Biology subjects

Liu, B.

Publications and source records attributed to Liu, B..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Mosaic terrestrial diversity dynamics through the Permo-Triassic interval

The end-Permian mass extinction (EPME) represents the most severe biotic crisis of the Phanerozoic Eon on Earth and has been well documented in marine taxa. However, its impact on terrestrial organisms and ecosystems remains incompletely understood. Here we present a high-resolution reconstruction of terrestrial diversification dynamics and spatial reorganization across the Permo-Triassic boundary (PTB) using comprehensive occurrence data of macroplants, sporomorphs and vertebrates. Terrestrial responses to the EPME show highly temporal, regional and taxonomic heterogeneities. Plants experienced a genus-level diversity loss of [~] 6.7%, across the PTB, whilst vertebrates, a lagged decline from the late Permian, peaking at a diversity loss of [~] 66.7%. Global distributions of plant and vertebrate show converging on similar latitudinal gradients post the PTB. Plant diversity loss is disproportionately high in low-latitude and tropical regions and progressively lower toward mid- and high-latitudes. Our study facilitates a fine-grained understanding to terrestrial macroevolution in geologic history through multi-analysis of a large volume of fossil data. Our findings challenge the long-held notion of global terrestrial collapse and mass extinction in plants during the PTB and offer a deep-time analogue for uneven response of extant terrestrial biodiversity to ongoing climate change.

paleontology↗

bioRxiv2026

STAnalyzer: Transparent Spatial Transcriptomics Analysis via an Agentic Architecture

Spatial transcriptomics enables high-resolution profiling of gene expression within spatial contexts, yet its potential is often hindered by fragmented toolchains, intricate parameters, and cognitive bottlenecks of interpreting high-dimensional data. While recent Large Language Model agents have attempted to automate this process, they remain constrained by rigid execution logic, lack multimodal feedback for self-correction, and operate in epistemic isolation from established biological knowledge. Here, we present STAnalyzer, an intelligent multi-agent framework designed to automate the end-to-end analytical lifecycle--from raw data processing to biological hypothesis generation. Transcending traditional pipelines, STAnalyzer employs a collaborative intelligence architecture to achieve three core capabilities: (1) Intent-Driven Orchestration, which dynamically translates natural language queries into rigorous bioinformatics workflows; (2) Multi-Modal Self-Refinement, which autonomously ensures analytical robustness through closed-loop synthesis of evidence from visual patterns and statistical metrics; and (3) Evidence-based Cross-Validation, which bridges the gap between data-driven correlations and biological causation by anchoring findings in ground-truth literature and structured databases. By eliminating manual analytical bottlenecks and ensuring rigorous evidentiary traceability and transparency, STAnalyzer makes high-resolution spatial omics more accessible to a broader research community. It provides a robust and scalable framework for cross-platform automated analysis and accelerated biological discovery, translating massive spatial datasets into verifiable biological insights.

bioinformatics↗

bioRxiv2026

Nucleoid-associated proteins sense phage-induced genome damage to elicit abortive infection

Bacteria have evolved diverse immune strategies to detect and neutralize bacteriophage infection. Here, we describe an unprecedented paradigm in which a chromosome-architecting nucleoid-associated protein (NAP) is repurposed as a viral infection sensor. When phage attack leads to genome degradation, the NAP sensor is released from the nucleoid to the cytoplasm, where it binds and activates diverse immune effectors. One such effector is a nucleotide-modifying toxin normally existing as an inactive homotetramer. NAP binding converts it into a catalytically active heterotrimer that halts both transcription and translation. Phylogenetic analyses unveiled the high modularity, polyphyletic origin, and wide distribution of NAP-mediated defenses. Collectively, we define a distinct class of defense systems in which bacteria sense phage-induced genome damage through NAP relocation, highlighting an unexpected but essential role for these proteins as sentinels of genome integrity.

