Human Datasets Research Articles

Scale-free behavior of weight distributions of connectomes

To determine the precise link between anatomical structure and function, brain studies primarily concentrate on the anatomical wiring of the brain and its topological properties. In this work, we investigate the weighted degree and connection length distributions of the KKI-113 and KKI-18 human connectomes, the fruit fly, and the mouse retina. We find that the node strength (weighted degree) distribution behavior differs depending on the considered scale. On the global scale, the distributions are found to follow a power-law behavior, with a roughly universal exponent close to 3. However, this behavior breaks at the local scale as the node strength distributions of the KKI-18 follow a stretched exponential, and the fly and mouse retina follow the log-normal distribution, respectively, which are indicative of underlying random multiplicative processes and underpins nonlocality of learning in a brain close to the critical state. However, for the case of the KKI-113 and the H01 human (1mm3) datasets, the local weighted degree distributions follow an exponentially truncated power law, which may hint at the fact that the critical learning mechanism may have manifested at the node level too. Published by the American Physical Society 2025

Towards Realistic Human Motion Prediction with Latent Diffusion and Physics-Based Models

Many applications benefit from the prediction of 3D human motion based on past observations, e.g., human–computer interactions, autonomous driving. However, while existing methods based on encoding–decoding achieve good performance, prediction in the range of seconds still suffers from errors and motion switching scarcity. In this paper, we propose a Latent Diffusion and Physical Principles Model (LDPM) to achieve accurate human motion prediction. Our framework performs human motion prediction by learning information about the potential space, noise-generated motion, and combining physical control of body motion, where physics principles estimate the next frame through the Euler–Lagrange equation. The framework effectively accomplishes motion switching and reduces the error accumulated over time. The proposed architecture is evaluated on three challenging datasets: Human3.6M (Human 3D Motion Capture Dataset), HumanEva-I (Human Evaluation dataset I), and AMASS (Archive of Motion Capture as Surface Shapes). We experimentally demonstrate the significant superiority of the proposed framework in the prediction range of seconds.

CD73 promotes the maturation of murine NK cells and their survival in the tumor microenvironment.

Natural Killer (NK) cells are crucial in recognizing and eliminating tumor cells, making them pivotal in antitumor responses. Adenosine, the product of ATP hydrolysis mediated by CD39 and CD73 ectonucleotidases, has been reported to reduce the proliferation and maturation of NK cells. In this study, we investigate the expression of CD73 in NK cells and its impact on maturation, phenotype, survival, and function. Our findings reveal that while splenic NK cells express minimal levels of CD73, its expression is induced upon activation and in the tumor microenvironment upon adoptive transfer to tumor-bearing mice. Notably, within the tumor microenvironment, CD73 expression in NK cells correlates with elevated levels of PD-L1 and CD226. Accordingly, analysis of human melanoma datasets uncovers a subset of immature tumor-infiltrating NK cells expressing CD73. To further understand the role of CD73 on NK cells, we used a CD73 knockout (KO) murine model and observed that CD73-deficient NK cells display a more immature phenotype and heightened proliferative activity than wild-type (WT) NK cells. Additionally, CD73-deficient NK cells exhibit elevated levels of P2X7R and reduced CD39 expression, suggesting an increased susceptibility to ATP-induced death. Following adoptive transfer to tumor-bearing mice, CD73KO NK cells are present at a lower frequency but demonstrate similar control over tumor growth compared to WT NK cells. In conclusion, our study demonstrates the upregulation of CD73 in NK cells infiltrating tumors and underscores its role as a checkpoint regulating the functional maturation of NK cells.

Stroma gene signature predicts responsiveness to chemotherapy in pancreatic ductal adenocarcinoma patient-derived xenograft models.

