The LRRK2 P1446L mutation triggers dopaminergic neurodegeneration via DAPK1-mediated microglial neuroinflammation and neuronal apoptosis

Ding, Liuyan; Shu, Hui; Chen, Minshan; Liang, Fengchu; Gan, Tingting; Huang, Xingting; Liang, Xiaolei; Luo, Kangting; Qiu, Linfeng; Huang, Weiqing; Zhu, Xiaoqin; Huang, Xiaoyun; Zhang, Wenlong; Xu, Pingyi

doi:10.1038/s41531-025-01234-2

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Published: 21 December 2025

The LRRK2 P1446L mutation triggers dopaminergic neurodegeneration via DAPK1-mediated microglial neuroinflammation and neuronal apoptosis

Liuyan Ding¹^na1,
Hui Shu¹^na1,
Minshan Chen¹^na1,
Fengchu Liang¹,
Tingting Gan²,
Xingting Huang²,
Xiaolei Liang¹,
Kangting Luo¹,
Linfeng Qiu³,
Weiqing Huang¹,
Xiaoqin Zhu⁴,
Xiaoyun Huang⁵,
Wenlong Zhang¹ &
…
Pingyi Xu¹

npj Parkinson's Disease volume 12, Article number: 23 (2026) Cite this article

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Abstract

Parkinson’s disease (PD) involves dopaminergic neuron loss and neuroinflammation, with leucine-rich repeat kinase 2 (LRRK2) mutations identified as major genetic risk factors. However, the pathogenic mechanism of the novel LRRK2-P1446L mutation remains unknown. Here, we designed LRRK2-P1446L mutant mice and demonstrated that the novel LRRK2-P1446L mutation drives neurodegeneration through death-associated protein kinase 1 (DAPK1) dysregulation. This mutation downregulates LRRK2 while upregulating DAPK1, which concurrently triggers microglial PI3K/Akt-dependent NF-κB activation (inducing IL-1β/IL-6/TNF-α expression) and neuronal mitochondrial apoptosis (via a Bax/Bcl-2 imbalance). Integrative multiomics revealed suppressed expression of the neuroprotective molecule tuftsin, which negatively correlated with DAPK1 expression and was linked to microbiota alterations. Our work establishes DAPK1 as a pivotal hub mediating neuroinflammation and apoptosis in LRRK2-related PD pathogenesis, and reveals novel associations with the gut-brain axis. These findings support DAPK1 inhibition as a promising therapeutic strategy, while the negative correlation with tuftsin suggests its restoration may be a potential future avenue for intervention.

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Introduction

Parkinson disease (PD) pathogenesis involves progressive dopaminergic neuron loss and neuroinflammation, with complex interactions between genetic and environmental factors affecting numerous fundamental cellular processes^1,2. Among these genetic factors, mutations in the gene encoding leucine-rich repeat kinase 2 (LRRK2) represent one of the most common monogenic causes of PD and have also been implicated in sporadic cases³, positioning these genes as major genetic drivers^4,5,6.

LRRK2 encodes a multidomain protein kinase whose catalytic core comprises a Ras-like GTPase domain (ROC) and a C-terminal domain (COR)^7,8. Pathogenic variants in LRRK2 dysregulate fundamental cellular processes, including autophagic flux, mitochondrial quality control, and neuroimmune responses, contributing to PD pathogenesis^9,10,11. Critically, the pathogenicity of LRRK2 mutations exhibits pronounced locus heterogeneity¹². While kinase domain mutations (e.g., G2019S) dominate current research^8,11,13, mutations within the ROC-COR tandem domain, which is essential for GTPase activity and protein function¹⁴, are poorly characterized at the functional and pathological levels.

This knowledge gap is particularly salient for the recently identified P1446L missense variant, which is linked to parkinsonism in clinical cohorts¹⁵. Localized to a conserved residue within the ROC domain, this missense mutation is predicted to modulate GTPase activity and effector binding based on structural analyses^14,16. However, three fundamental questions remain unresolved: whether P1446L directly drives neurodegeneration in vivo, through which molecular effectors it coordinates neuronal death and neuroinflammation, and whether it triggers multisystemic pathology beyond the nigrostriatal axis.

This study employed CRISPR-Cas9 gene editing technology to establish¹⁷, for the first time, an LRRK2 P1446L point mutation mouse model to address these questions. Through integrated behavioral, neuropathological, and multiomics analyses, we aimed to definitively validate the pathogenicity of this rare variant and identify its core downstream effectors that link LRRK2 dysfunction to dopaminergic degeneration. These findings provide experimental evidence for assessing the pathogenicity of rare LRRK2 variants and discovering novel pathogenic pathways amenable to therapeutic intervention.

Results

The LRRK2-P1446L mutation induces age-dependent parkinsonian phenotypes with dopaminergic degeneration and α-synuclein pathology

Following the knockout of sequences at the P1446 locus in exon 31, we introduced a donor DNA mutation into exon 31 of mouse LRRK2 via CRISPR/Cas9-mediated homologous recombination (Fig. 1a), resulting in the construction of LRRK2-P1446L point mutant mice.

**Fig. 1: The LRRK2-P1446L mutation induces motor deficits at 16 months of age.**

Systematic behavioral assessment revealed progressive motor and nonmotor deficits in LRRK2-P1446L knock-in mice compared with their wild-type (WT) littermates. Open field testing (Fig. 1b–g) revealed significant motor impairments in mutant mice, including a reduced total distance traveled, decreased velocity, and shorter center duration, with more pronounced effects observed in females. Consistent with these findings, elevated plus-maze (EPM) testing also revealed a reduced total distance traveled and decreased velocity (Fig. 1k–l). Motor coordination assays (grasping, pole climbing, and rotarod tests) consistently demonstrated a prolonged pole descent latency, reduced rotarod endurance, and impaired wire hanging ability (Fig. 1h–j). Anxiety-like phenotypes in LRRK2-P1446L mice, which are characterized by reduced exploratory activity, included fewer open-arm entries in the EPM test (Fig. 1n) and fewer total arm entries in the Y maze test (Fig. 1p). Depression-related behaviors were evidenced by increased immobility in the tail suspension test and forced swim test (Fig. 1q–r). Collectively, these findings indicate that the LRRK2-P1446L mutation induces motor dysfunction and compromises exploratory behavior and emotional states in mice.