microbiology↗

bioRxiv2026

Polyclonal-Monoclonal Transition in Lung Squamous Cell Carcinoma Evolution

The evolutionary trajectory of lung squamous cell carcinoma (LUSC) remains poorly defined, hindering the development of effective therapies. By integrating genomic and transcriptomic sequencing from human LUSC specimens, we delineated a polyclonal-to-monoclonal evolutionary trajectory during LUSC progression. This evolutionary pattern was corroborated by single-cell RNA sequencing, which revealed consistent tumor cell heterogeneity. Specifically, the SBS5 mutational signature was enriched and correlated with poor prognosis independent of tumor stage. By further utilizing the spontaneous LUSC mouse model to identify key genomic and genetic events in LUSC progression, we observed that the JNK pathway was inhibited and that cytoskeleton-related pathways were dysregulated during LUSC development, and identified the mutations in the JNK pathway (e.g., DACT1) and cytoskeletal regulators (e.g., KIF26A). Collectively, these findings established a polyclonal-monoclonal evolution paradigm for LUSC, potentially regulated by JNK pathways, which could benefit LUSC precision therapeutics.

genomics↗

bioRxiv2026

Gq-pathway activation in hippocampal CA1 astrocytes rescues ischemia-induced memory deficits and synaptic plasticity

More than 30% of stroke survivors develop post-stroke cognitive impairment (PSCI), for which current neuron-centric therapies not only lack cellular specificity but also carry risks of adverse effects such as epilepsy. Here, we explore the therapeutic potential of astrocytes by chemogenetically activating Gq signaling in hippocampal astrocytes, which rescues memory deficits and synaptic plasticity impairments in a mouse model of ischemic stroke. Gq activation restores dendritic complexity, spine density, and long-term potentiation in hippocampal CA1 neurons. Fiber photometry further reveals that astrocytic Ca{superscript 2} signals precede neuronal activity by 600 ms during novel environment exploration, indicating that astrocytes prime memory encoding. In contrast, Gi pathway activation induces pathological neuronal hyperactivity without cognitive improvement. These findings establish that astrocytes regulate post-stroke recovery through pathway-specific calcium signaling and uncover a previously unknown temporal hierarchy in astrocyte-neuron communication during memory processing, offering a new glia-targeted strategy to overcome the limitations of current neuromodulation approaches for PSCI.

neuroscience↗

bioRxiv2026

Hybrid eTFCE-GRF: Exact Cluster-Size Retrieval with Analytical p-Values for Voxel-Based Morphometry

Threshold-free cluster enhancement (TFCE) improves sensitivity in voxel-wise neuroimaging inference by integrating cluster extent across all thresholds, but its reliance on permutation testing makes it prohibitively slow for large datasets. Probabilistic TFCE (pTFCE) replaces permutations with analytical Gaussian random field (GRF) p-values, which reduces runtime by more than an order of magnitude, yet relies on a fixed threshold grid that introduces discretisation error. Exact TFCE (eTFCE) eliminates this discretisation by computing the integral exactly via a union-find data structure, but still requires permutations for inference. We propose a hybrid method that combines eTFCEs union-find data structure for exact cluster-size retrieval with pTFCEs analytical GRF inference. The union-find builds the full cluster hierarchy in a single pass over sorted voxels and enables exact cluster-size queries at any threshold in near-constant time; GRF theory then converts these sizes into analytical p-values without permutations. We validate the method through a six-experiment Monte Carlo study on synthetic phantoms (643, 80 subjects): null family-wise error rate is controlled at the nominal level (0/200 rejections, 95% CI [0.0%, 1.9%]); power curves match baseline pTFCE (Dice [≥] 0.999 at sufficient signal); smoothness estimation error is below 1%; and cross-variant concordance exceeds r = 0.99. On real brain data from UK Biobank (N = 500, within-vendor) and IXI (N = 563, cross-vendor), the method detects biologically plausible scanner, age, and sex effects; on IXI, significance maps form strict subsets of the reference R pTFCE output, which supports conservative family-wise error control. Both methods are implemented in pytfce, a pure-Python package with no R or FSL dependencies, available on PyPI. The baseline reimplementation completes whole-brain voxel-based morphometry in [~] 5 s (75 x faster than R pTFCE), while the hybrid variant completes in [~] 85 s (4.6 x faster) with the advantage of exact cluster-size retrieval; both are more than three orders of magnitude faster than permutation-based TFCE. HighlightsO_LIHybrid eTFCE-GRF: exact cluster-size retrieval with analytical inference C_LIO_LIFWER controlled at nominal level in Monte Carlo validation C_LIO_LIpytfce package 4.6 x (hybrid) to 75 x (baseline) faster than R pTFCE C_LIO_LIValidated on UK Biobank (N = 500) and IXI (N = 563) C_LIO_LIOpen-source Python package: pip install pytfce C_LI

neuroscience↗

bioRxiv2026

Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network

Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

neuroscience↗

bioRxiv2026

Cross-species meta-analysis reveals determinants of homing gene drive performance

