Biology subjects

Liu, J.

Publications and source records attributed to Liu, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2026

CHORD: a framework for cross-species single-cell integration across gene, cell and cell-type levels

Quantifying cross-species relationships among cell types from single-cell transcriptomic data can reveal both conserved and divergent patterns of cell-type hierarchies. However, existing cross-species integration methods can be limited in modeling genes beyond orthologs by leveraging cell-type-resolved transcriptional context, or in learning explicit type-level representations. Here we present CHORD, a cross-species integration framework that jointly learns representations of genes, cells and cell types. We demonstrate that CHORD can integrate cross-species single-cell atlases and support cell-type annotation with unknown cell-type detection. In the frog-zebrafish embryogenesis and mammalian motor cortex atlases, CHORD infers cell-type trees that place conserved cell types from different species in relative proximity and summarize hierarchical relationships among cell types. CHORD also supports cross-species comparison of continuous phenotypic variation by placing embryonic cells along an aligned developmental timeline. CHORD further yields gene embeddings that capture orthologous and functional relationships, and gene importance scores linking genes to cell types.

bioinformatics↗

bioRxiv2026

Application of a High-Biomimetic Tumor Organoid-CAF Co-Culture Model for the Efficacy Evaluation of CAR-T Drugs

The tumor microenvironment (TME) is a complex ecosystem composed of tumor cells, cancer-associated fibroblasts (CAFs), immune suppressive cells, and the extracellular matrix (ECM), playing a crucial role in tumor development and CAR-T cell therapy efficacy. CAR-T therapy has shown promise in hematological malignancies but faces challenges in solid tumors due to the TMEs ability to suppress CAR-T cell infiltration, proliferation, and cytotoxicity. Traditional drug evaluation models, such as 2D cell cultures and animal models, have significant limitations due to oversimplification of the in vivo environment or physiological differences between species. Organoid models offer a more biomimetic approach but often fail to fully recapitulate the TMEs complexity and heterogeneity. Our research developed a tumor organoid and CAF co-culture model using the IBAC co-culture chip, demonstrating that CAFs significantly impact CAR-T cell therapy efficacy by forming physical (e.g., fibronectin) and chemical (e.g., IL-10) barriers that prevent CAR-T cell infiltration and cytotoxicity. This model provides a high-biomimetic platform for investigating the TMEs effects on CAR-T therapy and highlights the importance of incorporating a comprehensive stromal component into in vitro models to enhance their predictive power for cancer treatment.

cell biology↗

bioRxiv2026

A global metagenomic atlas uncovers ubiquitous biosynthetic potential linked to adaptation in extreme environments

Extreme environments impose severe physicochemical stresses that drive microorganisms to evolve specialized survival strategies. Microbial secondary metabolites determined by biosynthetic gene clusters (BGCs) are recognized as important mediators of microbial adaptation to environmental stress. However, their ecological roles, particularly habitat-dependent preferences across different environments, remain poorly understood. Although extreme environments provide opportunities to mine microbiomes for unique adaptations, such research is hampered by a lack of systematic overview of its genomic diversity, BGC diversity, and the relationships between them. Here, we constructed a standardized extremophilic genomic catalogue (SEGC) from 1,462 metagenomic samples spanning seven representative extreme habitats. The catalogue comprised 54,661 metagenome-assembled genomes representing 21,805 species, 66.1% of which were previously uncharacterized. With this catalogue, we identified 162,855 BGCs distributed across 81.5% of MAGs. Gene cluster family analysis showed the strong habitat dependence largely explained by species-level habitat specificity. Terpene biosynthetic pathways illustrated habitat-linked adaptive strategies, with hopan-22-ol biosynthesis enriched in acid mine, deep sea and hydrothermal plume environments, while retinal-based phototrophy predominated in cryosphere and saline-alkaline habitats. Metatranscriptomic analyses supported in situ activity of these pathways. In conclusion, we presented a global atlas of biosynthetic potential across extreme-environment microbiota and revealed habitat-dependent patterns of secondary metabolism linked to microbial survival.

microbiology↗

bioRxiv2026

Pyruvate-driven hydrogen production promotes polyphenol bioconversion by gut bacteria

