Biology subjects

Xue, G.

Publications and source records attributed to Xue, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Novel Dissymmetric Ionizable Lipid-Assembled Lipid Nanoparticles for Delivery of Ferroptosis-Related siRNA in Diabetic Treatment

Small interfering RNA (siRNA) enables precise post-transcriptional gene silencing for refractory diseases, yet its clinical translation remains limited by the lack of safe and efficient delivery vectors. Inspired by the dissymmetric alkyl chain architecture of natural membrane phospholipids, we designed and synthesized 34 novel ionizable lipids with dissymmetric hydrophobic tails and formulated them into lipid nanoparticles (LNPs). Through systematic physicochemical and biological assessments, we established clear structure-activity relationships and identified two lead LNPs (O14-LNP, H18a-LNP) with superior endosomal escape capacity, enhanced in vivo gene silencing potency, and favorable biosafety relative to the clinical benchmark MC3-LNP. In both streptozotocin-induced and spontaneous db/db type 2 diabetes (T2D) mouse models, lead LNPs delivering ferroptosis-related siRNAs effectively ameliorated glucose and lipid metabolic disorders, restored islet function, and alleviated hepatic steatosis. This study not only lays a theoretical foundation for the rational design of novel ionizable lipids, but also validates the therapeutic potential of siRNA therapy targeting ferroptosis, providing a versatile delivery platform and targeted therapeutic strategy for the treatment of T2D.

pharmacology and toxicology↗

bioRxiv2026

Engagement-Dependent Neural Entrainment Underlies Dissociable tACS Effects on Item and Sequence Working Memory

A central challenge in neuromodulation research is to elucidate how exogenous electrical stimulation interacts with endogenous neural dynamics to shape cognitive function. Drawing on empirical evidence for dissociable oscillatory mechanisms underpinning item versus sequence working memory (WM), we employed transcranial alternating current stimulation (tACS) and magnetoencephalography (MEG) to establish a causal separation and characterize the underlying neurophysiological basis. We observed a clear behavioral and neural dissociation: prefrontal 6-Hz theta stimulation selectively enhanced item WM, whereas frontoparietal in-phase theta stimulation specifically improved sequence WM. MEG further revealed task-specific modulation effects: during sequence WM, where frontoparietal theta synchrony predicted memory success, in-phase theta stimulation selectively amplified inter-regional synchronization. In contrast, during item WM, where local theta power was indicative of memory success, the identical stimulation overall exerted no significant modulation effect. Critically, across both tasks and oscillatory measures (inter-regional synchronization and theta power), the magnitude of stimulation-induced modulation scaled with each region's or connection's contribution to memory performance, indicating that tACS proportionately amplifies actively engaged oscillatory processes. This engagement-dependent principle explains task- and region-specific neurophysiological effects and the variability in behavioral outcomes, providing a framework for designing precise, functionally aligned stimulation protocols to enhance translational efficacy.

neuroscience↗

bioRxiv2026

Alignment-free phylogenetic inference via hyperbolic protein language models

Conventional phylogenetic methods rely on multiple sequence alignments which are computationally intensive and often fail for highly divergent lineages. Here, we introduce LucaPhylo, an alignment-free framework that infers evolutionary relationships directly from unaligned sequences. Through a cascaded learning strategy LucaPhylo integrates protein language models with hyperbolic geometry, a representation space naturally suited to hierarchical branching, to capture deep evolutionary constraints without explicit homology matching. Using highly divergent RNA virosphere as a test case, LucaPhylo places unaligned sequences into phylogenetic trees with an accuracy comparable to leading alignment-based tree construction tools, while retaining divergent sequences that conventional pipelines frequently discard. It further enables the integration of divergent viral lineages into phylogenetic trees, thereby expanding the evolutionary landscape of RNA viruses. Together, LucaPhylo establishes an AI-driven, alignment-free paradigm for phylogenetic inference and provides a robust computational foundation for resolving deep evolutionary relationships among RNA viruses and other biological systems.

