National Science Review

Characterized by the earliest use of pottery, the Jomon culture was a unique Neolithic culture that spread throughout the Japanese Archipelago. Previous archaeological evidence suggests that Jomon hunter-gatherers colonized the southernmost islands, the Ryukyu Archipelago, by approximately 7,000 years before present (YBP). However, genetic characteristics of the Ryukyu Jomon population and its contribution to the modern population have not been elucidated yet. In this study, we newly sequenced 273 modern and 25 ancient (6,700-900 YBP) whole genomes collected across the Ryukyu Archipelago. Our analysis demonstrated a genetic differentiation between the Hondo (Japanese mainland) and Ryukyu Jomon, dating back to [~]6,900 YBP. After the divergence from the Hondo Jomon, the Ryukyu Jomon experienced severe bottlenecks, with an effective population size of [~]2,000. Admixture between the Ryukyu Jomon and migrants from the historic Hondo population occurred [~]1,000 YBP, which corresponds to the widespread adoption of iron tools and agriculture in the Central Ryukyus. Different demographic histories between modern Hondo and Ryukyu populations resulted in different rates of Jomon ancestry in these populations. By providing a new perspective on the peopling of the Ryukyu Archipelago, this study significantly enhances our understanding of cultural transitions in the region.

Enhancer-regulating epigenetic modifiers play critical roles in normal physiological processes and human pathogenesis. The major enhancer regulator paralogs MLL3 and MLL4 (MLL3/4) belong to the lysine methyltransferase 2 (KMT2) family, which catalyzes the methylation of lysine 4 on histone H3 (H3K4me). MLL3/4 are required for enhancer activation and are essential for mammalian development and stem cell differentiation. Recent studies have linked MLL3/4 with different metabolic pathways in the context of stem cell self-renewal and cancer cell growth; however, the underlying mechanisms remain elusive. Here, we utilize Seahorse extracellular flux analysis, stable isotope tracing, stem cell biology techniques, and transcriptomic analysis to investigate the functional relationship of MLL3/4, cellular respiration, and stem cell differentiation. Our results indicate that the loss of MLL3/4 impairs glycolytic activity and mitochondrial respiration in murine embryonic stem cells by downregulating the rate-limiting glycolytic enzyme Hexokinase 2 (HK2) and impairing the function of the Alpha-ketoglutarate dehydrogenase (OGDH) complex. Furthermore, simultaneously overexpression of HK2 and OGDH rescues defects in both cellular respiration and differentiation caused by MLL3/4 loss. Taken together, our study reveals a novel mechanism by which epigenetic machineries such as MLL3/4 govern the differentiation of pluripotent stem cells and facilitates the understanding of disease pathogenesis driven by enhancer malfunction.

The impact of cellular activities on large-scale brain dynamics is thought to determine brain functioning and disease, yet the causal relationships of neural mechanisms across scales remain unclear. Recently, the cerebellum has been reported to affect whole-brain dynamics during sensorimotor integration. To disclose the underlying mechanisms, we have developed a multiscale digital brain co-simulator, in which a spiking neural network of the olivo-cerebellar microcircuit is embedded in a mouse virtual brain and wired with other nodes using an atlas-based long-range connectome. Parameters and bi-directional interfaces between the spiking olivo-cerebellar network and other rate-coded modules were tuned to match experimental data of primary sensory and motor cortex (M1 and S1) power spectral densities and neuronal spiking rates. Then, the role of the cerebellar circuitry on sensorimotor integration was analyzed by lesioning critical circuit connections in silico. Simulations showed that spike processing within the cerebellar circuit is key to explaining the gamma-band coherence between M1 and S1 during sensorimotor integration. These results provide a mechanistic explanation of how the cerebellum promotes the formation of sensorimotor contingencies in relevant cortical modules as the basis of its critical role in sensorimotor prediction. On a broader perspective, this modelling approach opens new perspectives for the multiscale investigation of brain physiological and pathological states in relation to specific cellular and microcircuit properties.

