Biology subjects

Yao, Z.

Publications and source records attributed to Yao, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Sleep reactivation shapes memories of event and its temporal structure

Episodic memory provides a temporal scaffold that organizes past experiences according to their sequential occurrence. While memories are reactivated and consolidated during sleep, it remains unknown how exogenous reactivation reshapes memories of complex events, particularly the temporal structure that binds individual elements across time. In this preregistered study, 35 participants encoded neutral and aversive narratives composed of image sequences, with auditory context shifts defining event boundaries. During post-encoding slow-wave sleep, closed-loop targeted memory reactivation (TMR) delivered boundary-associated auditory cues time-locked to the up-state of slow oscillations. Reactivation led to faster, more confident, and more liberal recognition responses for cued than uncued memories. However, reactivation exerted a time-dependent effect on temporal-order memory, impairing it immediately after sleep but attenuating forgetting one week later. This behavioral dissociation was paralleled at the neural level: stronger cue-elicited theta activity predicted superior recognition of neutral, but not aversive event content, whereas greater theta responses were associated with poorer temporal-order memory for the same events. Together, these findings suggest that sleep reactivation does not uniformly strengthen episodic memories. Rather, it selectively prioritizes access to event content while reorganizing temporal relations among event elements, revealing distinct, time-dependent consequences of sleep reactivation for different components of complex experiences.

neuroscience↗

bioRxiv2026

Claudin-dependent clonal expansion couples epicardial activation to regenerative fate acquisition

Epicardial activation is a conserved response to injury across vertebrates, yet only in regenerative species do epicardium-derived cells (EPDCs) acquire the capacity to invade the injury site and contribute to cardiac regeneration. How regenerative competence is established in epicardial cells downstream of activation remains poorly understood. Using multicolour lineage tracing we find that epicardial cells of the salamander species Pleurodeles waltl undergo a transient phase of clonal expansion that precedes EPDC accumulation at the injury site. Depletion of cCpE-sensitive Claudins from otherwise intact tight junctions alters the organisation and the mechanical state of the epicardium, impairs clonal expansion and reduces the capacity of epicardial cells to delaminate. Consequently, EPDCs fail to activate a regenerative transcriptional programme and complete their transition into injury-induced migratory EPDCs that are required to sculpt a Tenascin-C-rich matrix supporting cardiomyocyte protrusion at the injury site. Together, our findings demonstrate that epicardial activation alone is insufficient for regeneration and identify Claudin-dependent clonal expansion as a mechanism that couples epicardial activation to the acquisition of a regenerative EPDC state.

cell biology↗

bioRxiv2026

The cerebellum exploits a thalamic carrier frequency to refine motor behaviors

Despite enabling nearly all cerebellar-cortical communication, how the thalamus processes and interacts with cerebellar activity remains unclear. We found a pronounced 1-5 Hz (delta) rhythm in the thalamic membrane potential and cerebellothalamocortical local field potentials during nonmoving periods in behaving mice. We show, using photometry, multielectrode recordings, closed-loop stimulation, and dynamic clamp, that CaV3.1-dependent resonance of thalamic neurons facilitates transmission of delta-rhythm cerebellar activity to the cortex. Knockout of CaV3.1 in cerebellar-thalamic neurons diminishes the thalamocortical delta oscillation and specifically impairs motor learning while minimally affecting motor performance. These results ground population oscillations to features of ion channels, highlight the cerebellum and delta's utility in offline motor learning, and support a carrier frequency-like encoding mechanism that underlies cerebellothalamocortical communication.

neuroscience↗

bioRxiv2026

Automatic bioinformatic software named entity recognition from literature

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

bioinformatics↗

bioRxiv2026

PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics enables robust generative modeling of gene expression and scales single-cell integration to 100 million cells