evolutionary biology↗

bioRxiv2026

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress has been observed in all-life and human-centric models, plant species, particularly for the staple crops, remains hindered by a lack of models. Here we introduce OneGenome-Rice (OGR), a rice (Oryza sativa) genomic foundation model pre-trained on a genomic dataset comprising 422 high-quality genomes of cultivated and wild rice. OGR is engineered upon a Mixture of Experts (MoE) transformer architecture with 1.25-billion parameters and supports an ultra-long context window of up to 1 million base (Mb) pairs at single-nucleotide resolution. A comprehensive benchmark demonstrated that OGR significantly outperforms existing state-of-the-art multi-plant or all-life genome models in 11 categories (e.g. motif identification, sweep detection, etc). We further demonstrated the utility of OGR in several downstream applications, such as indica-japonica subspecies introgression analysis, identification of agronomy trait-associated functional loci and prediction of gene expression from DNA sequences. These results establish OGR as a promising foundational computational infrastructure for rice functional genomics and precision breeding. The OGR and its fine-tuned models, including pretrained weights, training code and the rice genomic benchmark suit, have been fully opened.

genomics↗

bioRxiv2026

Reveal Principles of Codon Optimization via Machine Learning

High level of protein expression is usually welcomed in industry and research, and codon optimization is widely used to achieve high expression. Methods of implementing codon optimization can be divided into two branches, one is classical methods which develop cost functions based on empirical law, another is AI methods which learn the codon choice principles from endogenous genes with neural networks. Here we develop two codon optimization tools based on two branches respectively, namely OptimWiz 2.1 and OptimWiz 3.0. Results of fusion protein fluorescence detection indicate that both OptimWiz 2.1 and OptimWiz 3.0 are superior to all the other commercially available codon optimization tools. Principles of codon optimization are revealed in the process of machine learning on both tools.

bioinformatics↗

bioRxiv2026

Multiscale Mechanisms of Human Memory Modulation by Deep Brain Stimulation

The highly variable effects of deep brain stimulation (DBS) on cognition highlight a fundamental gap in our understanding of how external perturbations interface with the brains ongoing, task-engaged state. The current study addressed this challenge by combining intracranial EEG recordings with hippocampal DBS stimulation in human participants during spatial sequence memory task. We found 50 Hz stimulation targeting hippocampal white matter enhanced memory performance, whereas 5 Hz stimulation targeting hippocampal gray matter impaired memory. These opposing behavioral outcomes were both governed by two dissociated mechanisms: a region-specific modulation of theta rhythms in an engagement-dependent fashion, and a global modulation of the fidelity of memory representations across the cortex. Our results suggest targeted stimulation can concurrently fine-tune specialized circuits and govern the brains global state, which not only offers a novel roadmap for understanding how external inputs shape human cognition, but also helps to design precise, state-dependent neuromodulation therapies.

neuroscience↗

bioRxiv2026

Ramping-up hippocampal ripples and their neocortical coupling support human visual short-term memory

Emerging evidence suggests that the hippocampus contributes to visual short-term memory (VSTM). However, the neural mechanisms through which hippocampal activity supports the maintenance of VSTM representations remain largely unknown. Here, using intracranial EEG recordings from human participants performing a delayed match-to-sample task for naturalistic objects, we investigated the role of hippocampal ripple activity--brief high-frequency oscillations associated with memory replay--in VSTM maintenance and hippocampal-neocortical communication. We found that hippocampal ripple rates progressively ramped up during the maintenance period and supported successful VSTM. More critically, hippocampal ripples were temporally coupled with the ripples in the lateral temporal lobe (LTL), and these coupled ripples were associated with memory reactivation in the LTL. These findings provide direct evidence that hippocampal-neocortical interaction via coupled ripples supports VSTM, extending the role of hippocampal ripples from long-term consolidation to short-term mnemonic processes and offering new insights into the dynamic coding mechanisms of working memory. Impact StatementHippocampal ripple ramping-up and neocortical coupling support a dynamic coding mechanism for visual short-term memory maintenance

neuroscience↗

bioRxiv2025

Graph-based tracing of dynamically functioning gene circuits in cell fate decisions with isoform resolution