Despite many efforts to understand the molecular mechanisms of pancreatic ductal adenocarcinoma (PDAC) treatment resistance, there is still no reliable method for selecting patients who could benefit from standard pharmacological treatment. Here, four PDAC patient-derived xenografts (PDAC-PDXs) with different responses to gemcitabine plus nab-paclitaxel (nanoparticle albumin-bound paclitaxel) were studied to dissect the contribution of both tumor and host microenvironment to treatment response. PDAC-PDXs transplanted into the pancreas of immunodeficient mice retained the main genetic and histopathological characteristics of the original human tumors, including invasiveness and desmoplastic reaction. Response to chemotherapy was associated with a specific 294 stroma gene signature and was not due to the intrinsic responsiveness of tumor cells or differences in drug delivery. Human dataset analysis validated the expression of the 294 stroma gene signature in PDAC clinical samples, confirming PDAC-PDXs as a useful tool to study the biology of tumor-host interactions and to test drug efficacy. In summary, we identified a stroma gene signature that differentiates PDAC-PDXs that are responsive to gemcitabine plus Nab-paclitaxel treatment from those that are not, confirming the active role of the tumor microenvironment in the drug response.

SpaGraphCCI: Spatial cell-cell communication inference through GAT-based co-convolutional feature integration.

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better inferspatial cell-cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single-cell resolution datasets (AUC reaches 0.860-0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning-based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell-cell communication based on spatial transcriptome data.

Characterization of SARS-CoV-2 Entry Genes in Skeletal Muscle and Impacts of InVitro Versus InVivo Infection.

COVID-19 has been associated with both respiratory (diaphragm) and non-respiratory (limb) muscle atrophy. It is unclear if SARS-CoV-2 infection of skeletal muscle plays a role in these changes. This study sought to: 1) determine if cells comprising skeletal muscle tissue, particularly myofibres, express the molecular components required for SARS-CoV-2 infection; 2) assess the capacity for direct SARS-CoV-2 infection and its impact on atrophy pathway genes in myogenic cells; and 3) in an animal model of COVID-19, examine the relationship between viral infection of skeletal muscle and myofibre atrophy within the diaphragm and limb muscles. We used in silico bioinformatics analysis of published human single cell RNA-seq datasets, as well as direct qPCR examination of human myotubes and diaphragm biopsies, to assess expression of key genes involved in SARS-CoV-2 cellular entry. InVitro, we determined the ability of SARS-CoV-2 to directly infect myogenic cells and employed qPCR to assess the impact on muscle atrophy pathway genes (ubiquitin-proteasome, autophagy). Invivo, the diaphragm and quadriceps of Roborovski hamsters with SARS-CoV-2 respiratory infection were examined at day 3 post-inoculation to evaluate the relationship between atrophy pathway and SARS-CoV-2 transcripts by qPCR, as well as histological measurements of myofibre morphology. Angiotensin converting enzyme 2 (ACE2), the primary receptor for SARS-CoV-2, as well as cooperating proteases (furin, cathepsins B and L), are expressed by myofibres. ACE2 expression was increased 5-fold (p = 0.01) in the diaphragms of mechanically ventilated human subjects compared to controls. InVitro, a time-dependent increase of SARS-CoV-2 transcript levels was observed in myotubes directly exposed to the virus (p = 0.002). This was associated with downregulation of the ubiquitin ligase MuRF1 (by 64%, p = 0.002) and the autophagy gene LC3B (by 31%, p = 0.009). In contrast, invivo infection led to upregulation of MuRF1 in quadriceps (23-fold, p = 0.0007) and autophagy genes in both quadriceps (5.2-fold for Gabarapl1, p = 0.03; 7-fold for p62, p = 0.0002) and diaphragm (2.2-fold for Gabarapl1, p = 0.03; 2.3-fold for p62, p = 0.057). In infected hamsters the diaphragm lacked viral transcripts but exhibited atrophy (48% decrease in myofibre area; p = 0.02), whereas the quadriceps lacked myofibre atrophy despite elevated viral transcripts in the muscle. Although myogenic cells express the genes required for SARS-CoV-2 entry and can be directly infected, there was no evident relationship between viral transcript levels and manifestations of atrophy, either invitro or invivo. Our results do not support direct myofibre infection by SARS-CoV-2 as a likely cause of atrophy in COVID-19.

