Biology subjects

Ye, P.

Publications and source records attributed to Ye, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Disease-associated NK-cell state remodeling reveals transcriptional profiles predictive of clinical phenotypes

Natural killer (NK) cells adapt their cellular states to diverse tissue and inflammatory environments, yet how this plasticity manifests across pathological contexts and relates to patient-level disease phenotypes remains poorly defined. Here we established the Human NK-cell Atlas (HNKA) from over 1 million NK cells, including samples from 1,632 donors spanning 50 diseases. Within a shared NK-cell state architecture, analyses of state abundance and matched-reference exceedance distinguished disease-biased redistribution from transcriptional displacement. Fine-grained analyses localized disease-associated remodeling to specific NK-cell states, including CD56dim NK ISG15 in infection and autoimmunity and CD56bright NK ITM2C in tumors. These patterns were accompanied by recurrent interferon responses and state-specific regulatory activity. Cytokine perturbations recapitulated key transcriptional features of disease-associated NK-cell states. We developed NKceiver, a model based on state-specific NK-cell expression profiles for patient-level prediction. It distinguished 18 diseases and predicted COVID-19 severity and response to immune checkpoint inhibitors, with predictive performance retained in unseen patients and studies. Collectively, our results reveal that disease-associated remodeling within the diverse NK-cell state repertoire resolved by HNKA is biologically interpretable and clinically informative, supporting disease discrimination and prediction of clinical phenotypes.

immunology↗

bioRxiv2026

Scaling genetic discovery through automated post-GWAS interpretation

Genome-wide association studies (GWAS) have made association discovery systematic and cumulative, yet evidence for interpreting associated loci remains selective, heterogeneous and difficult to accumulate across studies. Here we present Post-GWAS Intelligence (PGI), which couples context-dependent post-GWAS analysis to a common representation of analysis-derived evidence. Its multi-agent engine, VariantAgent, executes workflows, records tool-derived results in standardized variant-centred units and generates trait-level interpretations linked to their analytical basis and evidential status. Complementary benchmarks showed that VariantAgent outperformed tested general-purpose and biomedical agents, with its largest advantage when success required both a correct answer and a valid analytical trace. Across 34 published GWAS, VariantAgent achieved a mean per-study recovery of 82.1% for directly comparable findings. The largest gains lay downstream of association mapping, where uniform analysis extended published interpretations and identified regulatory relationships at established risk loci not previously reported in the source studies. Analysis of 1,041 GWAS yielded 530,949 evidence units and corresponding reports, which PGI organized into a cross-trait catalogue. Catalogue interrogation resolved an apparent cross-trait overlap into distinct signals and trait-specific biological hypotheses. This work demonstrates how PGI can extend genetic discovery beyond association mapping by enabling biological evidence to accumulate across studies.

genetics↗

bioRxiv2026

Humanized tauopathy chimeras uncover microglial and lncRNA strategies for neuroprotection

Human genetics implicates innate immunity as a key modifier of tau toxicity, yet human-specific neuroimmune mechanisms remain difficult to test in vivo. Here, we developed HuMiNAX, the first humanized iPSC-based neuroimmune xenograft model of tau-associated neurodegeneration, enabling human microglia to interact with human neurons and astrocytes in the adult mouse brain. In HuMiNAX, tau seeding induced aggregation only in mutation-carrying human neural grafts, causing neuron loss and inflammatory activation of human microglia. Progranulin-overexpressing human microglia dampened tau-associated inflammation, preserved neurons, and restored neuronal gene-expression and RNA-splicing programs, supporting microglial control of neuronal resilience. CRISPRi knockdown of the human-specific lncRNA HNRNPK-AS1 also protected neurons in HuMiNAX. These findings establish HuMiNAX as a human neuroimmune model of tauopathy and identify microglial and RNA-mediated strategies of neuronal resilience.

neuroscience↗

bioRxiv2026

Microglial sTREM2 limits dyskinesia and acts on TrkB to support circuit plasticity