Gut microbial biotransformation of poorly absorbable polyphenols into bioactive, bioavailable metabolites is increasingly recognized as a key mechanism underlying their health benefits of polyphenols. Microbial ellagic acid (EA)-to-urolithin conversion represents a typical example, but the environmental factors that facilitate such metabolism remain underexplored. We discovered that urolithin production by a gut commensal bacterium, Gordonibacter urolithinfaciens (G. uro), is metabolically repressed by arginine. To overcome such limitations, we developed PhenolBoost Medium (PBM) that induces a metabolic shift by suppressing the arginine deiminase pathway while activating pyruvate metabolism and hydrogen production in G. uro, thereby driving urolithin dehydroxylation. Transcriptomic profiling and 13C-isotopic tracing analysis revealed that pyruvate metabolism in PBM upregulates hydrogenase expression, facilitating the dehydroxylation of EA. PBM also promoted the complete conversion of EA to urolithin A in G. uro-Enterocloster bolteae co-culture, and other polyphenol biotransformations. In addition, co-culturing G. uro with hydrogen-producing Bacteroides species significantly increased urolithin production. Furthermore, an arginine-limited, pyruvate-enriched dietary regimen proved effective in vivo, resulting in significantly higher urolithin production and bioavailability in a mouse model. Our findings reveal the critical role of hydrogen in facilitating polyphenol dehydroxylation, and offer a viable nutritional strategy for boosting microbial production of beneficial metabolites from polyphenols.

microbiology↗

bioRxiv2026

16S rRNA k-mer composition encodes microbial functional potential

16S rRNA amplicon sequencing is widely used for microbiome profiling, but most methods rely on reference databases of characterized organisms, limiting its accuracy in function prediction for underrepresented environments. We discovered that 16S rRNA k-mer composition carries substantial functional signal: (i) whole-genome k-mer profiles predict genome-encoded functions, and (ii) 16S rRNA k-mer profiles reflect their source genomes composition. Building on these relationships, we developed embeRNA, a neural network framework that predicts functions directly from 16S rRNA k-mer embeddings without requiring taxonomy assignment or phylogenetic placement. embeRNA outputs per-function probability scores, enabling users to tune decision thresholds to balance precision and recall or account for community novelty. In a stringent "novel microbes" benchmark - where all test sequences shared <97% identity with training data - embeRNA outperformed reference-based methods, particularly for hard-to-label functions. Applied to soil metagenomes with paired 16S and whole metagenome shotgun sequencing (WMS) data, embeRNA recovered most WMS-inferred functions and produced abundance profiles strongly correlated with WMS results, attaining better performance than a reference-based approach. Our findings demonstrate that 16S rRNA directly captures functional potential, and 16S amplicon sequencing data can complement WMS-based inference to broaden functional characterization of microbiomes, especially in understudied environments.

bioinformatics↗

bioRxiv2026

N-3 Polyunsaturated Fatty Acids Ameliorate Post-infarction Cardiac Dysfunction Through Modulation Of Adiponectin-Ceramide Metabolism

BackgroundIt has been shown that n-3 polyunsaturated fatty acids (n-3 PUFA) of marine origin exert significant beneficial effects on myocardial infarction (MI); however, the underlying mechanisms remained unclear. Ceramides play a vital role in the regulation of energy metabolism, mitochondrial function, and apoptosis. Through the integration of clincial studies and animal experiments, this study aimed to determine whether n-3 PUFA improved myocardial function by modulating ceramide metabolism. MethodsIn a case-control study, 100 patients with AMI and 100 healthy pariticipants were enrolled to measure serum ceramide concentrations. Meanwhile, mice were randomly allocated into 4 groups and administrated to a 3-week intervention with n-3 PUFA in triglyceride and phospholipid forms. A mouse model of MI was then established, followed by an additional 4 weeks of continuous intervention. Subsequent comprehensive assessments of cardiac function were performed in the mice. Finally, the mice were euthanized to conduct targeted ceramide lipidomic analysis and other relevant assays. ResultsThe levels of serum C16:0-, C18:0-, C20:0-, C24:1-ceramides and total ceramides in patients with acute myocardial infarction (AMI) were significantly higher compared with the healthy controls. In the murine model of myocardial infarction, pathological analysis via TTC staining demonstrated that interventions with fish oil (triglyceride form) and krill oil (phospholipid form) both significantly reduced myocardial infarct size. Concomitant echocardiographic assessment confirmed that both treatments markedly elevated left ventricular ejection fraction (LVEF), with the magnitude of improvement being significantly superior to that of the model control group. Concurrently, compared with the model group, the concentrations of ceramides in cardiac tissue and serum were significantly lower in the groups with fish oil and krill oil intervention. Western blot analysis further confirmed that n-3 PUFA intervention upregulated adiponectin expression, reduced ceramide accumulation in myocardial tissue, and inhibited mitochondria-mediated cardiomyocyte apoptosis, thereby improving cardiac function and prognosis following myocardial infarction. ConclusionsThis work demonstrates that n-3 PUFA exert cardioprotective effects following MI mediated by adiponectin-ceramide axis. However, there is no significant difference regarding therapeutic efficacy of n-3 PUFA in triglyceride or phospholipid forms.

