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Biology subjects

Zheng, S.

Publications and source records attributed to Zheng, S..

At least 55 records · Page 3Linked to original sources

Spatial learning in multi-scale environments: Roles of hippocampus, orbitofrontal cortex, and retrosplenial cortex

Navigation cognition involves the learning of multi-scale environments and the formation of cognitive maps. How do humans build cognitive maps in multi-scale environments? Cognitive maps are thought to be organized hierarchically, with local representations for subareas and global representations for the entire environment. However, it remains unclear how spatial learning influences the representations of multi-scale environments and their underlying neural mechanisms across different levels of representation. In the current study, we built a virtual environment (VE) with four orthogonally positioned rectangular rooms, each containing eight objects at the corners. Twenty-three healthy subjects completed a four-session spatial memory experiment conducted over two weeks. We measured their brain activity by using BOLD-fMRI at two stages: pre-learning stage and post-learning, when they were judging the relative direction between the objects within the VE. We found that with the progression of learning, the subjects shifted from relying on local, directional cues to using more global representations of the environment. At the neural level, the hippocampus (HIP), retrosplenial cortex (RSC), and orbitofrontal cortex (OFC) played distinct roles in encoding spatial information across the two learning stages. Specifically, after learning, the HIP shifted from local to global representations, while the RSC and OFC supported the integration of spatial information across these representational levels. In addition, the anterior cingulate cortex was involved in forming global representations, facilitating efficient spatial processing as learning advanced. These findings revealed how spatial learning leads to adaptive shifts in brain activity, contributing to the formation of cognitive maps in complex, multi-scale environments. HighlightsO_LIUsing fMRI to study neural mechanisms of cognitive map formation in a multi-scale environment. C_LIO_LIDetected that local and global spatial representations coexist and evolve during spatial learning. C_LIO_LIFound that learning progress is associated with activation changes in the entorhinal cortex, hippocampus, and retrosplenial cortex. C_LIO_LIRevealed the complementary roles of hippocampus and orbitofrontal cortex in representing the environment. C_LIO_LIShowed that the retrosplenial cortex improves both local and global representations, supporting the integration of multi-scale spatial information. C_LI

neuroscience↗

Interaction of Carbamoyl-Phosphate Synthase 1 with Agmatinase in the Liver of Torpid Bats

Mammalian torpor imposes unique metabolic constraints, yet the mechanisms of nitrogen metabolism during this state remain unclear. In this study, we show that the urea cycle is selectively regulated rather than broadly suppressed in torpid bats. A significantly increased abundance of carbamoyl-phosphate synthase 1 (CPS1) maintained its functional capacity during torpor and arousal in Myotis ricketti. Proteomic analyses and confocal microscopy identified a specific association and co-localization between CPS1 and agmatinase (AGMAT), an ATP-independent enzyme involved in nitrogen metabolism. Co-localization of CPS1 and AGMAT was also observed in torpid Rhinolophus ferrumequinum, a phylogenetically distant species. Fluorescence resonance energy transfer (FRET) further supported an indirect CPS1-AGMAT interaction. Most urea cycle enzymes exhibited stable or only moderately reduced expression during bat torpor, and metabolic profiling demonstrated sustained nitrogen flux. Together, these findings reveal a conserved adaptive mechanism that maintains urea cycle function, potentially enhancing osmotic stability and energy efficiency during prolonged fasting and water scarcity.

zoology↗

scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation

Recent advances in single-cell multi-omics co-assays and spatiotemporal sequencing technologies have provided unprecedented opportunities for systematically characterizing cellular heterogeneity. However, severe sparsity and pronounced spatial heterogeneity--hallmark features of complex diseases and tumor microenvironment--remain major obstacles in deciphering cellular multi-omics data. Here, we present scPhoenix, a contrastive learning-based framework for single-cell cross-modality translation. scPhoenix adopts a two-stage Aux-Core strategy to disentangle modality-specific feature extraction from cross-modality feature interaction. Across diverse datasets, it preserves cellular heterogeneity during translation and demonstrates significant advantages for data with high sparsity. In addition, the framework integrates a contrastive learning framework with five effective data augmentation methods tailored to single-cell data. Moreover, scPhoenixs design supports extensions to unpaired data training and spatial multi-omics translation, enabling robust performance in scenarios with high spatial heterogeneity. scPhoenix is freely available at https://github.com/liwz-lab/scPhoenix.