Microglia continuously survey the brain and shape neuronal activity, but their contribution to experience-dependent synaptic plasticity is unclear. Levodopa-induced dyskinesia (LID) is a disabling complication of late-stage Parkinsons disease (PD) that is linked to maladaptive striatal remodeling and is often assumed to reflect detrimental neuroinflammation. Here we identify a dyskinesia-associated microglial gene program in the striatum of PD patients and show that microglia instead act as a protective brake on LID. In a mouse model, microglial depletion exacerbated dyskinesia, whereas microglial repopulation mitigated it. Delivery of AAV expressing soluble TREM2 (sTREM2) similarly reduced LID without impairing the therapeutic benefit of levodopa. Single-nucleus transcriptomics revealed that microglial loss drives extensive remodeling of both direct and indirect spiny projection neurons (SPNs), while repopulation or sTREM2 reverses a large fraction of LID-associated transcriptional changes. Mechanistically, sTREM2 directly engages TrkB and potentiates BDNF-dependent TrkB-ERK signaling, consistent with positive allosteric modulation. Functionally, sTREM2 enhances BDNF-TrkB-dependent hippocampal synaptic plasticity and acutely rebalances striatal dendritic excitability in a compartment- and cell type-specific manner. These findings reveal an unexpected neuroimmune pathway in which microglia restrain maladaptive plasticity via sTREM2-TrkB signaling, with therapeutic implications.

neuroscience↗

bioRxiv2026

cGAS inhibition delays TDP-43-driven ALS Pathogenesis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder marked by motor neuron loss and cytoplasmic mislocalization of TAR DNA-binding protein 43 (TDP-43), a key regulator of RNA splicing. However, the upstream modulators of this process remain poorly defined. Here we identify cyclic GMP-AMP synthase (cGAS) as a central mediator of TDP-43 pathology and associated mis-splicing. cGAS expression was elevated in ALS patient brains and enriched across activated microglia. In human iPSC-derived microglia-motor neuron co-cultures, neuronal TDP-43 pathology triggered microglial cGAS activation, whereas pharmacological inhibition with a potent human cGAS inhibitor reduced phosphorylated TDP-43, restored lysosomal and phagocytic programs, normalized microglial reactivity, and reversed TDP-43-associated RNA splicing defects. In vivo, cGAS inhibition in TDP-43 Q331K mice reversed widespread RNA splicing abnormalities across neurons and oligodendrocyte lineage cells, attenuated neurodegenerative pathology, and preserved motor function. Together, these findings identify cGAS as a druggable upstream regulator linking innate immune signaling to TDP-43-dependent RNA mis-splicing and neurodegeneration, and establish cGAS inhibition as a promising therapeutic strategy for ALS.

neuroscience↗

bioRxiv2026

cGAS-mediated IFN-I signaling contributes to disease progression in drug-refractory epilepsy

Epilepsy is a prevalent neurological disease with a third of patients becoming non-responsive to antiepileptic drugs and developing drug-refractory epilepsy (DRE). Here we report that DRE disease progression is contributed by overactive cyclic GMP-AMP synthase (cGAS), a double-stranded DNA sensor that induces type I interferon (IFN-I) signaling. In human DRE microglia, we observe a robust IFN-I signature and the activation of upstream cGAS-STING signaling. Further, in mouse models of Dravet syndrome, a genetic form of DRE, we observe the activation of the cGAS pathway. We show that microglial cGAS can be activated by DNA released from hyperexcitable neurons. Genetic reduction and pharmacological inhibition of cGAS reduces epileptic phenotypes, glial inflammatory signatures, and neuronal transcriptomic changes, underscoring the therapeutic potential of targeting cGAS for DRE treatment.

neuroscience↗

bioRxiv2026

Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs oligodendrocyte functions and alters neuronal activity

Aging is the major risk factor for neurodegenerative disease, yet the mechanisms linking physiological aging to brain dysfunction remain unclear. Because telomere erosion is a hallmark of aging, we examined its impact on glial and neuronal physiology. Telomere shortened mice showed lipofuscinosis, hypomyelination, microglial atrophy, and cognitive deficits. Single nuclei RNA-seq revealed accelerated glial aging, elevated microglial senescence pathways, and impaired oligodendrocyte functions. Inducing senescence in human iPSC derived microglia with shortened telomeres identified soluble DLK1 as a novel senescence associated ligand. sDLK1 was increased in the cerebrospinal fluid of telomere shortened and naturally aged mice, and this increase was eliminated by microglial depletion. AAV delivery of sDLK1 in vivo caused hypomyelination and blocked oligodendrocyte lineage progression, demonstrating the detrimental nature of excessive sDLK1. In human iPSC systems, sDLK1 impaired oligodendrocyte maturation and altered calcium signaling in excitatory neurons. These findings identify microglial senescence as a core consequence of telomere shortening and reveal sDLK1 as a microglia-derived senescence ligand that drives oligodendrocyte and neuronal dysfunction in aging.