Brain-body interactions (BBIs) are fundamental to cognition and mental health, but their continuous multimodal dynamics remain difficult to extract. Previous approaches have been largely observational, and few frameworks enable these interacting processes to be modeled within an integrated generative system. Here, we applied a Predictive-Coding-Inspired Variational RNN (PV-RNN) to simultaneous EEG, ECG, and respiration recordings obtained from 33 participants during exteroceptive and interoceptive attention. The model learned a temporal hierarchy spanning modality-specific dynamics, multimodal associative integration, and sequence-level global states, and accurately reconstructed unseen physiological sequences. Specifically, the intermediate associative layer successfully captured the core complexities of BBI by extracting multiscale, nonlinear, and bidirectional coupling dynamics with variable temporal lags. Furthermore, the estimated precision (inverse variance) of latent variables representing BBI dynamics within this multimodal associative layer increased significantly during interoceptive attention. The magnitude of this condition-dependent precision enhancement correlated positively with subjective adaptive body controllability and negatively with psychiatric vulnerabilities, including rumination and trait anxiety. These findings identify a latent physiological signature of interoceptive attention and establish hierarchical generative modeling as an interpretable framework for extracting continuous BBI dynamics and linking multimodal physiology to cognitive and clinical characteristics.

Glioma is the most common primary malignant tumor of the brain, and accumulating evidence indicates that neuronal activity plays a pivotal role in tumor progression. In this study, neuronal activity is modulated in vitro using potassium chloride (KCl)-induced depolarization and midazolam (MDZ)-mediated suppression. MDZ is a neuronal activity modulation medication, commonly used for sedation, anxiolysis, and amnesia in clinics. After treatment, conditioned media derived from these neuronal cultures are subsequently co-cultured with glioma cells. EdU incorporation assays demonstrate that MDZ significantly inhibits glioma cell proliferation in vitro. Furthermore, an orthotopic xenograft glioma model is established to assess the anti-tumor efficacy of MDZ in vivo, as evaluated by tumor volume and Ki-67 immunostaining. Mechanistically, insulin-like growth factor 1 (IGF1) is identified as the neuronal-activity-regulated factor that promotes glioma growth through activation of the PI3K/AKT signaling pathway. Moreover, transcriptomic profiling of brain tissues reveals that MDZ attenuates neuronal activity and downregulates neuron-derived growth factors in both glioma and non-tumor regions, thereby exerting anti-tumor effects in vivo. Collectively, these findings demonstrate that MDZ suppresses glioma progression by suppressing neuronal activity and inhibiting neuron-derived trophic factors, providing new insights into the development of therapeutic strategies for glioma.

The evolution of the water-conducting xylem and sugar-conducting phloem tissues were key innovations in land plant evolution, enabling the origin of long-distance transport networks1. In extant vascular plants, phloem and xylem are linked functionally and always occur together2, though their evolutionary origin is unclear. This uncertainty is owed to the greater fossilisation potential of lignified xylem tracheids compared to thin-walled phloem cells3, 4, 5. Therefore, the fossil record of xylem is far more extensive than that of phloem, with the first definitive record of xylem being around 40 million years earlier6 than phloem7. This bias in the fossil record obscures characterisation of the origins of plant vasculature. In this study, this limitation is overcome by re-describing the "phloem-like" tissues of exceptionally preserved plants from the 407-million-year-old Rhynie chert8-15. We report that this tissue differs markedly from the phloem of extant plants, and propose its identification as a tissue of food-conducting cells (FCCs). Major histological differences were observed in the fossil plants, including no evidence for a pericycle, which in extant species delimits vascular from ground tissues, and the FCCs of the Rhynie chert plants were significantly larger in diameter than phloem cells. These differences suggest that early vascular plants lacked true phloem. However, putative sieve pores in the FCCs of Asteroxylon mackiei were identified. This represents to our knowledge the earliest record of sieve pores in the fossil record. Our results suggest an evolutionary scenario in which phloem features assembled gradually within FCCs, asynchronous to the evolution of xylem.