To further investigate potential sex-specific effects, we analyzed our behavioral data for sex-specific differences (Fig. S1 and S2). The primary motor deficits and most non-motor phenotypes induced by the LRRK2-P1446L mutation did not differ significantly between male and female mice. Although female mice exhibited a higher baseline of exploratory activity in the Y maze test (Fig. S2f), this did not impact the core metric of spatial working memory (Fig. S2e). Thus, these findings indicate that the Parkinsonian-like phenotypes are robustly expressed in both sexes.

Immunoblot analyses of tyrosine hydroxylase (TH) and phosphorylated α-synuclein (p-α-syn) levels in the substantia nigra and striatum revealed significant, age-dependent reductions in TH protein levels in LRRK2-P1446L mice at 8, 12, and 16 months of age, concurrent with the progressive accumulation of p-α-syn (Fig. 2a–d). This increase in phosphorylated protein was accompanied by a similar age-dependent rise in total α-synuclein levels, suggesting an overall accumulation of the protein (Fig. S3). Immunofluorescence staining corroborated these pathological changes, with markedly decreased TH-positive neuron counts and significantly elevated p-α-syn fluorescence intensity (Fig. 2e–h). Together, these results show that the LRRK2-P1446L mutation induces age-progressive dopaminergic neuron degeneration and α-synuclein pathology, which underlie the observed motor and behavioral deficits.

**Fig. 2: The LRRK2-P1446L mutation drives dopaminergic neuron loss and pathological α-synuclein accumulation.**

Transcriptomic profiling identifies DAPK1 upregulation and associated neuroinflammatory pathways in the substantia nigra

A bulk transcriptome analysis was performed on substantia nigra tissue from LRRK2-P1446L mutant and wild-type (WT) mice to elucidate the molecular mechanisms underlying LRRK2-P1446L-associated Parkinsonian neurodegeneration. The differential gene expression analysis revealed 13,006 differentially expressed genes (DEGs), 283 of which were upregulated and 433 of which were downregulated in LRRK2-P1446L mutant mice (Fig. 3a). Dapk1 expression was significantly upregulated in LRRK2-P1446L mice, as confirmed by violin plots (Fig. 3b–c), which revealed an approximately 2.5-fold increase in expression compared with that in WT mice (p = 0.000055). To explore a potential transcriptional mechanism driving this change, we performed a bioinformatic analysis intersecting differentially expressed transcription factors (TFs) with predicted binders of the Dapk1 promoter. This unbiased approach identified Sox10 as the sole candidate that was both predicted to regulate Dapk1 and was significantly downregulated (log2FC = −0.959, p < 0.05) in our mutant mice (Fig. S6). This finding suggests a potential regulatory axis where reduced Sox10 levels may lead to the de-repression of Dapk1 transcription.

**Fig. 3: The LRRK2-P1446L mutation upregulates DAPK1 expression and activates the IκBα/NF-κB pathway.**

To validate the clinical relevance and functional context of this key finding, we performed two additional bioinformatic analyses. First, an integrated analysis of four human single-cell RNA-sequencing datasets demonstrated that DAPK1 expression was also significantly upregulated (log2FC = 0.316, p < 0.0001) in dopaminergic neurons from Parkinson’s disease (PD) donors relative to healthy controls (Fig. S4). Second, to elucidate its functional role, we constructed a protein-protein interaction (PPI) network that placed DAPK1 and LRRK2 within a densely interconnected module (Fig. S5). Functional enrichment of this network revealed a significant overrepresentation of pathways central to our study, including apoptosis, autophagy, and NF-κB signaling. Together, these independent analyses strongly support DAPK1 as a critical and clinically relevant node in LRRK2-mediated pathology.

The KEGG pathway enrichment analysis of the DEGs revealed the significant enrichment of the PI3K–Akt signaling pathway (Fig. 3f), a critical regulator of cell survival, proliferation, and apoptosis. A heatmap of key genes within this pathway, including Gng4, Mtcp1, and Gng11, illustrated distinct expression patterns in the mutant mice (Fig. 3g). The subsequent gene set enrichment analysis (GSEA) revealed the significant enrichment of the NF-κB signaling pathway (NES = 1.534; p = 0.008147; Fig. 3i), immune response (NES = 1.693; p = 0.001988; Fig. 3j), and regulation of the inflammatory response (NES = 2.07; p = 0.001996; Fig. 3k) in LRRK2-P1446L mutant mice. Complementary GO enrichment analyses revealed dysregulation associated with NLRP3/NLRP1 inflammasome complexes, which was supported by the correlation analysis of the DEGs (Fig. 3d–e).

Collectively, the results of our transcriptomic profiling identified Dapk1 as a prominently upregulated gene in the substantia nigra of LRRK2-P1446L mutant mice. This finding is reinforced by evidence from human PD patient neurons, where DAPK1 is also upregulated, and by a PPI network analysis linking DAPK1 directly to LRRK2-associated pathways. The concurrent enrichment of PI3K–Akt, NF-κB, and immune response pathways therefore strongly indicates that DAPK1 upregulation is a central event, closely associated with the neuroinflammatory and apoptotic signaling cascades that contribute to the pathogenesis of parkinsonian neurodegeneration in this model.