neuroscience↗

bioRxiv2026

Sequential evolution of antidote and toxin links genetic incompatibility with immune responses

Toxin-antidote (TA) systems are selfish genetic elements that promote their own inheritance by selectively eliminating offspring lacking the module, thereby establishing post-zygotic genetic incompatibilities between individuals and populations. Although these systems are widespread across species, their evolutionary origins remain poorly understood. Here, we report the discovery of a novel TA gene pair in the nematode Caenorhabditis nigoni. The antidote gene, Cni-shls-2, is a species-specific F-box gene that arose through recent tandem duplications. Its absence results in embryonic lethality in both C. nigoni and its hybrids with the sister species C. briggsae. This lethality is caused by a maternally deposited toxin, Cni-hlix-1, a chimeric gene formed through the fusion of host and bacterial sequences. Phylogenetic and genomic analyses reveal a sequential evolution of the TA system, in which the antidote evolved prior to the toxin. The stepwise evolution of TA and the potential microbial origin of the toxin support the hypothesis that the antidote initially evolved in response to pathogen exposure, followed by the domestication of the toxin, thereby elucidating the origins of TA formation. These findings highlight the central role of host-pathogen conflict as a driving force in the emergence of genetic incompatibilities and the evolution of reproductive barriers.

evolutionary biology↗

bioRxiv2025

STING_IFN_CH25H lipid axis links innate immune activation to tau pathology

Genetic risk for Alzheimers disease is strongly enriched in pathways governing microglial activation and cholesterol metabolism, yet how these processes converge to drive neurodegeneration remains unclear. Here, we identify the oxysterol 25-hydroxycholesterol (25- HC) as a pathogenic lipid downstream of the cGAS-STING-IFN pathway. In models of tau pathology, interferon signaling induces Cholesterol 25-hydroxylase (CH25H) expression in microglia. Ch25h deletion in female P301S mice suppressed tau aggregation, preserved synapses, prevented brain atrophy, and rescued memory. Mechanistically, loss of CH25H disrupted STING trafficking, attenuated IFN activation, dampened self-perpetuating microglial inflammation. Strikingly, 25-HC directly accelerated tau propagation in human iPSC derived neurons. It also disrupted lysosomal and mitochondrial lipid composition, driving cholesteryl ester accumulation and promoting apoptosis under tau-induced stress. These findings define a STING-IFN-CH25H lipid axis that bridges innate immune activation to tau pathology and toxicity, offering a tractable therapeutic pathway for inflammation-driven neurodegenerative conditions.

neuroscience↗

bioRxiv2025

scMOBA: A conversational single-cell Multi-Omics Brain Agent across species

Single-cell and spatial multi-omics are revolutionizing our understanding of the complexity in the developmental, aging, and diseased brain, but integrating this knowledge across modalities and species remains challenging. To bridge this gap, we propose scMOBA, a conversational single-cell Multi-Omics Brain Agent established by a large language model, a gene encoder, and a cross-attention projector. The scMOBA was pre-trained on 130 million single-cell and spatial multi-omics data spanning the entire brain across diverse species, development, aging and diseases. The pre-training utilized a novel multi-omics Feature-Question-Answer (FQA) paradigm, enabling the model to generate biological answers from feature inputs and textual queries. This unique scheme facilitates superior zero-shot inference capabilities without requiring additional fine-tuning. We demonstrate that scMOBA achieves state-of-the-art performance in fine-grained cell type classification across different species and modalities, as well as in batch correction and multi-omics data integration. Furthermore, scMOBA significantly boosts the accuracy of kinds of critical downstream tasks, including cell-type specific aging clock construction and disease status prediction. Overall, scMOBA serves as a powerful scientific discovery engine for multi-omics brain research, advancing the precision prediction and early intervention of neurological aging and diseases.

bioinformatics↗

bioRxiv2025

Scalable integration and prediction of unpaired single-cell and spatial multi-omics via regularized disentanglement