Trypanosoma cruzi is the causative agent of Chagas disease, a neglected illness with outdated treatments. Bromodomain factors (BDFs) are essential proteins that recognize acetylated lysines and have strong therapeutic potential. They form part of epigenetic complexes that regulate chromatin accessibility and, therefore, gene expression. However, little is known about their structure in trypanosomatids. Here, we used a combination of experimental and bioinformatic approaches to infer the stoichiometry and structure of T. cruzi bromodomain-containing complexes. By reconstructing the proximity networks of five BDFs using TurboID-directed proximity labeling, we identified highly interconnected components that assemble into the CRKT and NuA4 complexes. Using novel structure prediction strategies that systematically explore the stoichiometric space, we inferred that CRKT assembles into three distinct modules and NuA4 in two, with different degrees of interaction dynamics. The core module of CRKT contains two copies of each component, including BDF3, BDF5, and BDF8, arranged in a subcomplex with central symmetry. The catalytic module of CRKT has three subunits, including the histone acetyltransferase 2 (HAT2), while the BET (bromodomain and extra-terminal) module has one unit of both BDF4 and BDF1. The catalytic module of NuA4 closely resembles the yeast piccolo-NuA4 module and contains HAT1, while the TINTIN module associates with the catalytic module via the C-terminal domain of BDF6. These insights shed light on the structure and composition of epigenetic complexes in trypanosomatids, opening new avenues for rational drug design aimed at disrupting their function.

The cGAS-STING pathway has been widely recognized as a critical DNA-sensing pathway that plays a broad-spectrum antiviral role. Livestock, especially pigs, represents one of the most important meat sources. In this study, we identified a key lysine 61 (K61) of porcine STING (pSTING) that plays an essential role in its degradation and antiviral signaling in a species-specific manner, with K61 as the major lysine of pSTING for K48-linked ubiquitination. After virus infection, pSTING recruits the E3 ligase, RNF5, which specifically assembles a K48-linked ubiquitin chain at K61, thereby mediating pSTING proteasomal degradation and reducing its antiviral activity. Meanwhile, the deubiquitylation of K61 is mediated mainly by deubiquitinase USP20, which enhances the stability and antiviral activity of pSTING. Together, given the relatively few lysine numbers in livestock STINGs and species-specific K61 regulation of pSTING stability and antiviral function, the K61 and its specific regulatory enzymes of pSTING could serve as potential targets for breeding of antiviral pigs and design of antiviral drugs, respectively.

BackgroundMonoamine transporters (MATs), including the dopamine, norepinephrine, and serotonin transporters (DAT, NET, SERT), are essential regulators of synaptic neurotransmission that rely on complex oligomeric bio-assemblies for proper function. The regulatory influence of naturally occurring, alternatively spliced truncated isoforms on bio-assembly dynamics remains profoundly underexplored. MethodsTo decode these interactions at the atomic level, we deployed a multiscale computational framework. We integrated genomics-guided multimer predictions with massive-scale, near-million-atom molecular dynamics (MD) simulations within explicit lipid bilayers. The thermodynamic stability of these heteromeric complexes was quantified using membrane-adapted MM/PBSA calculations, which were subsequently correlated with dynamics-aware evolutionary profiling to map co-evolutionary interaction hotspots. ResultsOur analyses reveal that the NET-derived truncated isoform A0A804HLI4 acts as a pan-family potential inhibitor. It forms stable, exergonic heterodimers with canonical NET and DAT, thermodynamically outcompeting native homodimerization. In full tetrameric simulations, the integration of a single isoform precipitates a macro-structural disruptions of the SERT complex. The variant anchors at non-native interfaces, locking the assembly into an asymmetric, non-native state. Residue-level thermodynamic decomposition and evolutionary mapping isolated conserved structural elements (most notably Gln236) that dictate this high-affinity cross-reactivity across the SLC6 family. ConclusionsTruncated MAT isoforms execute a dynamic mechanism of inhibitory effects and may systematically downregulate synaptic reuptake capacity by sequestering functional monomers. These findings establish a thermodynamically grounded, high-resolution model of isoform-induced bio-assembly disruptions. Crucially, they expose these non-canonical, isoform-driven interfaces as conserved and highly druggable targets, offering a distinct pharmacological paradigm for precision interventions in neuropsychiatric and neurodegenerative pathologies.