bioinformatics↗

The Implications of Alternative Splicing Regulation for Maximum Lifespan

Mammalian maximum lifespan (MLS) varies over a hundred-fold, yet the molecular mechanisms underlying this diversity remain unclear. We present a cross-species analysis of alternative splicing (AS) across six tissues in 26 mammals, identifying hundreds of conserved AS events significantly associated with MLS, with the brain containing twice as many tissue-specific events as peripheral tissues. MLS-AS events are enriched in pathways related to mRNA processing, stress response, neuronal functions, and epigenetic regulation, and are largely distinct from genes whose expression correlates with MLS, indicating that AS captures unique lifespan-related signals. The brain exhibits certain associations divergent from peripheral tissues and reduced overlap with body mass (BM)-associated splicing; neither is observed at the gene expression level. While MLS- and age-associated AS events show limited overlap, the shared events are enriched in intrinsically disordered protein regions, suggesting a role in protein flexibility and stress adaptability. Furthermore, MLS-associated AS events display stronger RNA-binding protein (RBP) motif coordination than age-associated ones, highlighting a more genetically programmed adaptation for lifespan determination, in contrast to the more variable splicing changes seen with chronological aging. These findings suggest alternative splicing as a distinct, transcription-independent axis of lifespan regulation, offering new insights into the molecular basis of longevity.

bioinformatics↗

Chronic Electronic Cigarette Exposure Promotes Atherosclerosis and Chondrogenic Modulation of Smooth Muscle Cells

BackgroundElectronic cigarette (E-cig) use has reached epidemic proportions worldwide, yet its cardiovascular consequences remain poorly defined. While several lines of evidences in human epidemiological and animal studies suggest chronic aerosol exposure accelerates atherosclerosis; the cellular and molecular mechanisms underlying this pathological remain unknown. MethodsWe exposed hyperlipidemic mice to chronic e-cigarette aerosol inhalation and characterized the plaque cellular landscape by coupling SMC lineage tracing with single-cell transcriptomic/epigenomic profiling and histologic phenotyping. We subsequently leveraged human coronary artery smooth muscle cells (HCASMCs) to validate in vivo discovery and identified E-cig specific pathological signaling pathways relevant to human vascular disease risk. ResultsChronic E-cig aerosol exposure accelerated atherosclerosis, increasing both SMC phenotypic modulation and plaque macrophage burden in a lipid-independent manner. Transcriptomically, SMCs are particularly more sensitive to E-cig than other vascular cell types. E-cig exposure reprogrammed SMCs toward a pro-calcifying, chondrogenic phenotype, thereby enhancing vascular ossification in vivo and in HCASMCs in vitro. Mechanistically, E-cig mediated SMC fate alteration occurs through activation of a glutamatergic/NMDAR signaling program, that increased NMDAR-dependent Ca2+ influx in a GRIN2A dependent manner. Notably, inhibition of GRIN2A mediated signaling reversed E-cig-induced pathological shifts in SMC phenotype. ConclusionsSMC chondrogenic reprogramming and subsequent vascular calcification are central to the detrimental cardiovascular consequences of E-cig exposure. Our findings implicate a GRIN2A-dependent glutamatergic/NMDAR signaling axis in SMC as a primary driver of this calcifying remodeling program. These findings define a SMC-specific vulnerability to E-cig aerosols and establish the GRIN2A/NMDAR pathway as potential therapeutic targets for mitigating E-cig-associated cardiovascular disease.

genomics↗

Structural insights and engineering of deep-sea halophilic PET hydrolytic enzymes

Pervasive use of polyethylene terephthalate (PET) poses tremendous challenges for global waste management and environmental sustainability, fueling growing interests in enzymatic degradation as an eco-friendly solution. While PET hydrolases hold significant promise, their industrial deployment is hindered by insufficient performance, particularly under high-salinity conditions raised from high substrate loads. Building on our previous discovery of three deep-sea PET hydrolases (dsPETase01, dsPETase05 and dsPETase06) with exceptional halophilicity and PET-degrading activity, we here present their three-dimensional structures and mechanistic characterization. Structural comparison, site-directed mutagenesis, and domain swapping reveal key structural features and the essential role of C-terminal acidic residues in salt tolerance. Integrating semi-rational protein design, Transformer-based modeling, and disulfide bond engineering, we synergistically enhance the thermostability and catalytic efficiency of dsPETase05. These findings elucidate the unique structural features and salt adaptation mechanisms of deep-sea halophilic PET hydrolases, and inform the future engineering of biocatalysts for application in harsh industrial environments.