Transcription factor (TF) regulatory networks govern complex cellular processes, yet their inference from single-cell transcriptomes is hindered by TFs low abundance, tissue-specific functions, and isoform diversity. We evaluated the utility of transcription factor activity (TFA) to infer TF-TF interactions, demonstrating its superiority over expression-based methods in capturing tissue-specific, high-fidelity regulatory relationships. Using simulated and real-world transcriptomes across human hematopoiesis and early embryonic development, we validated TFAs ability to render regulatory relationships. Through utilizing the concept of propagated regulome, we uncovered TFAs utility in retrospectively resolving dynamic regulatory circuits. To address regulome limitations, we developed an embryonic stem cell-specific Consensus REgulome Database (CRED) with isoform resolution, revealing functional heterogeneity among TF isoforms. Applied to human preimplantation single-cell datasets, CRED outperformed existing regulomes, identifying isoform-specific regulators and tracing regulatory modules despite severe dropouts. Our work unleashes TFA as a robust tool for reconstructing TF networks and highlights the importance of transited, isoform-level, cell-type-specific analysis in unraveling transcriptional regulatory circuits. HighlightsO_LITFA (Transcription Factor Activity) renders regulatory relationships with high fidelity compared to expression. C_LIO_LIUsing transited regulons enables TFA to retrospectively resolve dynamic regulatory interactions in cell fate decisions. C_LIO_LIAn integrated ESC-specific transited regulome database reveals isoform-level heterogeneity. C_LIO_LITFA successfully identifies key TF regulatory modules during human preimplantation from datasets with severe dropout. C_LI

systems biology↗

bioRxiv2025

Hierarchical coding of local and global positions in episodic memory for naturalistic sequences

Life unfolds continuously across multiple timescales, yet how the brain encodes hierarchical event sequences remains poorly understood. Using magnetoencephalography, we investigated the neural coding of hierarchical positions in naturalistic sequence memory. Participants viewed video sequences with a nested structure and later recalled their serial order. Behavioral analyses revealed transposition errors for items occupying identical global or local position. Multivariate classification and subspace analyses revealed separable neural representations of global and local positions. Local positions were better decoded in high-dimensional space, whereas global positions exhibited greater pattern separability in low-dimensional space. Brain regions encoding global positions exhibited longer neural timescales and reduced sensitivity to low-level event transitions compared to regions encoding local positions. These findings support a parallel hierarchical organization of content and context, a novel framework that enables integration of item and order across hierarchical levels and provides principles for hierarchical representations in both human memory and artificial intelligence. HighlightsO_LIHierarchical position coding in sequence memory for naturalistic stimuli C_LIO_LIThe neural representations of global and local positions are separable C_LIO_LIGlobal and local position codes are low and high dimensional, respectively C_LIO_LIGlobal position regions have longer timescales than local position regions C_LI

neuroscience↗

bioRxiv2025

Using Artificial Intelligence to Assess Cross-Species Transmission Potential of Influenza A Virus

Influenza A viruses (IAVs) pose pandemic threats through cross-species transmission, yet predicting their adaptive evolution remains challenging. We introduced Influenza A virus Adaptability to host X (FluAdaX), a deep learning framework that integrates a moving average-equipped gated attention mechanism using full-genome sequences. FluAdaX demonstrated robust host classification performance across endemic IAV strains, and outperformed traditional models in detecting avian-to-human transmission. Spillover score and adaptability score were introduced to evaluate host shift risk, which prioritized variants with elevated human adaptation potential, such as H7N9, H9N2 avian IAVs, and H1N1 swine IAVs. Besides HA and NA genes, PB2 and NS genes were found critical for zoonosis. Potential molecular markers associated with avian/human tropism were identified across PB2 and NS genes using XGBoost. FluAdaX provided a dynamic framework to decode IAV host adaptation, enabling real-time risk assessment of cross-species transmission of emerging IAV variants.

bioinformatics↗

bioRxiv2025

EVODEX: A Mechanistic Framework for Extracting, Structuring, and Predicting Enzymatic Reactivity

Accurately modeling enzymatic reactivity is essential for synthesis planning, mass spectrometry interpretation, and biochemical knowledge extraction, but existing reaction datasets are often incomplete or inconsistently annotated. EVODEX introduces two abstractions to address this: partial reactions, which isolate atom-mapped substrate-product transformations from complex biochemical reactions, and Electronic Reaction Operators (EROs), which encode the bonding and orbital environment of atoms undergoing change. From 349,458 curated reactions in EnzymeMap, EVODEX constructs over 186,000 partial reactions and mines a core set of 1,404 EROs, each supported by at least 10 examples. Together, these explain 91 percent of test reactions at the formula level and 62 percent at the mechanistic level. A further filtered synthesis subset of 436 one-to-one operators that exclude ubiquitous metabolites provides a compact and generalizable vocabulary of single-step enzymatic transformations. EVODEX operators are auditable, reusable, and available as part of an open-source Python package with datasets, Jupyter notebooks, and a browsable website.