DAPK1 upregulation directly drives dopaminergic neuronal apoptosis

Experimental validation confirmed the significant downregulation of LRRK2 protein expression in the substantia nigra of LRRK2-P1446L mutant mice compared with that in the substantia nigra of wild-type (WT) controls (Fig. 4a, b). Concomitantly, immunoblot analyses revealed upregulated expression of the apoptosis activator DAPK1 and the proapoptotic protein Bax and downregulated expression of the antiapoptotic protein Bcl-2 in nigral tissue from the LRRK2-P1446L mutant mice (Fig. 4c, d). To determine if these changes were dependent on age or sex, we performed a broader analysis. This revealed that the pathogenic molecular signature (the downregulation of LRRK2 and Bcl-2 and the upregulation of DAPK1 and Bax) was evident as early as 8 months and persisted through 16 months of age. Importantly, these changes were observed robustly in both male and female mutant mice, demonstrating a consistent, sex-independent mechanism (Fig. S3). The increase in DAPK1 immunofluorescence intensity specifically within the TH-positive dopaminergic neurons in the substantia nigra of LRRK2-P1446L mice (Fig. 4e, f) contrasted with the decrease in Bcl-2 intensity in these neurons, further corroborating the dysregulation of apoptotic proteins (Fig. 4g, h). The expression and role of DAPK1 in glial cells (Fig. 4i-l) are detailed in the context of neuroinflammation in the next section.

**Fig. 4: The LRRK2-P1446L mutation promotes neuronal apoptosis by upregulating DAPK1 expression.**

Complementary in vitro studies utilizing MN9D cells transfected with the LRRK2-P1446L mutant recapitulated these findings. Immunoblotting confirmed reduced LRRK2 expression (Fig. 5a, d) concurrent with the significant upregulation of DAPK1 and Bax alongside Bcl-2 downregulation compared to WT-transfected controls (Fig. 5b, e). This DAPK1 overexpression was further validated by the increased immunofluorescence intensity in the mutant-transfected cells (Fig. 5g, h). An assessment of mitochondrial function revealed significantly increased superoxide levels in mutant-transfected cells compared with those in control cells (Fig. 5i, j). To confirm that these pro-apoptotic signals culminated in cell death, we performed a TUNEL assay. Indeed, the expression of LRRK2-P1446L led to a significant increase in TUNEL signal intensity, indicating elevated DNA fragmentation and providing definitive evidence of mutation-induced neuronal apoptosis (Fig. 5k, l).

**Fig. 5: The LRRK2-P1446L mutation recapitulates DAPK1/NF-κB activation and apoptosis in vitro.**

Collectively, these in vivo and in vitro results indicate that the LRRK2-P1446L mutation induces DAPK1 upregulation, shifts the Bcl-2/Bax ratio toward a pro-apoptotic state, and increases mitochondrial superoxide production in dopaminergic neurons.

DAPK1 activation in microglia triggers neuroinflammation through the PI3K–AKT/NF-κB signaling cascade

In addition to its upregulation in dopaminergic neurons, DAPK1 expression was also significantly increased in Iba1-positive microglia (Fig. 4i, j) and GFAP-positive astrocytes (Fig. 4k, l) in the substantia nigra of LRRK2-P1446L mice. To elucidate the underlying molecular mechanism, we examined key inflammatory signaling pathways. Western blot analysis of substantia nigra tissue from LRRK2-P1446L mutant mice revealed significantly increased levels of phosphorylated AKT (p-AKT), IκB (p-IκB), and NF-κB p65 (p-NF-κB p65) compared with those in wild-type (WT) controls (Fig. 6a, b). This hyperphosphorylation signature, which is indicative of PI3K–AKT/NF-κB pathway activation, was recapitulated in BV2 microglia transfected with the LRRK2-P1446L mutant (Fig. 5c, f).

**Fig. 6: The LRRK2-P1446L mutation triggers microglial activation and a proinflammatory state.**

To identify the primary cell type driving this neuroinflammatory signaling in vivo, we performed immunofluorescence analysis and observed a significant increase in p-p65 fluorescence intensity specifically in Iba1-positive microglia (Fig. 6d, e). Consistently, Iba1/CD68 co-staining revealed elevated CD68 expression in the microglia of the mutant mice, indicating enhanced phagocytic activation (Fig. 6f, h). Further morphological assessments of Iba1-stained microglia confirmed an activated state in the mutants that was characterized by significant reductions in intersections, branch endpoints, and total process length, as well as an increased soma area (Fig. 6f–g, i–k). Finally, to investigate the functional consequence of this microglial activation, complementary qPCR analysis of nigral tissue were performed. This showed a significant upregulation of proinflammatory cytokine transcripts (IL-1β, IL-6, and TNF-α) in LRRK2-P1446L mutant mice compared with WT mice (Fig. 6c).

Together, these data demonstrate that DAPK1 expression is upregulated in glial cells, the PI3K–AKT/NF-κB signaling cascade is activated, the microglial morphology changes to indicate an activated state, and proinflammatory cytokine expression is increased in LRRK2-P1446L mice.

Brain metabolomics reveals tuftsin deficiency and its negative correlation with DAPK1 upregulation

Comprehensive brain metabolomic profiling of LRRK2-P1446L mutant and wild-type (WT) mice revealed distinct metabolic separation by principal component analysis (PCA; Fig. 7a). The volcano plot shows the significant downregulation of the neuroprotective peptide tuftsin in mutant brains (VIP = 2.4356, log2FC = -0.7894; Fig. 7B), which ranked as the second most significantly altered metabolite among the top 10 differentially abundant metabolites (P = 0.0018; Fig. 7d). This downregulation was further confirmed by examining its levels in violin plots (Fig. 7b, c). The KEGG pathway enrichment analysis revealed the significant enrichment of the “axon regeneration” pathway (KEGG_Rich factor = 12.2; Fig. 7e), with a differential abundance score of −1.0 (Fig. 7f).