pharmacology and toxicology↗

bioRxiv2026

Regression-based Modeling of Spearman's Rho for Longitudinal Metabolomics and Mental Wellness in Breast Cancer Patients

SO_SCPLOWUMMARYC_SCPLOWChemotherapy in breast cancer (BC) can substantially affect mental wellness. Advances in metabolomics enable comprehensive profiling of metabolic changes over time during and after treatment, offering insights into biological mechanisms linking chemotherapy to mental health outcomes. To study the association between metabolite profiles and mental wellness, correlation-based analyses are particularly useful. Spearmans rho is a widely used correlation measure and popular alternative to Pearsons correlation, since it also applies to non-linear association between variables. However, existing methods are not designed for longitudinal data and do not allow for covariate adjustments. In this paper, we propose a novel regression-based framework grounded in a class of semiparametric models, the functional response models, to extend this popular correlation measure to longitudinal settings with missing data under the missing at random assumption. This framework facilitates inferences about temporal changes in correlations over time and association of explanatory variables for such changes. We use simulation studies to evaluate performance of the approach with moderate sample sizes. We apply the approach to a one-year longitudinal substudy of the EPIGEN study to examine the longitudinal association between metabolite profiles and mental wellness in BC patients undergoing chemotherapy. The identified metabolites may serve as candidates for future in-depth bioinformatics analyses and translational investigations.

cancer biology↗

bioRxiv2026

Strain- and age-dependent divergence in mouse appetitive spatial learning and decision strategies

Animals rely on associative spatial memory to navigate toward previously learned, reward-associated goals. This reward-guided navigation is supported by the hippocampus and its interactions with cortical and subcortical regions: processes which are vital for integrating sensory cues and forming experience-dependent associations. In adulthood, hippocampal-dependent information processing is shaped by aging, reflecting changes in synaptic plasticity and neuromodulatory support. In parallel, mice with distinct genetic backgrounds show systematic differences in behavior, sensory function, and hippocampal plasticity. How mouse strain and age interact to affect spatial appetitive memories has not been well defined. Here, we trained CBA/CaOlaHsd and C57BL/6 mice in early (2-3 months) or late adulthood (7-8 months), in daily training events, to perform a T-maze task with rewards available at a fixed location and decreasing probability in one maze arm. The task consisted of an initial deterministic phase in which a correct response was always rewarded, followed by a probabilistic phase during which reward omissions became increasingly prevalent. We measured correct choices, as well as latencies across training blocks, and combined trial-by-trial metrics with reinforcement learning modeling to assess decision strategies. We observed that CBA/CaOlaHsd mice displayed lower choice latencies than C57BL/6 mice, reached high performance earlier, and maintained better performance when the reward probability decreased. Age was associated with higher latencies and modulated both performance and decision policies in a strain-dependent manner. Moreover, CBA/CaOlaHsd mice displayed higher learning rates from positive outcomes and adopted strategies consistent with more robust context exploitation under uncertainty. C57BL/6 mice, by contrast, exhibited stronger omission-driven strategy switching. Together, these findings reveal robust strain- and age-dependent differences in trial-by-trial decision policies and spatial learning performance.