Single-cell RNA technologies enable the routine acquisition of transcriptomic atlases. However, these molecular profiles are influenced by overlapping sources of variation. Since these covariates confound comparisons, data integration is the first step in most analyses. Three challenges remain: correcting strong batch effects, scaling to millions of cells, and modeling how covariates influence gene expression. To address these challenges, we developed PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics, a deep learning framework whose central feature is a generative model of gene expression data. Additionally, PIANO achieves robust integrations and trains 10x faster than previous methods. PIANO accurately integrates single-cell data across species and across single-cell and spatial transcriptomics modalities. As practical applications, PIANO models spatially-resolved gene expression during Alzheimer's disease progression in human brains and integrates over 100 million cancer cells to model drug perturbations. In summary, PIANO's integration and generative modeling capabilities will empower novel insights for countless future studies.

bioinformatics↗

bioRxiv2026

Dissociable reactivation during NREM and REM sleep supports memory consolidation and emotional dissipation

Sleep is critical for memory consolidation and emotional regulation, yet the respective roles of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep remain unclear. Here, using a within-subject crossover design, we recorded high-density electroencephalography (EEG) across two experimental nights while participants viewed neutral or aversive film clips in a counterbalanced order. Combining with daytime functional localizers establishing neural patterns of aversive vs. neutral emotional processing, multivariate pattern analysis revealed that the reactivation of aversive vs. neutral memory during nocturnal sleep was both stage-dependent and event-specific. In NREM sleep, valence-specific reactivation was time-locked to slow oscillation (SO)-spindle complexes but not to either event alone; in REM sleep, reactivation occurred selectively during phasic REM periods marked by rapid eye movements. Critically, NREM SO-spindle coupling percentage was associated with consolidation of temporal memories; whereas phasic REM reactivation strength was linked to overnight dissipation of negative affect. Our findings provide direct evidence that sleep reprocesses emotional experiences through dissociable stage- and event-specific mechanisms, laying out a framework for future targeted sleep-based interventions.

neuroscience↗

bioRxiv2026

Sleep reactivation of positive self-evaluation reveals neural-evaluative alignment that varies with depressive symptoms

Positive self-evaluation underpins resilience and mental health, yet how to enhance positive self-evaluation and the underlying neurocognitive mechanisms remain poorly understood. Existing data mostly examined self-evaluative processing of the wakeful minds, with the role of sleep remain unexplored. Here, we demonstrated that reactivation of positive self-evaluation during sleep revealed fine-grained neural-evaluative alignment that varies with depressive symptoms. We found that targeted reactivation of positive personality traits during non-rapid eye movement (NREM) sleep enhanced positive self-evaluation, but this benefit diminished with elevated depressive symptoms. Moreover, cueing positive traits during NREM sleep elicited trait-specific, 11-16 Hz EEG sigma power-based neural representations that aligned with post-sleep positive self-evaluative representational structures, as revealed by representational similarity analyses. Notably, individuals with elevated depressive symptoms exhibited weaker neural-evaluative representational alignments, identifying a maladaptive self-evaluative processing that bridges across sleep and wakefulness states. These findings provide mechanistic insights on how fine-grained neural-evaluative alignment can substantiate positive self-evaluation, and why it was disrupted as depressive symptoms increased. HighlightsO_LIPositive-trait reactivation during NREM sleep enhances positive self-evaluation C_LIO_LIThis behavioral benefit decreases as depressive symptoms increase C_LIO_LISleep cueing evokes trait-specific sigma-band neural representations C_LIO_LINeural-evaluative alignment weakens as depressive symptoms increase C_LI

neuroscience↗

bioRxiv2026

Conserved Cell Type Signatures Across the Brainstem and Spinal Cord in the Mouse Central Nervous System

Understanding how cell types are organized across the central nervous system (CNS) is key to uncovering neural function. Here, we integrate single-nucleus Multiome (RNA+ATAC) sequencing, spatial transcriptomics, and computational analyses to map conserved cell type signatures in the adult mouse brainstem and spinal cord. We identify a shared core of neuronal and non-neuronal cell types, alongside region-specific specializations reflecting distinct functions. Spatial data reveal conserved cellular niches across the brainstem-spinal cord boundary, indicating a continuous organizational logic. Cross-region comparisons uncover recurrent gene expression modules and signaling programs that may support shared circuit features. Chromatin accessibility profiling highlights cell-type-specific regulatory programs and implicates Hox transcription factors in positional identity. Notably, cell-type and positional identities are largely orthogonal, with varying regional influence across neuronal classes: motor neurons show strong positional coupling, whereas glutamatergic and GABAergic interneurons show minimal entrainment. This work provides a reference for the shared molecular architecture of these CNS regions.