**Fig. 7: The LRRK2-P1446L mutation reduces the level of the neuroprotective metabolite tuftsin, which directly interacts with DAPK1.**

The integrated analysis of transcriptomic and metabolomic datasets identified 21 KEGG pathways coenriched in both gene expression and metabolite profiles (Fig. s7a), and scatter plots show the enrichment patterns of differentially expressed genes (DEGs) and metabolites (DEMs) across pathways (Fig. s7b). Critically, the cross-omics integration revealed a significant negative correlation between tuftsin abundance and Dapk1 transcript levels (Pearson’s r = −0.8954, P < 0.01; Fig. 7g). To explore a potential molecular basis for this statistical association, we performed molecular docking simulations. These computational models predicted a strong binding affinity between tuftsin and DAPK1 (optimal Vina score: −7.5 kcal/mol), with potential interaction sites at DAPK1 residues GLU-392, ARG-314, SER-312, LEU-311, and ARG-400 (Fig. 7h). While our in silico modeling suggests a plausible direct interaction, these findings primarily serve to generate the hypothesis that tuftsin deficiency and DAPK1 signaling may be mechanistically linked. Future studies involving direct biochemical and functional assays are required to validate this potential relationship.

In summary, these analyses show that tuftsin is downregulated in LRRK2-P1446L mice and strongly correlates with increased Dapk1 expression. Our in silico modeling suggests a plausible interaction, these findings primarily generate a testable hypothesis linking tuftsin deficiency to DAPK1 signaling, which awaits experimental validation.

Gut dysbiosis in LRRK2-P1446L mice correlates with altered functional pathways and reduced brain tuftsin levels

16S rRNA gene sequencing of fecal samples from LRRK2-P1446L mutant mice and wild-type (WT) mice revealed distinct microbial community separation by principal component analysis (PCA; Fig. 8a–c). The taxonomic analysis indicated differences in the microbial composition at multiple classification levels (Fig. 8d–h), with the cladogram confirming the enrichment of different phylogenetic branches in the mutant mice (Fig. 8j). The functional prediction analysis (PICRUSt2) indicated an altered representation of microbiota-associated metabolic pathways annotated to neurodegenerative diseases and aging (Fig. 8k). The metabolite‒microbiota correlation analysis revealed a significant association between tuftsin abundance and the abundance of the bacterial genus Paraprevotella (Fig. 8l).

**Fig. 8: The LRRK2-P1446L mutation alters the gut microbiota and is correlated with the brain level of the metabolite tuftsin.**

Together, these data demonstrate a significant restructuring of the gut microbial community in LRRK2-P1446L mice, which is associated with predicted alterations in microbial metabolic functions. While the correlation between Paraprevotella abundance and brain tuftsin levels is intriguing, this finding does not establish a causal relationship. It does, however, generate the novel hypothesis that specific gut microbes may influence the levels of neuroprotective molecules in the brain, a possibility that warrants investigation in future mechanistic studies.

Discussion

Accumulating evidence indicates functional interactions between LRRK2 and neuroinflammatory pathways; however, the precise mechanisms by which LRRK2 kinase activity regulates NF-κB signaling remain incompletely defined^18,19. In this study, leveraging a novel CRISPR/Cas9-generated LRRK2-P1446L knock-in mouse model, we systematically delineate a pathogenic cascade in which this understudied ROC-COR domain mutation drives progressive parkinsonian neurodegeneration through the DAPK1-mediated coordination of microglial inflammation and neuronal apoptosis in the absence of exogenous stressors. These findings align with previous reports on the involvement of LRRK2 in Parkinson’s disease pathogenesis^20,21. Critically, our integrated multiomics approach—encompassing behavioral phenotyping, nigrostriatal pathology, transcriptomics, proteomics, brain metabolomics, and gut microbiome profiling—establishes DAPK1 as a central molecular hub that links LRRK2 dysfunction to dual cytotoxic processes while revealing tuftsin deficiency and gut dysbiosis as novel systemic contributors.

The P1446L mutation induced age-dependent motor deficits and nonmotor symptoms (anxiety/depression-like behaviors), accompanied by progressive TH⁺ neuron loss and α-synuclein pathology within nigrostriatal circuits. Transcriptomic profiling revealed the significant coactivation of DAPK1-mediated apoptotic pathways and NF-κB signaling in LRRK2-P1446L mice. The expression of DAPK1, a critical stress-response regulator, is aberrantly increased during neurodegeneration²² and is mechanistically linked to the excessive promotion of neuronal apoptosis and autophagy^23,24. Crucially, we identified DAPK1 upregulation as a molecular consequence of LRRK2-P1446L expression, in contrast to canonical kinase domain mutations such as G2019S that disrupt vesicular trafficking through Rab GTPase hyperphosphorylation^25,26,27, highlighting locus-dependent pathogenic heterogeneity. DAPK1 coordinates dual cytotoxic pathways: triggering mitochondrial apoptosis in dopaminergic neurons via a Bax/Bcl-2 imbalance and superoxide overproduction while concurrently activating microglial PI3K/AKT/NF-κB cascades that drive the release of proinflammatory cytokines (IL-1β, IL-6, and TNF-α), morphological changes (increased soma area and reduced arborization), and increased expression of the phagocytic marker CD68. These dual findings raise a critical question: which cell type—neurons or microglia—is the primary driver of pathology? Our evidence points to a “neuron-initiates, glia-amplifies” sequence. We established that the pathogenic cascade can begin cell-autonomously, as the LRRK2-P1446L mutation alone was sufficient to trigger a DAPK1-mediated apoptotic cascade in in vitro pure neuronal cultures. This neuron-centric model is further reinforced by human single-cell RNA-seq data, where DAPK1 upregulation is a conserved feature in the dopaminergic neurons of PD patients. Critically, the divergent DAPK1 expression in microglia—upregulated in mutant mouse model versus downregulated in end-stage human PD brains—is highly informative. We propose this reflects microglia acting as potent amplifiers during the active neurodegenerative phase, as captured in our mice, while the neuron remains the primary and constant locus of DAPK1-driven pathology. Thus, a sustained, neuron-autonomous process likely drives the disease, with the reactive glial response serving as a critical accelerator of neurodegeneration. This establishes pharmacologic or genetic DAPK1 inhibition as a promising strategy to simultaneously mitigate both degenerative processes in LRRK2-P1446L-associated Parkinson’s disease.