Precise control of gene expression is essential for optimizing metabolic pathways, yet current tuning strategies based on promoter strength or gene copy number remain largely empirical. Chromosomal position represents an additional regulatory axis, as identical gene expression cassettes can exhibit markedly different expression levels depending on their integration sites. Here, we systematically quantified the expression output of 98 intergenic regions (IGRs) in Saccharomyces cerevisiae using a fluorescent reporter and developed a predictive framework, Yeast IGR Prophet (YeIP), that infers expression potential directly from genomic context. By integrating multi-scale genomic features including transcriptional neighborhood, chromatin state, and chromosome topology, YeIP accurately predicted expression ranks and enabled the construction of a genome-wide atlas of expression hotspot and coldspot. Using this atlas, we rationally optimized a three-gene lycopene pathway solely through genomic integration site selection, achieving optimal transcriptional stoichiometry without modifying promoters or gene copy numbers. These results transform chromosomal integration sites from static genomic coordinates into programmable regulatory elements, establishing a predictive, data-driven framework for rational and scalable design of metabolic pathways in yeast.

Alzheimers disease (AD), the leading cause of dementia, affects over 33 million people worldwide, with pathogenesis tied to amyloid-{beta} (A{beta}) accumulation. Although anti-A{beta} monoclonal antibodies have shown clinical benefits, they often cause side effects including amyloid-related imaging abnormalities and brain microhemorrhage, especially in APOE E4 allele carriers. Here we used PHP.eB serotype adeno-associated virus (AAV), a vector with enhanced central nervous system (CNS) tropism, to deliver an A{beta} antibody expression vector (AAV-LEC) into the CNS of APP/PS1 and 5xFAD mice intravenously. The AAV-LEC-mediated expression of anti-A{beta} antibodies in the CNS significantly reduced the number and size of A{beta} plaques at various stages in both APP/PS1 and 5xFAD mice, alongside improved spatial learning and memory. It also reversed abnormal glial activation with reduced disease-associated microglia and astrocytes, and restored oligodendrocyte differentiation and myelin formation. No brain microhemorrhage or liver damage was detected following the AAV-antibody treatment. Thus, this AAV-mediated strategy offers a promising, convenient and safe AD therapeutic approach in the future.

Single-cell expression quantitative trait loci (sc-eQTL) analyses are powerful in identifying context-specific susceptibility genes from genome-wide association studies (GWAS) loci. However, few studies have comprehensively investigated cells of lung cancer origin in non-European populations. Here, we built a lung sc-eQTL dataset from 129 Korean women never-smokers with epithelial cell enrichment. eQTL mapping identified 2,229 genes with an eQTL in 33 cell types, including East Asian-specific findings when compared to predominantly European datasets. Integration with single-cell chromatin accessibility data demonstrated an enrichment of cell-type specific eQTLs in cell-type matched candidate enhancers, while shared eQTLs were more frequently found near promoters. Colocalization and transcriptome-wide association study unveiled 36 susceptibility genes from 22 cell types in 22 lung cancer loci, including 10 loci not achieving genome-wide significance in prior GWAS. Around 47% of these genes were from cells of the alveoli, underscoring their importance, especially in lung adenocarcinoma (LUAD) susceptibility. Focusing on the trajectory of alveolar epithelial cell regeneration, we detected 785 cell-state-interacting QTLs, which overlapped with 28% (10) of the identified susceptibility genes. Finally, we experimentally validated East Asian-and alveolar type 2 cell-specific eQTL of TCF7L2 underlying East Asian LUAD locus, 10q25.2. Consistent with its role as a Wnt/{beta}-catenin effector, TCF7L2 displayed significant effect on lung adenocarcinoma cell growth. Our data highlighted context-specific susceptibility genes, especially from alveolar cells of lung, contributing to lung cancer etiology.

The blood-brain barrier (BBB) severely restricts the delivery of systemically administered adeno-associated virus (AAV) vectors for central nervous system (CNS) gene therapy. To overcome this limitation, we engineered a library of AAV9 capsid variants through rational design focused on the low-density lipoprotein receptor-related protein 6 (LRP6), a conserved mediator of transcytosis. A multi-tiered screening strategy, encompassing human BBB endothelial cells followed by neuronal and glial target cells in vitro, identified three lead variants (QL9-21, QL9-22, and QL9-25) with markedly enhanced transduction potential. In mice, these variants achieved a 5-28 fold increase in brain-wide gene delivery compared to AAV9, without elevating hepatic tropism. Crucially, evaluation in non-human primates (NHPs) revealed that the lead variant, QL9-21, mediated a striking 3-40 fold enhancement in viral genome delivery across all examined brain regions versus AAV9, while concurrently reducing liver accumulation by 2.6 fold. Our study establishes an LRP6-guided engineering platform that yields novel AAV9 vectors capable of efficient, species-conserved BBB penetration coupled with a favorable safety profile, representing a significant advance toward clinically translatable CNS gene therapies.