PPARα-mediated lipid metabolism reprogramming supports anti-EGFR therapy resistance in head and neck squamous cell carcinoma

Anti-epidermal growth factor receptor (EGFR) therapy (cetuximab) shows a limited clinical benefit for patients with locally advanced or recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), due to the frequent occurrence of secondary resistance mechanisms. Here we report that cetuximab-resistant HNSCC cells display a peroxisome proliferator-activated receptor alpha (PPARα)-mediated lipid metabolism reprogramming, with increased fatty acid uptake and oxidation capacities, while glycolysis is not modified. This metabolic shift makes cetuximab-resistant HNSCC cells particularly sensitive to a pharmacological inhibition of either carnitine palmitoyltransferase 1A (CPT1A) or PPARα in 3D spheroids and tumor xenografts in mice. Importantly, the PPARα-related gene signature, in human clinical datasets, correlates with lower response to anti-EGFR therapy and poor survival in HNSCC patients, thereby validating its clinical relevance. This study points out lipid metabolism rewiring as a non-genetic resistance-causing mechanism in HNSCC that may be therapeutically targeted to overcome acquired resistance to anti-EGFR therapy.

Chinese sign language recognition and translation with virtual digital human dataset

Chinese sign language recognition and translation with virtual digital human dataset

Differential transcriptomic profiling of lipid metabolism and collagen remodeling in fast- and slow-twitch skeletal muscles in aging.

Skeletal muscle function gradually declines with aging, presenting substantial health and societal challenges. Comparative analysis of how aging affects fast- and slow-twitch muscles remains lacking. We utilized 20-month-old mice to reveal the aging effects on muscle structure and fiber composition, followed by bulk RNA sequencing for fast- and slow-twitch muscles and integration with human single-cell RNA sequencing dataset providing a comparative analysis across species. In mouse slow-twitch muscles, aging induced a switch from fast to slow fibers and distinctively altered lipid metabolism in ceramide and triglyceride, with the upregulation of regulatory genes Gk and Ppargc1a also observed in human slow fibers. Additionally, both types of muscles exhibited common collagen deposition and fibrosis, possibly due to the imbalance between collagen synthesis and degradation. The extracellular matrix gene changes substantially overlapped between mice and humans in aging, yet also highlighted clear differences. This integrative analysis provides further understanding of aged fast- and slow-twitch muscles and offers new insights into the molecular changes in aging.

Neoplastic immune mimicry is a generalizable phenomenon in breast cancer and epithelial CD69 enables early tumor progression.

Dedifferentiation programs are commonly enacted during breast cancer progression to enhance tumor cell fitness. Increased cellular plasticity within the neoplastic compartment of tumors correlates with disease aggressiveness, often culminating in greater resistance to cytotoxic therapies or the augmented ability to metastasize to distant organs. Here we report that subpopulations of dedifferentiated neoplastic breast epithelial cells express canonical leukocyte cell surface receptor proteins and have thus named this cellular program "immune mimicry." We document neoplastic cells engaging in immune mimicry within public human breast tumor single-cell RNA-seq datasets, histopathological breast tumor specimens, breast cancer cell lines, as well as in murine transgenic and cell line-derived mammary cancer models. Immune-mimicked neoplastic cells harbor hallmarks of dedifferentiation and appear enriched in treatment-resistant and high-grade breast tumors. We corroborated these observations in aggressive breast cancer cell lines where growth-arresting cytotoxic chemotherapies drove epithelial cells toward immune mimicry. Moreover, in subsequent proof-of-concept studies, we demonstrate that expression of the CD69 leukocyte activation marker by neoplastic cells confers a proliferative advantage that facilitates early tumor growth. We conclude neoplastic breast epithelial cells that upregulate leukocyte surface receptors potentiate malignancy. Moving forward, neoplastic immune mimicry should be evaluated for prognostic utility in breast cancer to determine its stratification potential for patients with increased risks of tumor recurrence, metastasis, and therapeutic resistance.

VAX014 Activates Tumor-Intrinsic STING and RIG-I to Promote the Development of Antitumor Immunity.