neuroscience↗

bioRxiv2026

Developing engineering strategies to enhance the genetic stability of fatty alcohol-producing strains for production scale-up

Robust strain stability is essential for industrial-scale fatty alcohol production, as metabolic burden and toxicity not only constrain productivity but also create selective pressure for low-producing or non-producing subpopulations. This genetic instability leads to genetic heterogeneity and compromises strain performance during large-scale fermentation. In this study, we investigated the production stability of fatty alcohol-producing Yarrowia lipolytica strains and developed systematic strategies to improve the genetic stability of engineered strains for scale-up production. In a mock fermentation scale-up, a fatty acyl-CoA reductase (FAR)-expressing strain lost fatty alcohol production after five consecutive passages. To address this, we fused FAR to phosphoglycerate kinase I (PGK1), a gene essential to cell growth, to promote the stability of FAR expression and prevent production loss. This strategy extended fatty alcohol production by one passage. Additionally, FAR was fused to GFP and extended production stability by four additional passages. In parallel, competitive co-culture experiments, in which producing strains were cultured alongside non-producing mutants, revealed that when non-producers emerged with a frequency of 10-5, they dominated the fermentation population within six passages; at 10%, they took only two passages. Furthermore, deep sequencing of strains that demonstrate different levels of stability and fatty alcohol productivity identified mutation patterns that contribute to strain instability. These findings provide insights into engineering Y. lipolytica with enhanced genetic stability for scale-up fatty alcohol production.

synthetic biology↗

bioRxiv2026

Targeted extracellular degradation of LRP8 promotes ferroptosis in cancer cells

Tumor reliance on antioxidant defenses creates a vulnerability to ferroptosis, yet strategies to therapeutically disable these systems remain limited. Here, we identify targeted degradation of the selenium uptake receptor LRP8 as an effective approach to decrease the abundance of the ferroptosis-protective enzyme glutathione peroxidase 4 (GPX4). Using bispecific cytokine receptor-targeting chimeras (KineTACs) that couple LRP8 to cytokine receptor internalization pathways, we selectively direct LRP8 to the lysosome for degradation. LRP8 degradation reduces the abundance of several selenoproteins, including GPX4, lowering the cellular threshold for lipid peroxidation and sensitizing cancer cells to ferroptosis. These findings establish receptor-mediated selenium uptake as a critical, targetable node in ferroptosis resistance and demonstrate that extracellular protein degradation can be leveraged to reprogram intracellular translational dependencies in cancer cells. More broadly, this work provides a framework for exploiting nutrient acquisition pathways to overcome therapy resistance.

bioengineering↗

bioRxiv2026

Mitochondrial redox potential couples to single-cell mass in a conserved sublinear scaling law, modulated by mitochondrial mass-control genes

Using quantitative phase microscopy and genetically-encoded metabolism probes, we simultaneously measured single-cell dry mass and mitochondrial redox efficiency across eukaryotic cell types. We discovered that single-cell mass scales with mitochondrial redox efficiency, according to a sublinear power law, across budding yeast, mouse, and human cells. Genetic and pharmacological perturbations of mitochondrial redox balance shifted the coefficient k and exponent y predictably, while preserving the scaling law, indicating a causative coupling between biomass accrual and mitochondrial redox efficiency. We also discovered that both single-cell mass and mitochondrial redox efficiency manifest log-normal distributions, providing more support for previous theories that biomass accrual is an exponential process, meaning the rate of mass increase depends on the current mass. Finally, our yeast knockout screen identified 81 mass control genes (MCGs) that disrupted the log-normality of single-cell mass. Interestingly, MCGs were enriched for mitochondrial metabolism and cristae maintenance, whereas <10% were related to the cell cycle. Conserved MCGs, including human TRIAP1, ATPAF1, and ACO1, also maintained human single-cell mass log-normality and sublinear scaling with mitochondrial redox efficiency. These findings reveal a shared bioenergetic principle at the single-cell level, and new techniques to study it. This scaling law provides a quantitative framework for understanding cell mass control, metabolic coupling, and disease-associated growth regulation.