Unbiased brain metabolomics revealed a significant downregulation of tuftsin (Thr-Lys-Pro-Arg), an immunomodulatory tetrapeptide^28,29 with established neuroprotective and anti-inflammatory functions^30,31,32 that are mediated by its binding to neuropilin-1 (NRP1)^33,34,35. This result represents a novel finding within the context of LRRK2-associated Parkinson’s disease. Key observations include a strong negative correlation between tuftsin abundance and DAPK1 expression and molecular docking predictions suggesting direct binding between tuftsin and DAPK1, indicating a potential regulatory interaction. The KEGG enrichment analysis linked tuftsin depletion to suppressed “axon regeneration” pathways. Based on these correlational and in silico findings, we have generated a new hypothesis: tuftsin deficiency may contribute to a pathogenic cycle by removing a potential layer of regulation on DAPK1, thereby exacerbating both neuroinflammation and neuronal apoptosis. While this model is compelling, it remains a hypothesis that requires direct experimental validation. If validated, restoring tuftsin signaling could emerge as a therapeutic avenue.

Beyond central pathways, fecal 16S rRNA sequencing revealed the significant remodeling of the gut microbiome in mutant mice. Functional predictions (PICRUSt2) implicated pathways associated with neurodegeneration. Notably, tuftsin abundance correlated significantly with the abundance of Paraprevotella, a genus that is also altered in human Parkinson’s disease cohorts³⁶. This observation suggests a potential “microbiome–tuftsin–brain” link, but it is important to interpret this correlation with caution. The current data do not establish causality or directionality; it is unclear whether these microbiome changes are primary drivers of tuftsin deficiency or secondary consequences of ongoing neurodegeneration. These findings are therefore best framed as generating a novel hypothesis: that gut dysbiosis, and specifically changes in microbes like Paraprevotella, may contribute to the systemic metabolic landscape in LRRK2-associated parkinsonism.

While this study established death-associated protein kinase 1 (DAPK1) as a critical hub coordinating LRRK2-P1446L-driven neurodegeneration, several limitations warrant attention. First, the precise mechanism linking LRRK2 dysfunction to DAPK1 transcriptional upregulation remains undefined. Our bioinformatic analysis provides a compelling, data-driven hypothesis, pointing to the transcription factor Sox10 as a potential mediator. We found that Sox10, a known transcriptional repressor^37,38,39, was downregulated in mutant mice, offering a plausible mechanism for the de-repression of the Dapk1 gene. However, this link is currently predictive and requires direct experimental validation, such as chromatin immunoprecipitation (ChIP). Second, as our conclusions regarding a tuftsin-DAPK1 link are based on correlational and in silico modeling, direct functional validation—for instance, through in vitro kinase assays or in vivo supplementation studies—is essential to test the hypothesis that tuftsin directly modulates DAPK1 activity. Similarly, the causal role of Paraprevotella-associated dysbiosis in tuftsin deficiency require further investigation using mechanistic models such as tuftsin supplementation, or fecal microbiota transplantation. Third, whether this P1446L-specific cascade is generalizable to other LRRK2 mutations (e.g., G2019S) or sporadic Parkinson’s disease (PD) merits exploration.

Collectively, our integrated analysis elucidates a multitiered pathogenic axis: the LRRK2-P1446L mutation initiates DAPK1 upregulation, triggering synergistic microglial neuroinflammation (mediated by the PI3K/AKT/NF-κB pathway) and neuronal apoptosis (mediated by a Bax/Bcl-2 imbalance), which occurs in the context of tuftsin deficiency and gut dysbiosis. This work makes three seminal contributions: it establishes DAPK1 as a unifying effector in LRRK2-associated Parkinson’s disease (LRRK2-PD) pathogenesis, explaining site-dependent heterogeneity among mutations; it identifies tuftsin depletion as a novel metabolic signature negatively correlated with DAPK1 expression; and it highlights a potential role for gut–brain interplay in disease. These findings suggest that the inhibition of DAPK1 expression, the restoration of tuftsin expression, and the modulation of the microbiome could be promising avenues for precision therapeutic strategies for LRRK2-P1446L-associated PD, advancing a framework for targeting the synergy between neuroimmune activity and apoptosis in neurodegeneration. The schematic summary of this proposed mechanism is depicted in Fig. 9.

**Fig. 9: Schematic summary of the proposed mechanism.**

Methods

Reagents

Anti-TH (sc-25269), anti-α-synuclein (SC-53955) (all from Santa Cruz Biotechnology, USA), anti-Iba1 (019-19741; FUJIFILM Wako, Japan), anti-GFAP (MAB360; Millipore, USA), anti-Bcl-2 (WL01556; Wanlei Biotechnology, China), anti-Bax (ET1603-34, Huaan Biotechnology, China), anti-DAPK1 (25136-1-AP), anti-Bcl-2 (68103-1-Ig), anti-GAPDH (60004-1) (all from Proteintech Group, USA), anti-CD68 (ab53444), anti-LRRK2 (ab133518), anti-phospho-IκBα (ab133462), anti-IκBα (ab32518) (all from Abcam, USA), anti-NF-κB (#8242), anti-phospho-NF-κB (#3033), anti-pSyn129 (#23706), anti-phospho-Akt (#4060), and anti-Akt (#4685) (all from Cell Signaling Technology, USA) antibodies were used. The secondary antibodies used were Alexa Fluor® 555-conjugated F(ab’)₂ fragment [anti-rabbit (#4413), anti-mouse (#4409)] and Alexa Fluor® 488-conjugated F(ab’)₂ fragment [anti-rabbit (#4412), anti-mouse (#4408)] (Cell Signaling Technology, USA).