In situ immunization (ISI) has emerged as a promising approach to bolster early phases of the cancer immunity cycle through improved T cell priming. One class of ISI agents, oncolytic viruses (OVs), has demonstrated clinical activity, but overall benefit remains limited. Mounting evidence suggests that due to their inherent vulnerability to antiviral effects of type I interferon (IFN), OVs have limited activity in solid tumors expressing stimulator of interferon genes (STING) and/or retinoic acid-inducible gene I (RIG-I). Here, using a combination of pharmacologic, genetic, and in vivo approaches, we demonstrate that VAX014, a bacterial minicell-based oncolytic ISI agent, activates both STING and RIG-I and leverages this activity to work best in STING- and/or RIG-I-positive tumors. Intratumoral treatment of established syngeneic tumors expressing STING and RIG-I with VAX014 resulted in 100% tumor clearance in two mouse models. Antitumor activity of VAX014 was shown to be dependent on both tumor-intrinsic STING and RIG-I with additive activity stemming from host-intrinsic STING. Analysis of human solid tumor datasets demonstrated STING and RIG-I co-expression is prevalent in solid tumors and associates with clinical benefit in many indications, particularly those most amenable to intratumoral administration. These collective findings differentiate VAX014 from OVs by elucidating the ability of this agent to elicit antitumor activity in STING- and/or RIG-I-positive solid tumors and provide evidence that STING/RIG-I agonism is part of VAX014's mechanism of action. Taken together, this work supports the ongoing clinical investigation of VAX014 treatment as an alternative to OV therapy in patients with solid tumors.

HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM

Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.

Spatial and temporal distribution patterns and conservation status of seagrasses in the Yellow Sea and Bohai Sea.

Seagrasses represent a significant class of marine foundation species, yet the distribution of seagrasses in the Yellow Sea and Bohai Sea remains uncertain, thereby impeding the efficacy of conservation and restoration practices. In this study, the spatial and temporal distribution pattern of seagrasses was simulated by the MaxEnt model based on the construction of marine environment and human activity datasets. The main controlling factors affecting seagrass potential distribution were analyzed, and the response of seagrass distribution to global change was clarified. Additionally, the current status of protected and disturbed seagrass meadows was determined. The results indicate the accuracy of the MaxEnt model was enhanced (AUC=0.987) through the integration of a human activity index, pinpointing a highly suitable seagrass area of 867.64km2. The primary factors dictating seagrass distribution were identified as human activities index (18.4%), water quality indicators (including nitrate 20.9%, silicate 11.3%, and dissolved iron 6.4%), and topographic elements (such as bathymetry 15.1% and slope 12.0%). The highly suitable areas for seagrasses showed a gradual expansion in the future, with a projected increase of 12% in 2100 under the SSP585 scenario. A binary overlay analysis showed that only 2.61% of seagrass potential habitats (highly and moderately suitable areas) were adequately protected, highlighting a significant conservation gap in the Yellow Sea and Bohai Sea regions. On the contrary, seagrass potential habitats exposed to mariculture activities were as much as 49.76%. These findings underscore the urgent need for monitoring and protecting seagrasses in the study area, with the establishment of MPAs emerging as a viable conservation strategy. This research provides a scientific basis for understanding the degradation and conservation status of seagrass meadows and has important practical implications for targeted conservation and restoration efforts of these critical marine foundation species.

Longitudinal phage–bacteria dynamics in the early life gut microbiome

Microbial colonization of the human gut occurs soon after birth, proceeds through well-studied phases and is affected by lifestyle and other factors. Less is known about phage community dynamics during infant gut colonization due to small study sizes, an inability to leverage large databases and a lack of appropriate bioinformatics tools. Here we reanalysed whole microbial community shotgun sequencing data of 12,262 longitudinal samples from 887 children from four countries across four years of life as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We developed an extensive metagenome-assembled genome catalogue using the Marker-MAGu pipeline, which comprised 49,111 phage taxa from existing human microbiome datasets. This was used to identify phage marker genes and their integration into the MetaPhlAn 4 bacterial marker gene database enabled simultaneous assessment of phage and bacterial dynamics. We found that individual children are colonized by hundreds of different phages, which are more transitory than bacteria, accumulating a more diverse phage community over time. Type 1 diabetes correlated with a decreased rate of change in bacterial and viral communities in children aged one and two. The addition of phage data improved the ability of machine learning models to discriminate samples by country. Finally, although phage populations were specific to individuals, we observed trends of phage ecological succession that correlated well with putative host bacteria. This resource improves our understanding of phage–bacteria interactions in the developing early life microbiome.