neuroscience↗

bioRxiv2026

Light-sheet excitation-encoded volumetric spectroscopy for fast multiplexed imaging and quantitative physicochemical mapping in cells

Understanding how cellular organization relates to function requires imaging approaches that resolve structural and physicochemical information throughout three-dimensional volumes. Here we introduce light-sheet excitation spectroscopic microscopy (LS-ExSM), a method that captures full excitation spectra with [~]10 nm spectral resolution at each voxel for multiplexed imaging and quantitative spectroscopic readout. Excitation-wavelength encoding makes this spectroscopic readout compatible with single-objective wide-field light-sheet imaging while maintaining high spectral fidelity. Combined with sparse volumetric sampling and deep-learning-based reconstruction, LS-ExSM achieves high-speed volumetric imaging while preserving structural and spectral information. We demonstrate six-color volumetric imaging of live cells at near-second temporal resolution with minimal crosstalk, enabling visualization and quantitative analysis of dynamic organelle interactions in three dimensions. We further apply LS-ExSM to three-dimensional mapping of lipid-droplet polarity at near-diffraction-limited resolution. Together, these results establish excitation-encoded volumetric spectroscopy as a route to whole-cell imaging of subcellular organization and physicochemical state.

cell biology↗

bioRxiv2026

An orthogonal near-infrared optical switch for wireless neuromodulation in freely behaving mice

Modulating neuronal activity with light is a powerful tool for neuroscience research. However, currently available technologies often require invasive fibers for delivery of visible light, causing tissue damage and limiting behavioral studies. Although near-infrared (NIR) neuronal modulation improves tissue penetration depth, sustained NIR illumination during neuromodulation raises significant concerns about photothermal effects. Here, we introduce a dual-wavelength near-infrared switch (Dual-NIR Switch) that uses two transcranial NIR inputs to initiate and terminate neuronal activation, enabling tether-free neuromodulation in freely moving mice. Dual-NIR Switch employs orthogonal dichromatic upconversion nanoparticles that emit blue and green light under 980 nm and 808 nm excitation, respectively, to activate and inactivate the step-function opsin SOUL. Therefore, transient 980 nm NIR illumination initiates neuronal excitation, which will remain excited without further stimulation but will be rapidly terminated on demand by a subsequent 808 nm illumination. Upon transcranial 980 nm and 808 nm NIR illuminations in freely moving mice, we achieve on-demand control of behavioral paradigms across tunable timescales, ranging from seconds to minutes and even extending to sub-hour durations. By eliminating the need for sustained NIR irradiation, Dual-NIR Switch offers an on-demand, duration-tunable neuromodulation tool for both basic neuroscience and potential therapeutic applications in treating brain diseases.

neuroscience↗

bioRxiv2026

A Brain-wide Neuronal Spiking and Behavior Dataset for Working Memory-Specific Activation and Reactivation in Mice

AbstractHow do millisecond-scale neural activities mediate multi-second behaviors such as working memory? This dataset provides brain-wide, high-spatiotemporal-resolution single-unit spiking activity recorded from 40 head-fixed mice performing an olfactory delayed paired-association (ODPA) task, encompassing 33,028 neurons across 62 anatomically defined brain regions. Utilizing this dataset enables the extraction of millisecond-scale within- and cross-region spike couplings enriched among memory-content-selective neurons. These couplings may be used to identify higher-order, hierarchically structured activity motifs. Because these multi-neuron patterns are captured both during active task performance and spontaneous inter-trial intervals, this dataset provides a resource for studying replay-related activity and for testing hypotheses about circuit-level processes associated with perceptual working memory. The dataset supports broad investigations into cross-regional spike organization, functional coupling, and temporal coding in working memory circuits.

neuroscience↗

bioRxiv2026

Retinoic acid resolves the conflict between X-chromosome inactivation and pluripotency program in female cleavage-stage embryos