Animals

The LRRK2^P1446L mutant mice were generated in collaboration with Shanghai Model Organisms Center, Inc. (Shanghai, China) using the CRISPR/Cas9 method as detailed in the next Section. The male and female mice were grouped based on age and genotype, and compared with age-matched wild-type (WT) littermates. The mice were maintained under controlled conditions, with a 12-h light/dark cycle and food and water provided ad libitum, and housed in groups of 4–5 per cage with nesting material. All animal experimental procedures were conducted in accordance with the ethical guidelines established by the Institutional Animal Care and Use Committee (IACUC) of Guangzhou Forevergen Biosciences (No: ACUC-AEWC- F210201063) and the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals (NIH Publication No. 8023, revised 1978) regarding the ethical treatment of animals. No unexpected adverse events or health complications were observed during the study period. For tissue collection, mice were euthanized by isoflurane anesthesia followed by cervical dislocation.

Generation of P1446L mutant mice

We employed CRISPR/Cas9 gene editing technology to generate LRRK2-P1446L point mutant mouse models. The experimental procedure was as follows: First, we designed a specific gRNA targeting the P1446 locus of the LRRK2 gene (gRNA sequence CGGTGATTCTGGTGGGCACA). This gRNA was comicroinjected with the Cas9 mRNA and donor DNA (oligo donor sequence: CCGGAAACAGGAAGTGTCTTGTCTCTGTTCCCTTAGGCTCGTGCCTCTTCTTCCCTGGTGATTCTGGTGGGCACACATTTGGATGTTTCTGATGAGAAGCAGCGGAAAGCG) into fertilized eggs of C57BL/6J mice to produce F0 generation mice. The F0 mice were then validated through PCR amplification using specific primers (Forward: 5’-AGGGAGGGGACCTAACATCC-3’; Reverse: 5’-TTTTCCGAAGCTTTGCCAGC-3’) followed by sequencing (sequencing primer: 5’-AGGGAGGGGACCTAACATCC-3’). After the confirmation of successful editing, the F0 mice were bred with wild-type C57BL/6J mice to obtain F1 generation heterozygous mice with stable inheritance of the P1446L mutation (see Fig. 1a).

Behavioral analyses

All groups of mice were subjected to an open field test, a rotarod test, a pole-climbing test and a forelimb grip strength test, as described previously⁴⁰. All behavioral tests were conducted during the light phase under consistent environmental conditions. To minimize bias, all behavioral tests and subsequent outcome assessments were performed in a randomized order by experimenters blinded to animal genotypes.

Open field test

A 40 × 40 cm square apparatus was used, with a central zone measuring 20 × 20 cm. The mice were allowed to freely explore the arena for 15 minutes. Their locomotor activity was recorded using a video tracking system (EthVision XT software, Beijing, China), with the following parameters analyzed: movement speed (cm/s), total distance traveled (cm), time spent in the central zone (s), latency to first entry into the center (s), and number of entries into the center.

Grasping test

For the grasping test, mice were placed with their forepaws on a 1-mm diameter horizontal stainless steel wire suspended 30 cm above the ground, and their hindlimb grasping ability was scored over 10 seconds (3 points for both hindlimbs grasping, 2 for one hindlimb grasping, 1 for no grasping, and 0 for falling), with three trials averaged per mouse.

Rotarod test

The rotarod test included three days of acclimation training at a constant 10 rpm speed followed by formal testing in which the rod accelerated from 4 to 40 rpm over 5 min, and the latency to fall (the test ended if the mouse fell or clung without walking for two full rotations) was recorded, with three trials averaged.

Pole climbing test

In the pole climbing test, after 30 min of habituation, the mice were placed 7.5 cm below the top of a 9-mm diameter, 75-cm long fabric-wrapped metal pole, and the time to descend to the base was recorded (maximum 60 s), with three trials averaged per mouse.

Elevated plus maze (EPM)

The EPM test was performed as described in our previous study⁴¹. The EPM device consists of two open arms (30 × 5 cm), two closed arms (30 × 5 × 15 cm), and a central zone (5 × 5 cm). The device was elevated to a height of 50 cm above the ground. The mice were placed in the central intersection and allowed to explore for 5 min. A video tracking system (EthoVision XT) was used to record the time spent in the open and closed arms.

Y Maze

A Y-maze test was performed as described in our previous study⁴². The Y maze consisted of three arms (30 cm long, 10 cm wide, and 20 cm high) and a connected central area. The mice were placed within the center area and allowed to explore the Y maze freely for 8 min. A video tracking system (EthoVision XT; Beijing, China) was used to analyze spontaneous alterations.

Tail suspension test (TST)

The TST was performed as we previously described⁴³. The mice were suspended 5 cm above the ground using adhesive tape placed ~1 cm from the tip of the tail. The immobility time was recorded with the EthoVision XT system for 6 min.

Forced swimming test (FST)

The mice were individually placed into a transparent cylindrical container (25 cm height × 10 cm diameter) filled with water (23–25 °C) to a depth of 10 cm. The immobility time was recorded during the final 4 min of a 6-minute test session using the EthoVision XT video tracking system.