AHNAK2: a potential diagnostic biomarker for pancreatic cancer related to cellular motility

Pancreatic ductal adenocarcinoma lacks suitable biomarkers for early diagnosis of disease. In gene panels developed for early diagnosis of pancreatic cancer, high AHNAK2 mRNA expression was one possible biomarker. In silico analysis of published human sample datasets (n = 177) and ex vivo analysis of human plasma samples (n = 30 PDAC with matched 30 healthy control) suggested AHNAK2 could be a diagnostic biomarker. At a plasma level of 421.47 ng/ml, AHNAK2 could potentially diagnose PDAC with a specificity and sensitivity of 83.33% and 86.67%. In vitro analysis suggests that in cell lines with diffuse cytoplasmic distribution of AHNAK2, there was colocalization of AHNAK2 with Cortactin in filipodia. This colocalization increased when cells were cultured on substrates such as Fibronectin and Collagen, as well as in hypoxia, and resulted in an augmented invasion of cancer cells. However, in cell lines with a vesicular AHNAK2 staining, such changes were not observed. Our study posits AHNAK2 as a valuable diagnostic biomarker in PDAC, now demanding prospective validation. Determination of mechanisms regulating AHNAK2 subcellular localisation may help explain its biological role.

Inferred Developmental Origins of Brain Tumors from Single Cell RNA-Sequencing Data

Abstract Background The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated developmental factors play a role in the progression of brain tumors is therefore crucial for identifying potential targets for therapeutic interventions. Single-cell RNA sequencing (scRNA-Seq) provides an opportunity to understand how developmental programs are dysregulated and reinitiated in brain tumors at single-cell resolution. The aim of this study is to identify developmental origins of brain tumors using scRNA-Seq data. Methods Here, we introduce COORS (Cell Of ORigin like CellS), a computational tool trained on developmental human brain single-cell datasets that annotates “developmental-like” cell states in brain tumors. COORS leverages cell type-specific multilayer perceptron models and incorporates a developmental cell type tree that reflects hierarchical relationships and models cell type probabilities. Results Applying COORS to various brain cancer datasets, including medulloblastoma (MB), glioma, and diffuse midline glioma (DMG), we identified developmental-like cells that represent putative cells of origin in these tumors. Our method provides both cell of origin classification and cell age regression, offering insights into the developmental cell types of tumor subgroups. COORS identified outer radial glia (oRG) developmental cells within IDHWT glioma cells whereas oligodendrocyte precursor cells (OPCs) and neuronal-like cells in IDHMut. Interestingly, IDHMut subgroup cells that map to OPC show bimodal distributions, that are both early and late weeks in development. Furthermore, COORS offers a valuable resource by providing novel markers linked to developmental states within of MB, glioma and DMG tumor subgroups. Conclusion Our work adds to our cumulative understanding of brain tumor heterogeneity and helps pave the way for tailored treatment strategies.

Benefits of spaced learning are predicted by the re-encoding of past experience in ventromedial prefrontal cortex.

More than a century of research shows that spaced learning improves long-term memory. However, there remains debate concerning why that is. A major limitation to resolving theoretical debates is the lack of evidence for how neural representations change as a function of spacing. Here, leveraging a massive-scale 7T human fMRI dataset, we tracked neural representations and behavioral expressions of memory as participants viewed thousands of natural scene images that repeated at lags ranging from seconds to many months. We show that spaced learning increases the similarity of human ventromedial prefrontal cortex representations across stimulus encounters and, critically, that these increases parallel and predict the behavioral benefits of spacing. Additionally, we show that these spacing benefits critically depend on remembering and, in turn, "re-encoding" past experience. Collectively, our findings provide fundamental insight into how spaced learning influences neural representations and why spacing is beneficial.