The tight coupling of X-chromosome inactivation (XCI) and pluripotency is a paradigm of holistic developmental regulation, and their inverse correlation enables XCI establishment during implantation. In contrast, the co-establishment of imprinted XCI (iXCI) and pluripotency program in female preimplantation embryos challenges the holistic pattern, but how embryos coordinate the two remains unknown. Here, we find that Nanog during the cleavage stage strongly represses the co-upregulated Rnf12-Xist axis, the triggering signaling of iXCI, creating a conflict that threatens iXCI. We identify retinoic acid (RA) as the coordinator that resolves the conflict and safeguards iXCI. RA activates its receptor RARG to evict NANOG from Rnf12 5region, avoiding NANOGs threat. In vivo, either maternal vitamin A deficiency or embryonic RA synthesis defects results in impaired iXCI and female-biased embryonic lethality. Thus, we highlight RA as a master regulator to holistically balance iXCI and pluripotency during cleavage stages, thereby ensuring proper female embryogenesis.

developmental biology↗

bioRxiv2026

A Preoptic Neuronal Population Regulates Energy Expenditure and Balance

Maintaining energy balance requires coordination between food intake and energy expenditure, yet the neural pathways that regulate energy expenditure remain unclear. This study identifies kappa opioid receptor-expressing neurons in the preoptic area of the hypothalamus as a key regulator of whole-body metabolism. Using mouse models combined with fiber photometry, chemogenetic activation and inhibition, and chronic disruption of synaptic output, the results show that activity of these neurons follows daily pattern, are suppressed during feeding, and their inhibition acutely increases energy expenditure, body temperature, and activity levels. Long-term inhibition of this population produces sustained weight loss, selective reduction of white fat, preservation of lean mass and brown fat, and improved glucose tolerance even during high-fat feeding. These findings reveal a previously unrecognized circuit that links metabolic state with daily timing cues and suggest that targeting this neuronal population may offer new strategies for treating obesity and related metabolic disorders.

neuroscience↗

bioRxiv2026

Dormant Bacteria's Fatal Attraction to RNA Bacteriophages

Phages are the most abundant biological entities on Earth, yet RNA phages are strikingly scarce compared to their DNA counterparts--a long-standing mystery in phage biology. Here, we use dsRNA phage phiYY as a model to demonstrate that while RNA phages efficiently infect growing bacteria, they are gradually eliminated inside dormant bacteria through weak and time-dependent ROS-mediated damage. The RNA phages decline over days rather than through immediate clearance inside dormant bacteria. Accordingly, scavenging ROS with mannitol or overexpressing ROS degradation enzymes AhpB/TrxB2 rescues RNA phages from elimination. Crucially, because the underlying ROS-mediated RNA damage is minimal, RNA phage survival hinges on genomic redundancy. In single-phage infections, the lone RNA genome is highly vulnerable to cumulative damage and is eventually inactivated. In contrast, during co-infection by multiple RNA phages, the presence of multiple genome copies provides functional redundancy, thereby allowing a fraction of RNA phages to survive inside dormant bacteria. Given that most environmental bacteria are dormant and subject to heterogeneous phage infection, this copy number-dependent vulnerability offers a possible explanation for the scarcity, but not perish, of RNA phages in nature.

microbiology↗

bioRxiv2026

A Prefusion Form of Herpes Simplex Virus 1 gB has a Distinct Antigenic Signature

Herpes simplex virus (HSV) fusion and entry is mediated by a cascade of interactions among gB, gD and gH/gL. The gB homotrimer undergoes a conformational transition from a metastable prefusion state to a more stable postfusion form, driving fusion of the viral envelope and a host cell membrane. An H516P mutation in gB domain III restricts formation of the extended core alpha helix and constrains gB in a prefusion state. Several prefusion gB structures have been determined that contain this mutation. We assessed the antigenic reactivity of gB H516P by quantitative immunodotblot using a panel of gB-reactive monoclonal antibodies All antibodies tested bound to both prefusion (H516P) gB and wild type gB. Antibodies tested to gB domains II, IV and V exhibited differential binding to H516P gB compared to wild type gB. The results suggest that gB H516P has a distinct antigenic profile. The antigenic signature of H516P may be useful as a rapid indicator of prefusion forms of gB. The low pH environment of the cellular endosome is a cell-specific factor for HSV entry and triggers antigenic changes in gB. The step at which low pH impacts gB refolding to execute fusion is not well-understood. The results suggest that gB H516P undergoes wild-type-like conformational changes in gB domains I and V triggered by low pH. We propose that pH acts on an early stage of gB fusion function, prior to extension of the domain III core helix.