cell biology↗

bioRxiv2026

Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq

Functional genomics studies have provided critical insights into cell type-specific gene regulatory programs, but to date most have been conducted in wild-type tissues or cell cultures. Here, we present a gene expression functional atlas across the mouse brain. We use an enhanced in vivo Perturb-seq platform to analyze transcriptome-wide responses to loss of 1,947 disease-associated genes, profiling over 7.7 million cells spanning major brain regions and neuronal populations. We find striking cell-type-specific essentiality and transcriptional programs and show that closely related disease genes such as two NMDA receptor subunits can drive opposing transcriptional programs. Together, this work reveals insights into the genetics and mechanisms of neurodevelopmental, psychiatric, and neurodegenerative diseases in vivo, paving the way for the design of future genetic medicine.

genomics↗

bioRxiv2026

LysinFusion: Integrating Multi-Feature Encoding and Hybrid CNN-Transformer Architecture for Phage Lysin Prediction

The escalating antimicrobial resistance crisis has intensified the urgent need for alternative antibacterial agents. Phage lysins exhibit potent bactericidal activity with low resistance potential, yet their large-scale discovery from rapidly expanding genomic resources remains limited, as existing computational methods often rely on sequence homology and few reproducible, independently validated, and practically deployable prediction frameworks are currently available. We present LysinFusion, a reproducible deep learning framework that integrates heterogeneous sequence features via a hybrid CNN-Transformer architecture for accurate lysin identification. Trained on a curated, de-redundant corpus (PHROG + inphared) and validated on 148 experimentally confirmed UniProt proteins, LysinFusion achieves an accuracy of 0.81, an AUC of 0.89, and an MCC of 0.62, outperforming the state-of-the-art DeepMineLys by up to 52.3% across metrics while reducing false positives by 64% (12 vs. 33). Ablation studies confirm that both CNN and Transformer modules are individually essential and that serial processing outperforms parallel alternatives. Model interpretability via occlusion and LIME aligns with established lysin biology, with attention concentrating on the N-terminal catalytic domain and decision boundaries reflecting characteristic charge-composition thresholds documented in Gram-negative lysin C-terminal regions. The framework, including all source code, curated datasets, and an accompanying web server, is freely available at https://github.com/sinuo560/LysinFusion.

bioinformatics↗

bioRxiv2026

MapMyCells: High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies

Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells, an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimers Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimers disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.

bioinformatics↗

bioRxiv2026

Neurocognitive deficits in controlling aversive memory among insomnia disorders

BackgroundInsomnia disorder is a common sleep disturbance characterized by adverse daytime cognitive and emotional impairments, such as repetitive negative thinking and increased psychological distress. Memory control, a key self-regulatory ability to control or inhibit unwanted thoughts and memories, plays an essential role in supporting cognitive functions and emotional well-being. Here, we delineate the neurocognitive mechanisms underlying memory control among individuals with insomnia. Methods41 participants meeting DSM-5 criteria for insomnia disorder and 40 healthy sleepers completed an emotional Think/No-Think task, during which participants either retrieved (Think) or suppressed the retrieval (No-Think) of aversive memories in response to memory cues while electroencephalograms were recorded. ResultsLinear mixed model analyses with age and depression scores as covariates showed that participants with insomnia exhibited impaired memory control abilities, as evidenced by reduced suppression-induced forgetting in memory recall when compared to healthy sleepers. Electrophysiologically, healthy sleepers showed enhanced right prefrontal theta power in retrieval suppression than in retrieval, indicating elevated needs of inhibitory control during memory control. In sharp contrast, this difference was absent among those with insomnia. Notably, the greater the severity of insomnia symptoms, the smaller the retrieval vs. retrieval suppression theta power differences across participants, linking inefficient top-down control of unwanted memories with low sleep qualities. ConclusionIndividuals with insomnia showed impaired memory control of aversive memories and aberrant electrophysiological activities during retrieval suppression. Future research shall investigate the causal relationship between memory control abilities and insomnia symptoms.