Integrated gene expression and alternative splicing analysis in human and mouse models of Rett syndrome

Mutations of the MECP2 gene lead to Rett syndrome (RTT), a rare developmental disease causing severe intellectual and physical disability. How the loss or defective function of MeCP2 mediates RTT is still poorly understood. MeCP2 is a global gene expression regulator, acting at transcriptional and post-transcriptional levels. Little attention has been given so far to the contribution of alternative splicing (AS) dysregulation to RTT pathophysiology. To perform a comparative analysis of publicly available RNA sequencing (RNA-seq) studies and generate novel data resources for AS, we explored 100 human datasets and 130 mouse datasets from Mecp2-mutant models, processing data for gene expression and alternative splicing. Our comparative analysis across studies indicates common species-specific differentially expressed genes (DEGs) and differentially alternatively spliced (DAS) genes. Human and mouse dysregulated genes are involved in two main functional categories: cell-extracellular matrix adhesion regulation and synaptic functions, the first category more significantly enriched in human datasets. Our extensive bioinformatics study indicates, for the first time, a significant dysregulation of AS in human RTT datasets, suggesting the crucial contribution of altered RNA processing to the pathophysiology of RTT.

Targeting EPHB2/ABL1 restores antitumor immunity in preclinical models of ependymoma

Ependymoma (EPN) is a common form of brain tumor in children, often resistant to available cytotoxic therapies. Molecular profiling studies have led to a better understanding of EPN subtypes and revealed a critical role of oncogenes ZFTA-RELA fusion and EPHB2 in supratentorial ependymoma (ST-EPN). However, the immune system's role in tumor progression and response to therapy remains poorly understood. New treatments for various molecular subtypes of EPN are desperately needed. Using ST-EPN-ZFTA subtype-specific syngeneic mouse models, we found an increased frequency of M2-like tumor-associated macrophages (TAMs), which proportionally increased with tumor size during tumor progression. Transcriptomic profiling of ST-EPN-ZFTA and analysis of a human EPN dataset revealed multiple protein kinases as potential druggable targets. By matching transcriptomic signatures with the target spectrum of FDA-approved drugs, we found that the multikinase inhibitor dasatinib potently inhibited the growth of EPN both in vitro and in vivo, mainly through blocking EPHB2 and ABL1. Treatment with dasatinib reprogrammed the EPN immune microenvironment by polarizing TAMs toward an M1-like phenotype and increasing CD8 T cell activation. Furthermore, dasatinib treatment induced complete regression of established EPN tumors in 78% of the animals and protected survivors against tumor recurrence. Depletion of CD8 cells compromised the durability of EPN responses and reduced overall survival. These data indicate that dasatinib has the potential to be an effective therapy for ST-EPN-ZFTA molecular subgroup of EPN and support further investigation of dasatinib in clinical trials.

Proximity labeling of the Tau repeat domain enriches RNA-binding proteins that are altered in Alzheimer's disease and related tauopathies.

In Alzheimer's disease (AD) and other tauopathies, tau dissociates from microtubules and forms toxic aggregates that contribute to neurodegeneration. Although some of the pathological interactions of tau have been identified from postmortem brain tissue, these studies are limited by their inability to capture transient interactions. To investigate the interactome of aggregate-prone fragments of tau, we applied an in vitro proximity labeling technique using split TurboID biotin ligase (sTurbo) fused with the tau microtubule repeat domain (TauRD), a core region implicated in tau aggregation. We characterized sTurbo TauRD co-expression, robust enzyme activity and nuclear and cytoplasmic localization in a human cell line. Following enrichment of biotinylated proteins and mass spectrometry, we identified over 700 TauRD interactors. Gene ontology analysis of enriched TauRD interactors highlighted processes often dysregulated in tauopathies, including spliceosome complexes, RNA-binding proteins (RBPs), and nuclear speckles. The disease relevance of these interactors was supported by integrating recombinant TauRD interactome data with human AD tau interactome datasets and protein co-expression networks from individuals with AD and related tauopathies. This revealed an overlap with the TauRD interactome and several modules enriched with RBPs and increased in AD and Progressive Supranuclear Palsy (PSP). These findings emphasize the importance of nuclear pathways in tau pathology, such as RNA splicing and nuclear-cytoplasmic transport and establish the sTurbo TauRD system as a valuable tool for exploring the tau interactome.