RNA sequencing and bioinformatics analyses

For RNA-seq, total RNA was extracted from SN and hippocampal tissues using TRIzol (Life Technologies, Carlsbad, CA, USA), as described previously⁴⁴. In accordance with the manufacturer’s instructions, sequencing libraries were prepared using the NEBNext Ultra^TM RNA Library Prep Kit for Illumina (NEB, USA). Next, the PCR products were purified (AMPure XP system), and the library quality was assessed with an Agilent Bioanalyzer 2100 system. Clustering of the index-coded samples was performed with the cBot Cluster Generation System using the TruSeq PE Cluster Kit v4-cBot-HS platform (Illumina) according to the manufacturer’s instructions. After cluster generation, the prepared libraries were sequenced on the Illumina platform, and paired-end reads were generated. The raw reads were further processed with the online bioinformatic tool BMKCloud (www.biocloud.net). The sequencing results were adjusted using the Benjamin–Hochberg method to control the error detection rate. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the results were performed.

Metabolomics and analyses

Brain tissues for metabolomic profiling were collected from 16-month-old male LRRK2-P1446L mutant mice and their wild-type (WT) littermates. Metabolomic assays were conducted as described previously⁴⁵. Metabolites were extracted using a methanol/acetonitrile (1:1, v/v) solution containing an internal standard (2 mg/L), followed by ultrasonication (10 min, ice water bath) and centrifugation (4 °C, 12,000 rpm, 15 min). The supernatant was dried in a vacuum concentrator, reconstituted in acetonitrile/water (1:1, v/v), vortexed, sonicated, and centrifuged again. The supernatants were transferred to injection vials, with 10 μL mixed for QC. The samples were analyzed using a Waters Acquity I-Class PLUS UPLC system and a Xevo G2-XS QT high-resolution mass spectrometer with a UPLC HSS T3 column (1.8 μm, 2.1 × 100 mm). The data were analyzed by Biomarker Technologies (Beijing, China), with compound classification obtained from the KEGG, HMDB, and lipidmap databases. PCA and Spearman’s correlation analysis verified the sample quality, and t tests revealed significant differences in metabolite levels. Differentially abundant metabolites were identified based on an FC > 1.5, p < 0.05, and VIP > 1. Enriched KEGG pathways were assessed using a hypergeometric distribution test.

Fecal 16S rDNA sequencing and processing

The methods were performed as previously described⁴⁶. Total genomic DNA was extracted from the fecal samples using the CTAB/SDS method, and the DNA concentration and purity were assessed by 1% agarose gel electrophoresis before the samples were diluted to 1 ng/μL with sterile water. Target regions were amplified using specific primers (16S V4, 515F-806R; 18S V4, 528F-706R; 18S V9, 1380F-1510R; ITS1, ITS5-1737F and ITS2-2043R; ITS2, ITS3-2024F and ITS4-2409R) for 16S/18S rRNA gene fragments. PCRs (30 μL) contained 15 μL of Phusion High-Fidelity PCR Master Mix (New England Biolabs [NEB], Ipswich, MA, USA), 0.2 μM each primer, and 10 ng of template DNA. The thermal cycling conditions included initial denaturation at 98 °C for 1 min; 30 cycles of 98 °C for 10 s, 50 °C for 30 s, and 72 °C for 30 s; and a final extension at 72 °C for 5 min. Samples showing clear 400–450 bp bands were selected for downstream analysis. The sequencing libraries were prepared using an Ultra^TM DNA Library Prep Kit (NEB, Ipswich, MA, USA), and quality control was performed using a Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Finally, paired-end sequencing (250 bp) was conducted on the Illumina HiSeq platform (Illumina, San Diego, CA, USA).

Cell culture

The cells were purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA) and cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Carlsbad, CA, USA) supplemented with 8% fetal bovine serum (FBS; Gibco), 2 U mL⁻¹ penicillin (Beyotime Biotechnology), and 2 mg mL⁻¹ streptomycin (Beyotime Biotechnology). The cells were maintained at 37 °C in a humidified atmosphere containing 5% CO₂, and the medium was changed every two days. Cell suspensions (≈ 5 × 10⁴ cells) were seeded into 24-well plates and allowed to adhere for 24 h. The cells were then transfected with either LRRK2-WT or LRRK2-P1446L plasmids using Lipofectamine 3000 transfection reagent (Invitrogen).

Mitochondrial superoxide detection assay

MitoSOX (Thermo Fisher Scientific) and Hoechst 33342 (Beyotime Biotechnology) working solutions were added to the cells. After being incubated for 10 min at 37 °C, the cells were washed three times. The images were captured using a confocal laser-scanning microscope (SP8, Leica).

Western blot

Protein was extracted using RIPA buffer supplemented with 1 mM PMSF (Beyotime, China) through ultrasonic homogenization. The protein concentration was determined with a BCA assay (Beyotime, China). Protein molecular weight was determined using the PageRuler™ Prestained Protein Ladder (Thermo Scientific, #26616). Following SDS‒PAGE and PVDF transfer, the membranes were blocked (30 min, RT) and then incubated with primary antibodies (overnight, 4 °C) and HRP-conjugated secondary antibodies (1 h, RT). Protein bands were detected using ECL reagents (Beyotime, China) on a GeneGnome 5 system (Syngene, Cambridge, UK) and analyzed with ImageJ (NIH, USA).

Immunofluorescence staining

Mouse brains were fixed with 4% paraformaldehyde and dehydrated in 20−30% sucrose at 4 °C for 3 d. Brain samples were then embedded in OCT compound (Solarbio, 4583) and coronally sectioned at a thickness of 30 μm using a Leica cryostat. The sections were blocked with QuickBlock™ blocking buffer (Beyotime, P0252) at room temperature for 60 min, followed by an incubation with primary antibodies at 4 °C overnight. The sections were subsequently incubated with fluorescent dye-labeled secondary antibodies at room temperature for 1 h and counterstained with DAPI. Sections were imaged using a laser scanning confocal microscope (ZEISS 900), and fluorescence was quantified using ImageJ software.