neuroscience↗

bioRxiv2026

Targeted shedding of extracellular membrane proteins by induced protease recruitment

Extracellular targeted protein degradation has emerged as a promising therapeutic modality to eliminate proteins of interest (POIs) at the cell surface, by using bifunctional molecules to recruit natural recycling receptors or membrane-bound E3 ligases that redirect POIs to the lysosome. Another natural mechanism involves extracellular proteases that cleave and shed extracellular domains. Here, we exploit this endogenous mechanism by engineering bispecific antibody Shedders, that recruit a classic sheddase ADAM10 to POIs, inducing selective ectodomain shedding. We first targeted the immune checkpoint receptor LAG-3 and observed robust depletion of surface LAG-3 accompanied by accumulation of soluble LAG-3 fragments in both engineered cell lines and primary human T cells. Using biochemical and imaging assays, we confirmed that this antibody-induced shedding is restricted to extracellular protease activity and occurs independently of lysosomal trafficking. Notably, induced shedding of LAG-3 on activated primary T cells partially alleviated inhibitory signaling and reinvigorated IFN{gamma} secretion. We extended the scope of induced shedding by developing Shedders that recognize synthetic epitope-tags that enabling rapid assessment of substrate compatibility across diverse targets. Using this platform, we identified multiple immune modulatory cell-surface receptors, including IL6R, CD62L and MIC-A that can be targeted for shedding. In summary, this work establishes a new paradigm for targeted extracellular proteolysis and expands the toolkit for studying extracellular proteolysis with potential therapeutic benefit.

bioengineering↗

bioRxiv2026

Enhancing ML-based binder design with high-throughput screening: a comparison of mRNA and yeast display technologies

Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein binders with minimal manual input. While computational design capacity has scaled rapidly, experimental validation methods have lagged, creating a bottleneck in binder discovery pipelines. Here, we apply mRNA display to screen an ML-designed miniprotein binder library and directly compare its performance with the more widely used yeast surface display platform using a single shared DNA library. We screened 2,009 designs targeting the platelet receptor TLT-1 and 3,159 designs targeting the immune receptor B7-H3 across both platforms. While both selection methods reliably identified functional binders, we found that mRNA display preferentially enriched binders with slower dissociation rates. In addition, mRNA display achieved higher library coverage than yeast display, likely rescuing functional designs that are penalized in a cell-based expression system. Biophysical characterization of selected binders from both platforms revealed strong binding affinities and high thermal stabilities. These results showcase the power of integrating ML-based computational design tools with rapid in vitro selection technologies, providing a scalable framework for therapeutic miniprotein discovery. IMPORTANCEMiniprotein binders offer major advantages as next-generation therapeutics, including small size, high stability, and efficient production. In this work, we conduct a side-by-side comparison of mRNA and yeast display as platforms for high-throughput evaluation of de novo miniprotein binders. The binders generated here serve as starting points for therapeutics targeting TLT-1 or B7-H3, two clinically relevant molecules.

bioengineering↗

bioRxiv2025

PARP10 condensation inhibits viral infection via targeting NAD+ homeostasis

ADP-ribosylation is a critical post-translational modification mediated by diphtheria toxin-like ADP-ribosyltransferases (ARTDs). The human ARTD family comprises 17 members that catalyze either poly- or mono-ADP-ribosylation. However, the functions and regulatory mechanisms of many ARTD proteins remain poorly understood. Here, we uncover an antiviral role for PARP10 through its ability to form biomolecular condensates. Viral infection triggers PARP10 condensation, a process driven by multivalent homotypic interactions among the structured domains. We show that mono-ADP-ribosylation of PARP10 suppresses its condensation, serving as a negative feedback mechanism that regulates condensate dynamics and protein stability. PARP10 condensates exhibit enhanced enzymatic activity, leading to decreased NAD+ levels and disruption of NAD+ homeostasis, ultimately inhibiting viral replication. Our findings establish PARP10 condensation as a novel mechanism for ARTD enzyme compartmentalization, with significant implications for innate immunity and host defense.

cell biology↗