Homing gene drives can bias their inheritance above Mendelian expectations, but reported outcomes vary widely. We compiled a cross-species dataset of nearly one million scored progeny from 42 publications reporting CRISPR/Cas9 endonuclease-based gene drives in 10 model, pest, and vector species. Using multilevel meta-analytic models, we evaluate biological, cross, and transgene design factors as predictors of biased drive inheritance. Species is the strongest predictor, but most heterogeneity remains unexplained; design features each explain a modest fraction of the remaining variation, with large construct-to-construct differences pointing to the full combination of design choices rather than any single factor. Nuclease expression timing, the most common optimization target, has limited predictive value after accounting for correlated factors, and predictions do not transfer well between species. Maternal nuclease deposition has a marginal effect on drive inheritance but dramatically increases somatic phenotype rates in offspring, revealing a tissue-of-action rather than repair-outcome effect. An interactive web tool enables community analysis of this dataset, which will guide the design of more efficient gene drives for genetic vector control, invasive species management and other applications. https://sverkuijl.shinyapps.io/GeneDrive/

bioengineering↗

bioRxiv2026

A vagal gut-brain axis for feeding induced gastric secretion

Feeding begins with food-seeking and ends with nutrient absorption and waste elimination. Although meal-related sensory cues are known to shape feeding behavior, how they engage neural circuits to control digestion remains unclear. We dissected vagal gut-brain-gut pathways that mediate meal-induced gastric secretion. Vagal afferent neurons innervating the stomach were necessary and sufficient for secretion. Among those, Glp1r and Sst neurons robustly drove acid, pepsin, and gastrin secretion, whereas Oxtr and Vip neurons had little effect. Piezo2 was expressed in Glp1r mechanosensory afferents, and conditional deletion of Piezo channels attenuated distension-evoked secretion. On the efferent limb, genetically defined vagal motor neuron subsets differentially recruited secretory outputs: Cck neurons broadly increased acid, pepsin, and gastrin, whereas Grp neurons preferentially drove acid secretion. Finally, enteric circuit dissection revealed modular output control: Calb2 enteric neurons potently drove secretion with minimal motility effects, Grp enteric neurons coupled secretion to motility, and Cysltr2 enteric neurons selectively modulated acid output. Together, these results define a cell-type resolved vagal-enteric architecture for gastric control and show that secretory and motor programs can be segregated across dedicated enteric neuron subtypes.

neuroscience↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2026

Modulation of Intrauterine Adhesion Fibrosis by Lactobacillus delbrueckii: Insights from ERK1/2 Pathway Inhibition and Multi-omics Analyses

BackgroundIntrauterine adhesion (IUA) is an important cause of infertility and menstrual disorders, substantially reducing quality of life and increasing healthcare costs. Current treatments, including hysteroscopic adhesiolysis and hormonal therapy, show limited efficacy and high recurrence, highlighting the need for new therapeutic strategies. MethodsWe evaluated the antifibrotic effects of Lactobacillus delbrueckii in IUA, with a focus on ERK1/2 signaling inhibition. A multimodal framework integrating clinical outcomes, cellular and animal experiments, uterine microbiome profiling (16S rRNA sequencing), transcriptomics, and metabolomics was used to characterize host-microbe-metabolite interactions relevant to fibrosis. ResultsClinically, patients receiving L. delbrueckii showed lower adhesion formation scores(AFS) and reduced recurrence. Microbiome profiling demonstrated increased uterine microbial diversity and altered community composition in treated patients, accompanied by reduced expression of fibrosis-associated markers. In both cellular and mouse models, L. delbrueckii decreased fibrotic marker expression and reduced ERK1/2 phosphorylation. Metabolomics identified elevated 3-hydroxyanthranilic acid (3-HAA) in treated groups, suggesting a potential role in immune and inflammatory modulation. ConclusionL. delbrueckii attenuates IUA-associated fibrosis, at least in part, by suppressing ERK1/2 phosphorylation and increasing the abundance of the metabolite 3-HAA. These findings support the therapeutic potential of L. delbrueckii for improving uterine repair and reproductive outcomes. Future studies should validate efficacy and safety in larger, diverse cohorts and assess long-term outcomes.