TUNEL assay

Cell apoptosis was detected by the Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. The assay was performed using a commercial kit (C1088, Beyotime Biotechnology) following the manufacturer’s protocol.

qRT‒PCR

qRT–PCR was performed according to the methods described in a previous work⁴⁷. Total RNA was extracted from cells and substantia nigra tissues using TRIzol reagent (Invitrogen, San Diego, CA, USA). cDNA synthesis was performed with the PrimeScript RT reagent Kit (Takara, Otsu, Japan). qPCR was conducted on an Applied Biosystems 7500 Real-Time PCR System (Thermo Fisher Scientific, USA) using TB Green Premix Ex Taq II (Takara, Shiga, Japan) with the following cycling parameters: 95 °C for 30 s (initial denaturation), followed by 40 cycles of 95 °C for 5 s, 55 °C for 30 s, and 72 °C for 30 s. The experiment included three biological replicates, each with three technical replicates. Gapdh served as the reference gene, and the following qPCR primers were used: IL-1β (F: AATGCCACCTTTTGACAGTGAT, R: TGCTGCGAGATTTGAAGCTG), IL-6 (F: AGGATACCACTCCCAACAGACC, R: AAGTGCATCATCGTTCATACA), and TNF-α (F: CACGTCGTAGCAAACCACC, R: TGAGATCCATGCCGTTGGC). Relative quantification was performed using the 2^−ΔΔCT method.

Integrated analysis of public human scRNA-seq Data

We integrated four human single-cell RNA-seq datasets from the Gene Expression Omnibus (GEO)—GSE148434, GSE184950, GSE202210, and GSE235330—all containing human midbrain or neuron-related samples relevant to Parkinson’s disease. Batch effects across datasets were removed using Seurat’s integration functions (FindIntegrationAnchors and IntegrateData), and resulting cell clusters were visualized via t-distributed stochastic neighbor embedding (t-SNE).

Protein—protein interaction (PPI) network analysis

To functionally contextualize DAPK1 in Parkinson’s disease, we built a PPI network linking it to the key PD gene LRRK2. This network was constructed with the GeneMANIA plugin (v3.6.0) in Cytoscape (v3.9.1), integrating multiple interaction types—physical interactions, genetic interactions, co-expression, and co-localization—to reveal functional relationships.

Prediction of transcription factor binding sites (TFBS)

To identify potential transcriptional regulators of Dapk1, a predictive analysis of transcription factor binding sites (TFBSs) was conducted. The putative promoter region, defined as the 1-kb sequence upstream of the murine Dapk1 transcription start site, was scanned for TFBSs using the TFBSTools R package. Transcription factor binding motifs were sourced from the JASPAR core database (http://jaspar.genereg.net/).

Statistical analysis

Data are presented as means ± SEM. Statistical analyses were carried out with Student’s t test or one-way ANOVA followed by Tukey’s post hoc test, where applicable. A p-value of less than 0.05 was deemed statistically significant. All analyses were performed using GraphPad Prism version 9.0. Significant differences are marked with * for p < 0.05 and ** for p < 0.01.

Data availability

All data supporting the current study are provided in the source data file. All additional information and the code used for the analyses in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by supported by the National Natural Science Foundation of China (No. 82471261, No. 82101325, No. 82071416), Guangzhou basic and applied basic research foundation (No. 2024A04J3540), And Science and Technology Projects in Guangzhou (grant number 2024B03J0904; 2023A03J0424), Natural Science Foundation of Guangdong Province of China (grant number 2023A1515011897; 2023A1515140070; 2025A1515011146). We also acknowledge the Scientific Research Center of Guangzhou Medical University for instrument analysis and measurement.

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These authors contributed equally: Liuyan Ding, Hui Shu, Minshan Chen.

Authors and Affiliations

Department of Neurology, The First Affiliated Hospital of Guangzhou Medical university, Guangzhou, China
Liuyan Ding, Hui Shu, Minshan Chen, Fengchu Liang, Xiaolei Liang, Kangting Luo, Weiqing Huang, Wenlong Zhang & Pingyi Xu
Xinjiang Medical University Affiliated Second Hospital, Urumqi, Xinjiang, China
Tingting Gan & Xingting Huang
Deptartment of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China
Linfeng Qiu
Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
Xiaoqin Zhu
Department of Neurology, Houjie Hospital of Dongguan, the Clinical Medicine of Guangdong Medical University, Dongguan, China
Xiaoyun Huang

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Liuyan Ding
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Hui Shu
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Minshan Chen
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Fengchu Liang
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Tingting Gan
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Contributions

L.Y.D. and X.T.H. performed the animal study. H.S. analyzed the transcriptomic, metabolomic, and microbiomic data. F.C.L. performed the immunofluorescence. T.T.G. and X.L.L. performed the cell experiments. K.T.L. conducted the qPCR assay. L.F.Q. performed the Western blot. L.Y.D. and M.S.C. drafted the manuscript. W.Q.H., and X.Q.Z. reviewed and edited the draft. X.Y.H., P.Y.X. and W.L.Z. conceived and supervised the study. All authors have reviewed the final version of the manuscript and approved its submission.

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Correspondence to Xiaoyun Huang, Wenlong Zhang or Pingyi Xu.

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Ding, L., Shu, H., Chen, M. et al. The LRRK2 P1446L mutation triggers dopaminergic neurodegeneration via DAPK1-mediated microglial neuroinflammation and neuronal apoptosis. npj Parkinsons Dis. 12, 23 (2026). https://doi.org/10.1038/s41531-025-01234-2

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Received: 26 August 2025
Accepted: 03 December 2025
Published: 21 December 2025
Version of record: 22 January 2026
DOI: https://doi.org/10.1038/s41531-025-01234-2