The timing of the last eukaryotic common ancestor (LECA) remains a fundamental question in evolutionary biology and palaeontology. Unambiguous eukaryotic-grade fossils appear from 1780 Ma, but no crown-group supergroups are confidently identified before the end of the Mesoproterozoic (ca. 1050 Ma). The late LECA hypothesis suggests that this absence of crown-assignable fossils and biosignatures implies a late Mesoproterozoic origin of the crown. Here we show that this hypothesis is incompatible with the evolutionary dynamics of the eukaryote clade, even under the limited constraints of the fossil record. Studying stem-crown dynamics based on a birth-death model, we show that a late crown age requires diversification rates well below the minimum rate needed to generate observed living eukaryote diversity (~2.5 - 10 million species) for any plausible total group age. Our results suggest that only an early LECA can bridge evolutionary dynamics with the eukaryotic-grade fossil record, the living diversity, and the molecular clock estimates. Based on these constraints, we suggest a feasible minimum age estimate for LECA of ca. 1696 Ma, supported by current fossil evidence and supporting molecular clock estimates. These results also provide a fossil-testable prediction: crown-group eukaryotes likely exist in early Mesoproterozoic assemblages, albeit undetected with current morphology-based approaches.

Synapses are known to remodel their proteome during sleep. However, the exact mechanisms driving this remodelling and its impact on synaptic function or cognition are not well understood. We combine 24-hour EEG recordings with time-resolved synaptic proteomics in a model of SYNGAP1-Related Disorders to reveal a mechanism by which slow-waves and spindles, two NREM sleep oscillations, mediate the remodelling of the synaptic proteome. Moreover, we uncover that this remodelling promotes synaptic stabilization, which could support sleep-dependent memory consolidation. In contrast, the increase of slow-waves and decrease of spindles found in Syngap1+/- mice would activate molecular pathways involved in synaptic weakening instead of stabilization. This is consistent with the proposed roles of slow-waves and spindles in synaptic downscaling and potentiation, respectively. Here, we provide evidence on how NREM oscillations regulate the synaptic proteome and reveal a pathological mechanism that could be of relevance to all neurodevelopmental disorders coursing with sleep disturbances.

Nonlinearity is a hallmark of brain complexity at multiple scales. However, existing functional magnetic resonance imaging (fMRI) functional connectivity studies typically utilize linear methods. Therefore, links between nonlinear fMRI connectivity patterns and the development of the human brain during critical periods such as infancy remain unclear. To address this gap in knowledge, we developed a data-driven approach to capture brain intrinsic connectivity networks from explicitly nonlinear resting-state fMRI connectivity and profiled their developmental associations in a cohort of typically developing human infants. We identified neurobiologically structured nonlinear fMRI connectivity patterns during early postnatal life, indicating that macroscopic brain ensembles systematically participate in nonlinear relationships at birth. Furthermore, we found that linear and explicitly nonlinear network counterparts are linked to partially overlapping but complementary developmental profiles during this period of rapid brain maturation, with the explicitly nonlinear approach unveiling insights into the development of networks that have been associated with sensorimotor capacities, default mode processes, executive functioning, language production, and stimulus saliency. Our study marks the first comprehensive developmental investigation of whole-brain nonlinear fMRI networks in human infants and deepens contemporary perspectives on neuroimaging data discovery by emphasizing the informational richness of nonlinear relationships at fMRI scales of observation.