bioinformatics↗

bioRxiv2025

Molecular mechanisms of recruitment, function and regulation of UPF1 in histone mRNA decay

Animal replication-dependent histone mRNAs end in a conserved stem loop (SL) instead of the canonical poly(A) tail present in all other eukaryotic mRNAs. Degradation of the histone SL at the end of the S-phase is initiated by the stem-loop binding protein SLBP and its interplay with the RNA helicase UPF1 and the exoribonuclease 3hExo. We report direct interactions between SLBP and UPF1 and show that the unstructured SLBP N-terminus wraps around the UPF1 helicase core, contacting it at multiple sites. Although binding of SLBP to UPF1 impedes unwinding activity, it is critical for efficient histone mRNA decay in cells, as unwinding of the SL facilitates degradation by 3hExo. Here we show that the UPF1-activator, UPF2, binds 3hExo, and that UPF2-mediated activation of UPF1 overrides the inhibitory effect of SLBP. Our results highlight the intricate network of UPF1-centric protein-protein and protein/RNA interactions that fine-tunes its unwinding activity and orchestrates timely and efficient degradation of histone mRNA.

biochemistry↗

bioRxiv2024

Slow-wave sleep and REM sleep differentially contribute to memory representational transformation

Memory transforms over time, gradually becoming less idiosyncratic and more gist-like. While sleep contributes to memory transformation, how different sleep stages and EEG activity influence memory transformation is far from clear. Applying representational similarity analysis to electroencephalogram (EEG) recordings, we examined memory representational transformation at both the idiosyncratic "item-level" and the generic "category-level". Our findings revealed that after an overnight sleep, item-level neural representations for post-sleep remembered items were abolished. In contrast, category-level representations remained prominent, but they became distinctive from pre-sleep. Across participants, more rapid eye movement (REM) sleep relative to slow-wave sleep (SWS) was associated with reduced item-level neural representational strength, increased category-level representational strength, as well as the decreased item-level representational similarity between pre-sleep learning and post-sleep retrieval sessions. Moreover, the theta and beta EEG power during REM sleep, and delta power during SWS differentially supported these representational transformations. These findings suggest that post-learning REM sleep and SWS play differential roles in supporting overnight memory transformation.

neuroscience↗

bioRxiv2024

Functional implications of the interaction of the SARS-CoV-2 Nucleocapsid protein with factors involved in nonsense-mediated mRNA decay

The RNA genome of the SARS-CoV-2 virus encodes for four structural proteins, 16 non- structural proteins and nine putative accessory factors. A high throughput analysis of interactions between human and SARS-CoV-2 proteins identified multiple interactions of the structural Nucleocapsid (N) protein with RNA processing factors. The N-protein, which is responsible for packaging of the viral genomic RNA was found to interact with two RNA helicases, UPF1 and MOV10 that are involved in nonsense-mediated mRNA decay (NMD). Using a combination of biochemical and biophysical methods, we investigated the interaction of the SARS-CoV-2 N-protein with NMD factors at a molecular level. Our studies led us to identify the core NMD factor, UPF2, as an interactor of N. The viral N-protein engages UPF2 in multipartite interactions and can negate the stimulatory effect of UPF2 on UPF1 catalytic activity. N also inhibits UPF1 ATPase and unwinding activities by competing in binding to the RNA substrate. We further investigate the functional implications of inhibition of UPF1 catalytic activity by N in mammalian cells. The interplay of SARS-CoV-2 N with human UPF1 and UPF2 does not affect decay of host cell NMD targets but might play a role in stabilizing the viral RNA genome.

biochemistry↗

bioRxiv2024

Transcranial Direct Current Stimulation over the Posterior Parietal Cortex Increases Non-target Retrieval during Visual Working Memory