microbiology↗

bioRxiv2026

Generative design of intrinsically disordered protein regions with IDiom

Intrinsically disordered protein regions are ubiquitous across all kingdoms of life. These structurally heterogeneous regions play central roles in cellular processes such as transcriptional regulation, cellular signaling, and subcellular organization, yet they have remained largely inaccessible to rational design. Structure-based generative methods are not applicable to proteins that lack a stable fold, and existing sequence-based approaches for disordered regions rely on sampling methods that do not capture the evolutionary statistics of natural disordered regions. Here, we introduce IDiom, an autoregressive protein language model trained on 37 million intrinsically disordered region sequences curated from the AlphaFold Database. Trained using a fill-in-the-middle data augmentation, IDiom generates disordered region sequences conditioned on their surrounding structured context, as well as fully disordered proteins without any context. The model generates diverse sequences that recapitulate biologically relevant sequence features of natural disordered regions, and we demonstrate that post-training via reinforcement learning with a subcellular localization reward model produces sequences with features which are consistent with known sequence determinants of compartment-specific localization. These results establish IDiom as a general platform for the generative design of intrinsically disordered proteins and regions.

bioinformatics↗

bioRxiv2026

Continental-scale multi-omics reveals a distinct microbial-viral biome in sandy beach ecosystems structured by tidal zonation

Sandy beaches are dynamic coastal interfaces shaped by strong physical forcing and intense exchange between marine and terrestrial environments, yet their microbiomes remain poorly resolved at the genomic scale. Here we present a genome-resolved survey of microbial and viral communities across sandy beaches spanning a continental-scale latitudinal gradient along the Chinese coastline. By integrating cross-shore sampling, coastal geochemistry and large-scale multi-omics, we generated 978 metagenomes, 63 viromes and 72 metatranscriptomes, reconstructing 13,337 metagenome-assembled genomes and 38,255 viral populations. Sandy beach microbiomes exhibit exceptionally high genomic novelty, with more than 90% of species-level genomes representing previously undescribed taxa, suggesting that permeable coastal sediments constitute a distinct microbial and viral reservoir. Tidal zonation emerged as a dominant ecological driver structuring microbial diversity, metabolic strategies and virus-host interactions across cross-shore gradients. Genome-resolved analyses revealed systematic metabolic shifts from oxic heterotrophy in supratidal sediments toward increasingly chemolithotrophic and autotrophic pathways toward the low-intertidal and subtidal zone. Sandy beach microbiomes further encode broad potential for hydrocarbon and plastic transformation, together with diverse biosynthetic and antibiotic resistance repertoires that may mediate microbial chemical interactions. Together, these findings identify sandy beaches as a previously under-recognized microbial-viral biome shaped by tidal forcing, providing insight into microbiome evolution and coastal ecosystem resilience under increasing anthropogenic pressure.

microbiology↗

bioRxiv2026

Deep-Plant: a supervised foundation model for plant regulatory genomics

Large-scale sequence-to-function deep learning models have demonstrated unparalleled ability to model biological sequences and have revolutionized the field of regulatory genomics. However, the majority of such efforts have centered on human and mammalian systems, leaving plant regulatory genomics comparatively underexplored. To address this gap, we introduce DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW, a supervised foundation model trained to predict chromatin state directly from genomic sequence. In contrast to large language models, which are trained in a selfsupervised manner using sequence alone, our model is trained to predict chromatin state across tissues and conditions. Training the model on a large collection of genome-wide experiments including DNA accessibility, transcription factor binding, and histone modifications, provides it with added biological context beyond the sequence itself. We demonstrate that the resulting model is an effective platform for developing accurate models of regulatory activity relevant to gene expression and active enhancers, exhibiting large improvements in speed, accuracy, and interpretability over the complementary approach of fine-tuning DNA language models. DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW models are available in Arabidopsis and rice, and work well as a building block for sequence modeling in related species such as corn. Together, these results establish supervised, chromatin-informed foundation models as a practical and effective paradigm for regulatory sequence modeling in plants.

genomics↗