microbiology↗

bioRxiv2026

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta

Cryogenic electron microscopy (cryo-EM) has become a widely used technique for determining the three-dimensional structures of biological macromolecules. Despite its advantages, building accurate structural models from cryo-EM data remains challenging, particularly at non-atomic resolutions. Here, we present CryoZeta, a de novo structure modeling program that leverages a diffusion-based generative deep neural network to integrate cryo-EM map density features with a biomolecular structure prediction pipeline similar to Alphafold3. By jointly leveraging sequence information and density-based features, CryoZeta generates highly accurate structural models that are consistent with the experimental map density. Evaluated on benchmark datasets covering protein complexes, protein-nucleic acid assemblies, and nucleic acid-only systems at resolutions up to 10 [A], CryoZeta consistently outperforms existing cryo-EM modeling methods in atomic accuracy. These results highlight the benefits of directly incorporating cryo-EM density into modern structure prediction pipelines and establish the method as a robust tool for automated, high-fidelity modeling from cryo-EM maps.

bioinformatics↗

bioRxiv2026

MATR3 is essential for oocyte growth and maturation quality through a dual molecular mechanism

The molecular mechanisms governing mRNA accumulation during oocyte growth, essential for developmental competence, remain poorly understood. This study investigates the role of Matrin-3 (MATR3), a highly expressed RNA-binding protein in growing oocytes (GOs), using oocyte-specific knockout mouse models and human oocyte maturation arrest (OMA) samples. The results showed that MATR3 was more abundant in GOs than fully-grown oocytes (FGOs), highly expressed in the nucleus of non-surrounded nucleolus (NSN) oocytes, and exited the nucleus during the NSN-to-surrounded nucleolus (SN) transition. In OMA patients, MATR3 nuclear localization was missed, with smaller oocytes than FGOs. Further, Matr3 deletion in mouse GOs caused restricted oocyte growth, global transcription disorders, follicle development failure, blocked GO-granulosa cell communication (via reduced Gdf9 and Radixin expression), and infertility. Mechanistically, MATR3 regulated transcription by recruiting H3K9me2-demethylating lysine-specific demethylase 3B or binding target gene promoters, like Radixin. These findings reveal a critical role of MATR3 in orchestrating transcription and paracrine signaling during oogenesis and suggest its potential as a diagnostic and therapeutic target for OMA.

physiology↗

bioRxiv2026

Pragmatic analysis with knowledge-guided for unraveling peptide-protein pairwise non-covalent mechanisms

Understanding peptide-protein interactions is vital for decoding cellular signaling and developing targeted therapies. However, the complexity of multi-molecular associations and diverse non-covalent interactions make accurate prediction and site-specific annotation challenging. Here, we propose KGIPA, a knowledge-guided pragmatic analysis framework that incorporates pragmatic concepts from natural language into life science, capturing the influence of biological environments on non-covalent interactions. KGIPA integrates intra- and extra-linguistic contextual information to combine multimodal single-molecule features and build residue-level interaction maps. It also uses biological prior knowledge to coordinate various non-covalent interaction types. Benchmark tests demonstrate KGIPA outperforms the state-of-the-art methods in evaluating molecular binding, including protein and peptide binding residues and residue-pair interactions. Furthermore, KGIPA demonstrates strong performance in peptide-protein binding affinity prediction and peptide virtual screening. Wet-lab experiments validate its reliability, revealing high consistency between predicted and experimentally measured binding behaviors. These results highlight KGIPAs potential to accelerate peptide drug discovery and establish pragmatic analysis as an effective paradigm for decoding the molecular language of interactions.

bioinformatics↗

bioRxiv2026

SLECA: a single-cell atlas of systemic lupus erythematosus enabling rare cell discovery using graph transformer