Frailty is a prevalent geriatric syndrome, and the shortage of objective biomarkers restricts its early diagnosis and intervention. This study aimed to identify robust molecular signatures and diagnostic markers for frailty using bioinformatics analyses of multiple independent datasets. Two transcriptome datasets (GSE144304, n=80; GSE287726, n=70) were obtained from the GEO database. We performed differential gene expression analysis, GO, KEGG and GSEA enrichment, and machine learning (70% training / 30% validation) to screen and validate core biomarkers. Numerous shared differentially expressed genes were identified. Vitamin D metabolism, ABC transporter, and inflammatory/immune pathways were consistently enriched and confirmed by GSEA. Machine learning models based on these signatures showed favorable diagnostic performance. Our study demonstrates that vitamin D metabolic disorders and chronic inflammation are core molecular features of frailty. The identified biomarkers provide new strategies for basic research, early clinical diagnosis, and therapeutic target development for frailty.

ObjectiveAiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale studies, and the lack of direct validation of single-cell systems biology models in human samples, this study aims to verify whether the results of transcriptome-wide Mendelian randomization (TWMR) based on large-scale populations are consistent with the causal inference results of deep learning combined with double machine learning (DML) using single-cell transcriptome data from human samples, to clarify whether statistical biology and systems biology can converge to the same biological truth, and provide methodological support for mechanism dissection and precision medicine research of complex diseases such as rheumatoid arthritis (RA). MethodsThis study integrated multi-omics data to conduct a two-stage causal inference and cross-scale validation analysis. In the first stage, based on the summary statistics of RA genome-wide association study (GWAS) from 456,348 individuals of European ancestry in the UK Biobank (UKB), and cis-expression quantitative trait locus (cis-eQTL) data from 31,684 individuals in the eQTLGen Consortium, a two-sample Mendelian randomization approach was adopted. Transcriptome-wide causal effect analysis was performed using the inverse-variance weighted (IVW) method, MR Egger regression, and weighted median method, and gene-level causal effect values were obtained after strict quality control and multiple testing correction. In the second stage, based on single-cell RNA sequencing (scRNA-seq) data from RA patients and healthy controls (RA group: 11 samples, 211,867 cells; Healthy control group: 38 samples, 456,631 cells), after preprocessing via the Seurat pipeline, batch effect correction, and cell type annotation, a hierarchical deep neural network was constructed to complete feature compression of high-dimensional expression data, and the DML framework was used to estimate the causal effects of genes on RA disease status. Finally, Pearson correlation analysis was performed to conduct cell type-specific cross-scale validation of gene-level causal effect values obtained by the two methods, and the validated model was used to quantify the causal effects of 16 RA-related pathways from the Reactome database. ResultsThis study confirmed that the gene causal effect values obtained from large-scale population TWMR analysis were significantly correlated with those calculated by the deep learning combined with DML model based on single-cell transcriptome data. Among them, the correlation was extremely significant (p<0.001) in core naive B cells (r=0.202, p=3.2e-05, n=414) and core naive CD4 T cells (r=0.102, p=0.037, n=412). The validated DML model successfully quantified the cell type-specific causal effect values of 16 RA-related signaling pathways. ConclusionStatistical biology and systems biology can converge to the same biological truth. The cross-scale consistency between the two can significantly shorten the "translational distance" in biomedical research, and realizes the direct validation of the single-cell systems biology causal model of human samples based on large-scale population genetic data, getting rid of the excessive dependence on animal/cell experimental models in traditional research. This research paradigm not only provides a new path for mechanism dissection and therapeutic target screening of complex diseases such as RA, but also provides a feasible solution for rare disease research to break through the limitation of GWAS sample size, and lays an important theoretical and methodological foundation for constructing standardized systems biology models of human complex diseases and promoting the development of precision medicine.

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

Alzheimers disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss, cognitive decline, and emotional dysregulation. Adult hippocampal neurogenesis (AHN) critically contributes to cognition and mood but undergoes precipitous decline during AD progression. Here, we investigated whether enhancing AHN through genetic expansion of endogenous neural stem cells (NSC) ameliorates AD-related phenotypes. Using lentiviral overexpression of the cell cycle regulators Cdk4 and CyclinD1 in the dentate gyrus of 3xTg-AD mouse, we show that enhancing AHN partially rescues hippocampal-specific cognitive functions, namely: spatial navigation and exploratory behavior. These findings show that endogenous NSC can be exploited to ameliorate hippocampal cognitive functions in AD, providing additional evidence for exploiting AHN as a promising therapeutic target for neurodegenerative disease.