Deciphering cellular states requires methods capable of integrating large-scale heterogeneous single-cell and spatial omics data. However, these data are typically unpaired due to destructive assays and further confounded by modality heterogeneity, technical noise, and immense scale. Here we present scMRDR, a scalable computational framework based on regularized disentangled representation learning for integrating fully unpaired single-cell and spatial multi-omics datasets. Built on a unified and structure-preserving architecture, scMRDR removes the need for pairing supervision while maintaining computational efficiency, enabling scaling to large datasets spanning multiple disparate omics modalities. Across diverse real-world benchmarks, scMRDR demonstrates strong performance in batch correction, modality alignment, and biological signal preservation. The framework further supports cross-modal translation across omics modalities and enables spatial coordinate imputation for non-spatial single-cell datasets using a reference atlas. The resulting spatial mapping allows spatially resolved analyses, including identification of spatially variable genes and characterization of epigenetic regulatory programs in their native tissue context. These capabilities position scMRDR as a scalable and versatile framework for large-scale multi-omics integration.

genomics↗

bioRxiv2025

Unveiling Protein-DNA Interdependency: Harnessing Unified Multimodal Sequence Modeling, Understanding and Generation

The interdependent orchestration of proteins, coding DNA sequences (CDS), and adjacent regulatory DNA elements underpins biological functionality, where evolutionary constraints shape codon usage, transcriptional regulation, and structural stability. We present ProDMM, a multimodal framework that deciphers these systemic relationships through unified sequence modeling. Integrating a context-aware encoder with conditional generation, our architecture captures codependent patterns between protein motifs and cis-regulatory syntax. ProDMMs encoder demonstrates superior predictive capabilities in forecasting pathway-level phenotypes and protein expression profiles, outperforming conventional single-modality approaches. Its decoder generates synthetically optimized regulatory elements by resolving spatial dependencies between coding sequences and adjacent non-coding regions, enabling transcriptional fine-tuning unavailable to sequence-agnostic methods. By resolving how localized CDS-NCDS-protein interdependencies propagate to system-level phenotypes, ProDMM establishes a paradigm for holistic biological sequence engineering. This approach enables coordinated optimization of translation efficiency, enzymatic activity, and pathway flux, offering transformative potential for designing tailored biosynthesis systems and advancing sustainable bioproduction strategies.

bioinformatics↗

bioRxiv2025

scWGBS-GPT: A Foundation Model for Capturing Long-Range CpG Dependencies in Single-Cell Whole-Genome Bisulfite Sequencing to Enhance Epigenetic Analysis

Single-cell DNA methylomes are challenging to interpret because of sparse CpG coverage and the complexity of genome-wide sequences. We present scDNAm-GPT, a universal foundation model that uses context-aware CpG tokenization, a Mamba backbone, and cross-attention to capture both local and global DNA methylation patterns. Trained on over one million single cells from 35 human and mouse tissues, scDNAm-GPT enables accurate cell clustering, zero-shot prediction of CpG effects on gene expression, improved trajectory inference, and reference-free deconvolution of cell types from cell-free DNA. The model hierarchically learns regulatory features, and its attention maps highlight functionally relevant regions, demonstrating high biological interpretability. These results establish scDNAm-GPT as a scalable and generalizable framework for single-cell epigenomic analysis, offering new opportunities to dissect epigenetic regulation in health and disease. Code is available at GitHub (https://github.com/ChaoqiLiang/scDNAm-GPT).

bioinformatics↗

bioRxiv2024

EmbSAM: Cell boundary localization and Segment Anything Model for fast images of developing embryos

Cellular shape dynamics are critical for understanding cell fate determination and organogenesis during development. However, fluorescence live-cell images of cell membranes frequently suffer from a low signal-to-noise ratio, especially during long-duration imaging with high spatiotemporal resolutions. This is caused by phototoxicity and photobleaching, which limit laser power and hinder effective time-lapse cell shape reconstruction, particularly in rapidly developing embryos. Here, we devised a new computational framework, EmbSAM, that incorporates a deep-learning-based cell boundary localization algorithm and the Segment Anything Model. EmbSAM enables accurate and robust three-dimensional (3D) cell membrane segmentation for roundworm Caenorhabditis elegans embryos imaged every 10 seconds. The cell shape data prior to gastrulation quantitatively characterizes a series of cell-division-coupled morphodynamics associated with cell position, cell identity, lineage, and fate, and can be accessed locally and online. The framework also exhibits potential in segmenting and quantifying the fluorescence labeling various cell-membraned-attached molecules in both wild-type and RNAi-treated embryos.

bioinformatics↗

bioRxiv2024

A Newborn F-box Gene Blocks Gene Flow by Selectively Degrading Phosphoglucomutase in Species Hybrids

