the assembly efficiency of BACE1/γ-secretase complexes at the neuronal surface, leading to broader effects on APP metabolism.

In summary, the miR-449a/Navβ2 axis may function as a mechanistic link between neuronal hyperexcitability and core molecular pathology in AD, particularly Aβ production. Through post-transcriptional repression of Navβ2, this axis modulates neuronal excitability and synaptic plasticity and may also influence APP processing balance by affecting BACE1 substrate availability and activity. This dual mechanism provides a novel perspective on how network dysfunction and molecular pathology may synergistically exacerbate disease progression in AD.

Study limitations and future directions

Although the present findings provide mechanistic insight into the contribution of miR-449a and Navβ2 to AD-related pathology, several limitations should be acknowledged. First, all experiments were conducted in mouse models. While these systems recapitulate key features of AD, the relevance of the identified mechanisms to human disease will require validation in postmortem brain tissue and well-characterized longitudinal clinical cohorts. Second, miRNAs typically regulate multiple downstream targets. Although the co-transfection results support Navβ2 as a major effector of miR-449a activity in this setting, contributions from additional miR-449a-regulated pathways cannot be excluded. Third, the current analysis focused on hippocampal dysfunction, and it remains unclear whether similar regulatory relationships operate in other vulnerable regions, including the cortex and entorhinal cortex. Fourth, while this study examined synaptic, electrophysiological, and APP-related endpoints, additional pathways linking Navβ2 to tau pathology or neuroinflammation were not explored. Finally, potential sex-dependent differences in miR-449a/Navβ2 signaling were not investigated, despite the recognized sex bias in AD prevalence.

Future studies should extend these observations through validation of miR-449a and Navβ2 dysregulation in human CSF and brain samples from individuals with MCI or AD, application of single-cell sequencing to identify cell type-specific regulatory networks involving this axis, and development of pharmacological or gene-based strategies to modulate miR-449a/Navβ2 signaling in vivo with assessment of long-term therapeutic efficacy. It will also be important to determine how Navβ2-dependent changes in neuronal excitability interact with other major pathological features of AD, such as tau phosphorylation and glial activation.

CONCLUSION

In summary, this study identified a previously unrecognized pathway through which miR-449a regulates Navβ2 to influence synaptic integrity, APP metabolism, neuronal excitability, and cognitive function in models of MCI and AD. These findings establish the miR-449a/Navβ2 axis as a candidate therapeutic target and further emphasize the importance of defining the molecular basis of synaptic failure during AD progression.

SUPPLEMENTARY DATA

Supplementary data to this article can be found online.

zr-47-3-765-S1.pdf (1.5MB, pdf)

Acknowledgments

COMPETING INTERESTS

The authors declare that they have no competing interests.

AUTHORS’ CONTRIBUTIONS

All authors contributed to study conception and design. Conceptualization: G.J.Y., X.Y.S., M.N.L., D.W., C.D.Y., X.W.L., Y.J.Z., T.H., and Y.B.X.; Data curation: G.J.Y., X.Y.S., C.J.Y., S.S.L., X.Y.W., and T.H.; Formal analysis and investigation: M.N.L., D.W., X.W.Y., X.Y.W., C.D.Y., and X.W.L.; Methodology: M.N.L., C.J.Y., S.S.L., and Y.J.Z.; Project administration: Y.B.X. and T.H.; Supervision: Y.B.X. and X.W.Y.; Validation and visualization: G.J.Y., X.Y.S., and Y.B.X.; Writing—original draft: G.J.Y., X.Y.S., and Y.B.X.; Writing—review and editing: Y.B.X., X.W.Y., and X.Y.S.; Funding acquisition: Y.B.X. and X.W.Y. All authors commented on previous versions of the manuscript. All authors read and approved the final version of the manuscript.

ACKNOWLEDGMENTS

We thank the funding agencies and other members of the laboratory for their support and assistance in this work.

Funding Statement

This work was supported by the National Natural Science Foundation of China (82560226, 82371468, 32360138, 81760648), First-Class Discipline Team of Pathology of Kunming Medical University (2024XKTDTS11), Yunnan Fundamental Research Projects (202401AT070172), and Scientific Research Fund Project of Education Department of Yunnan Province (2023Y0778)

Contributor Information

Xin-Wang Yang, Email: yangxinwanghp@163.com.

Tao Hu, Email: 18134318@qq.com.

Yan-Bin Xiyang, Email: xiyangyanbin@kmmu.edu.cn.

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