Accurate visual working memory (VWM) requires precise content-context binding. Previous studies have revealed a close relationship between the posterior parietal cortex (PPC) and feature binding during VWM, this study further examined their causal relationship through three transcranial direct current stimulation (tDCS) experiments. In Experiment 1 (N = 57), participants underwent three sessions of tDCS separately, including PPC stimulation, occipital cortex stimulation, and sham stimulation, and completed a series of delayed estimation tasks for orientations before and after stimulation. Results showed that tDCS over PPC selectively prolonged recall response time (RT) and increased the probability of non-target responses (a.k.a. failure of feature binding). In Experiment 2 (N = 29), combining metacognition estimation during the task, we further investigated whether the effects of PPC stimulation on RT and increased probability of non-target responses were attributed to more mis-binding (i.e., participants self-reported "remembered" in non-target responses) or informed guessing (participants self-reported "forgotten" in non-target responses). We replicated the main findings in Experiment 1, and we also observed greater tDCS effects of PPC on RT in informed guessing trials than mis-binding trials while comparable effects on non-target response rates in these two types of trials. In Experiment 3 (N = 28), we then examined whether the effects of tDCS over PPC specifically influenced the memory retrieval process by using a change detection task. We found that PPC stimulation did not influence the recognition RT or accuracy. Together, this study provides causal evidence supporting the involvement of PPC in feature binding during VWM retrieval. Significance StatementVisual working memory (VWM) enables humans to temporarily store and process visual information, which requires accurate binding of items to their unique context. Accumulating studies posited that the posterior parietal cortex (PPC) is closely related to this binding process, the current study further examined their causal relationship. Through three strictly within-subject well-designed non-invasive neural stimulation experiments, we found that PPC stimulation selectively increased response time (RT) and binding error during VWM. Moreover, we found these changes were modulated by individual metacognition and only occurred during memory recall instead of recognition. Together, our results provided strong evidence that PPC is causally involved in the binding process during visual working memory retrieval.

neuroscience↗

bioRxiv2024

A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems

In embryonic development and organogenesis, cells sharing identical genetic codes acquire diverse gene expression states in a highly reproducible spatial distribution, crucial for multicellular formation and quantifiable through positional information. To understand the spontaneous growth of complexity, we constructed a one-dimensional division-decision model, simulating the growth of cells with identical genetic networks from a single cell. Our findings highlight the pivotal role of cell division in providing positional cues, escorting the system toward states rich in information. Moreover, we pinpointed lateral inhibition as a critical mechanism translating spatial contacts into gene expression. Our model demonstrates that the spatial arrangement resulting from cell division, combined with cell lineages, imparts positional information, specifying multiple cell states with increased complexity--illustrated through examples in C.elegans. This study constitutes a foundational step in comprehending developmental intricacies, paving the way for future quantitative formulations to construct synthetic multicellular patterns.

systems biology↗

bioRxiv2024

Selective attention modulates feature accumulation speed during perception and memory retrieval

Mounting evidence indicates that information processing in the visual hierarchy follows a sequential progression from low-level perceptual to high-level conceptual features during visual perception, with the reverse order during memory retrieval. However, the nature of this hierarchical processing and its modulation by selective attention remains unclear. By integrating a deep neural network with the drift-diffusion model on reaction time data, we identified parallel processing of perceptual and conceptual features during perception, rather than sequential processing. The slower reaction times observed in conceptual tasks compared to perceptual tasks were primarily driven by the larger decision boundary and longer non-decision time associated with conceptual features, which could not be compensated by their faster evidence accumulation rates. Using single-trial multivariate decoding of MEG (magnetoencephalography) data to examine the timing of neural representations of different features, we found that selective attention reversed the onset times of perceptual and conceptual features in the occipital and parietal lobes during perception. This led to earlier detection of conceptual features compared to perceptual features in the animacy and size tasks. During retrieval, the colour task exhibited earlier peak times for perceptual features compared to conceptual features in the frontal lobe, indicating that perceptual features were reconstructed with the highest fidelity before conceptual features. These findings provide novel insights into the hierarchical nature of information processing during perception and retrieval, emphasizing the crucial role of selective attention in modulating the speed of feature information accumulation.

neuroscience↗

bioRxiv2023

A Logic-incorporated Gene Regulatory Network Deciphers Principles in Cell Fate Decisions

Organisms utilize gene regulatory networks (GRNs) to make fate decisions, but the regulatory mechanisms of transcription factors (TFs) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision- making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top- down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

systems biology↗