The establishment of reproductive barriers such as postzygotic hybrid incompatibility (HI) remains the key to speciation. Gene duplication followed by differential functionalization has long been proposed as a major model underlying HI, but few supporting evidence exists. Here, we demonstrate that a new-born F-box gene, Cni-neib-1, of the nematode Caenorhabditis nigoni specifically inactivates an essential phosphoglucomutase encoded by Cbr-shls-1 in its sister species C. briggsae and their hybrids. Zygotic expression of Cni-neib-1 specifically depletes Cbr-SHLS-1, but not Cni-SHLS-1, in approximately 40 minutes starting from gastrulation, causing embryonic death. Cni-neib-1 is one of thirty-three paralogues emerging from a recent surge in F-box gene duplication events within C. nigoni, all of which are evolving under positive selection. Cni-neib-1 undergoes turnover even among C. nigoni populations. Differential expansion of F-box genes between the two species could reflect their distinctive innate immune responses. Collectively, we demonstrate how recent duplication of genes involved in protein degradation can cause unintended destruction of targets in hybrids that leads to HI, providing an invaluable insight into mechanisms of speciation. Significance statementUnderstanding how reproductive isolation forms and triggers speciation remains a central topic in evolutionary biology. In this study, the authors reveal a novel molecular mechanism that obstructs gene flow between the nematodes Caenorhabditis briggsae and C. nigoni. A recently evolved, species-specific F-box gene in C. nigoni selectively depletes an essential phosphoglucomutase enzyme in C. briggsae, leading to embryonic lethality in hybrids. This mechanism effectively prevents gene flow from C. nigoni to C. briggsae. This discovery underscores how a newborn gene, arising from recent gene duplication, can contribute to reproductive isolation by inadvertently disrupting a functionally important protein in hybrid offspring.

evolutionary biology↗

bioRxiv2024

Genomics-FM: Universal Foundation Model for Versatile andData-Efficient Functional Genomic Analysis

Artificial intelligence (AI) plays a crucial role in functional genomic analysis, offering great potential for comprehending biological phenomena such as heredity, development, diseases, and evolution. However, the development of AI models needs substantial labeled data, and these models are typically task-specific with limited generalizability to various applications. Here, we develop Genomics-FM, a genomic vocabulary driven foundation model that enables versatile and label-efficient functional genomic analysis. Specifically, Genomics-FM is first pretrained with ensemble genomic vocabulary on vast unlabelled data to learn comprehensive and generalizable representations and then finetuned with specific genomic vocabulary on limited labeled data to selectively activate and adapt the pretraining knowledge for specific tasks. We show that Genomics-FM significantly reduces the dependence on labeled data, and demonstrates the capability to outperform existing models across a comprehensive suite of tasks including genome annotation, epigenomic and expression profile prediction, and variant effect assessment. Remarkably, Genomics-FM even shows impressive zero-shot predictive capabilities across diverse species and tissues and exhibits noticeable adaptability to RNA-related tasks. With feasibility in data scarcity and even cross-domain biological scenarios, Genomics-FM will promote the broad application of AI and empower researchers to tackle previously insurmountable challenges, paving the way for groundbreaking research and discoveries.

bioinformatics↗

bioRxiv2024

BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (BEnchmArk for COmprehensive RNA Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https://github.com/terry-r123/RNABenchmark.

bioinformatics↗

bioRxiv2024

APOE3-R136S mutation confers resilience against tau pathology via cGAS-STING-IFN inhibition

The Christchurch mutation (R136S) on the APOE3 (E3S/S) gene is associated with attenuation of tau load and cognitive decline despite the presence of a causal PSEN1 mutation and high levels of amyloid beta pathology in the carrier1. However, the specific molecular mechanisms enabling the E3S/S mutation to mitigate tau-induced neurodegeneration remain unclear. Here, we replaced mouse ApoE with wild-type human E3 or E3S/S on a tauopathy background. The R136S mutation markedly decreased tau load and protected against tau-induced synaptic loss, myelin loss, and reduction in theta and gamma powers. Additionally, the R136S mutation reduced interferon response to tau pathology in both mouse and human microglia, suppressing cGAS-STING activation. Treating tauopathy mice carrying wild-type E3 with a cGAS inhibitor protected against tau-induced synaptic loss and induced similar transcriptomic alterations to those induced by the R136S mutation across brain cell types. Thus, suppression of microglial cGAS-STING-IFN pathway plays a central role in mediating the protective effects of R136S against tauopathy. One-sentence summaryThe R136S mutation on APOE3 enhances resistance to tau-related pathology and toxicity by downregulating the cGAS-STING-IFN signaling pathway.

neuroscience↗