Systemic lupus erythematosus (SLE) is a highly heterogeneous autoimmune disease with complex immune and molecular dysregulation. While rare immune cell populations are increasingly recognized as critical drivers of disease pathogenesis and progression, the lack of sufficiently powered, comprehensive single-cell atlases has limited their systematic identification and characterization. To address this gap, we present SLECA, the first large-scale single-cell atlas of SLE, enabled by a novel graph-transformer framework for the interpretable discovery and analysis of disease-relevant rare cell populations. SLECA integrates 366 single-cell samples with standardized clinical and biological metadata, providing a comprehensive and analytically unified atlas of systemic lupus erythematosus. By enabling scalable integration and interpretable analysis, SLECA resolves 54 distinct cell types, including rare populations with critical disease relevance. Notably, we identify double-negative T cells (DNTs) as a disease-expanded population whose abundance correlates with clinical severity. Through in silico perturbation, we demonstrate that key transcription factors, specifically JUN and EGR1, can reprogram DNT cells toward conventional T-cell phenotypes, highlighting actionable regulatory vulnerabilities in SLE.

bioinformatics↗

bioRxiv2026

Viral haplotype reconstruction from long reads with virCHap

Resolving genomes at the haplotype level for viral populations is crucial for understanding the prevalence of viral diseases and for the development of effective therapeutic treatments. However, viral haplotype reconstruction still presents challenges, such as an unknown number of strains, high inter-strain similarity, repetitive regions, and difficulties in abundance estimation. Here, we developed virCHap, a new reference-based haplotype phasing algorithm for viruses, which applies graph partitioning followed by iteratively quantifiable cluster merging on long-read sequencing data. Benchmarking on simulated and real datasets demonstrates that virCHap outperforms current tools in terms of recall, accurate abundance estimates and read clustering accuracy. On the simulated large-genome VZV experiment, virCHap had a 96.5% recall, 14% higher than the second-best method, and had the most accurate abundance estimates. On a real 5-strain PVY dataset, virCHap had a precision exceeding 92.9%, a recall of over 97%, and a read clustering accuracy of 82%, outperforming the second-best method by 32%. On a real 6-strain SARS-CoV-2 dataset, virCHap achieved >96.9% accuracy, and the most accurate abundance estimates within the spike gene.

bioinformatics↗

bioRxiv2026

m6A-dependent microRNA binding to chromatin-associated RNA for transcriptional activation

For decades, microRNAs (miRNAs) have been canonically viewed as post-transcriptional repressors. We discovered extensive binding of microRNAs to chromatin-associated RNAs (caRNAs) and uncovered an N6-methyladenosine (m6A)-dependent transcriptional activation mechanism of microRNAs. We show that m6A-binding proteins FXR1/2 anchor AGO1/2 at m6A-marked caRNAs, where AAGUGC-seed microRNAs function as guide RNAs to direct AGO positioning. This dual anchoring stabilizes the AGO-microRNA/FXR-m6A complex at specific loci, which in turn recruits the ATP-dependent chromatin remodeler SMARCA4 (BRG1) to promote local chromatin opening and TET1 for DNA demethylation, respectively. Together, these coordinated activities establish a transcriptionally permissive chromatin environment, enhancing accessibility and transcription across hundreds of genes in diverse cell types. Beyond the AAGUGC-seed family, additional microRNAs and siRNAs also enhance transcription, suggesting that caRNA binding and transcriptional activation may represent a broader property of small RNAs.

molecular biology↗

bioRxiv2026

Intracellular targeting of a light-activated GPCR elicits spatially selective signalling

G protein-coupled receptors (GPCRs) initiate unique cell responses dependent on their sub-cellular location. Major advances in our understanding of sub-cellular GPCR signalling have been driven by experimental approaches that manipulate receptor trafficking, block signalling at specific intracellular sites, or compare receptor signalling in response to ligands with differing membrane permeability. Here, we describe an optogenetic platform that allows a single stimulus to activate the same GPCR at different intracellular membranes, enabling direct comparisons of spatial signalling in intact cells. To achieve this, we have directed a light-activated GPCR chimera to endosomal, mitochondrial, Golgi and nuclear membranes using C-terminal targeting sequences. We demonstrate activation of cAMP and ERK signalling in response to light at all sites; however, each targeted receptor generates a unique transcriptional fingerprint dependent on its sub-cellular location. Our engineered optogenetic platform reveals that GPCRs targeted to distinct intracellular locations are self-sufficient signalling platforms and highlights GPCR positioning as a critical driver of signalling. TeaserControlling GPCR signals by light and location reveals how intracellular positioning shapes cellular responses.

pharmacology and toxicology↗