molecular biology↗

Single-dye, transfection-free FLIM multiplexing via bioorthogonal chemistry

Fluorescence lifetime imaging microscopy (FLIM) can visualize multiple targets in a single spectral window, making it a powerful tool to overcome multiplexing limitations during live cell fluorescence microscopy. Here we show that small molecule probes-which are well-suited for imaging applications due to high specificity, low toxicity, and the elimination of transfection requirements-can be fine-tuned via bioorthogonal chemistry to exhibit predictably different fluorescent lifetimes suitable for FLIM multiplexing.

biochemistry↗

An evolutionary leap in plant reproduction: From archegonia and motile sperm to embryo sacs and pollen tubes

The transition of plants from water to land marks a pivotal step in their evolutionary history, with the change in reproductive mode being the most fundamental. This change freed plant reproduction from aquatic dependence, fostering terrestrial lifes prosperity. Key to the shift from archegonial to embryo sac reproduction are pollen tube emergence and female gametophyte changes, though their evolutionary trajectories and mechanisms remain unclear. Ginkgo, a basal gymnosperm, occupies a pivotal position in this transition, retaining motile sperm, simplified archegonia, and primitive pollen tubes, making it an ideal model. To study how the transition between these two reproductive modes occurred, we collected transcriptome data from the reproductive organs of Ginkgo and evolutionarily representative species. Our findings reveal the MYB98-CRP-ECS module- critical for angiosperm pollen tube guidance--in Ginkgos mature archegonia, with pollen tubes containing guidance components. While egg cells are transcriptionally and functionally conserved across land plants, changes in the cell fate of other female gametophyte component cells drove the shift from archegonia to embryo sacs. Pollen tubes may originate from fern male gametophyte internalization and modification. These findings clarify the evolutionary path and molecular basis of this major reproductive transition.

plant biology↗

CAR T cell cytotoxic responses are rapidly generated and sensitive to the unligated TCR

Chimeric antigen receptor (CAR) T cells expressing tumor-targeting engineered receptors can robustly eliminate cancer cells through secretion of cytotoxic factors. Durable remission in leukemia and lymphoma treatment has not been matched in solid tumors. Efforts to maximize tumor destruction and minimize toxicities have driven efforts to tune CAR signaling. However, the molecular mechanisms for CAR triggering and thresholds for activation are incompletely understood. Here, we measured the collection of CAR binding interactions that culminate in polarized delivery of lytic granules to the junction with the target. CAR T cells binarized cytotoxic activities in response to a few binding events and population outcomes were dominated by a subset of cells. Activation at the single molecule level matches the sensitivity of the native T cell receptor (TCR) and points to potent downstream signal propagation. Disruption of the unligated TCR with a transmembrane-targeting inhibitory peptide strongly dampened CAR T cell activation, indicating a critical crosstalk between the two receptors. Harnessing CAR T cell efficacy and reduction of toxicity will require new approaches to modify integration of the binding events, collected stochastically, that are rapidly digitized. These sensitive CAR T cell responses provide new insights into driving cytotoxic signaling through surface interaction engineering.

immunology↗

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as robust inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in Precision@10 for CASP 13 and 14 targets, and 31.94% for DIPS-Plus datasets. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in the low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.

bioinformatics↗

A mechanistic model for dynamics between CAR T cells and target cells captures features that determine killing profiles

Chimeric antigen receptor (CAR) T cells represent a potent, programmable therapeutic that repurposes T cell cytotoxicity toward target cell elimination. Direct killing of tumor cells has been demonstrated in several cancer contexts but deficits in destroying solid tumors have been attributed to molecular and mechanical complexity. Understanding and predicting the efficacy of CAR T cell therapy requires a rigorous framework to capture the mechanistic interactions between immune cells and tumor targets. Toward enumerating such features and to quantitatively describe tumor cell survival in response to CAR T cell visitation, we propose a parametric hazard rate model for the right-censored conditional lifetime of a tumor cell, having the form of a time-discounted integral of cell:cell engagement. The model, conditioned using data on the numbers, durations, and modes of contacts made during the stochastic encounters between the two cell types, extracts mechanistic details from the experiments. We suggest two types of parameters to encapsulate features of CAR T cell killing potency ({kappa}) and the resilience of the tumor ({gamma}), respectively. These parsimonious parameters, nevertheless, generate substantial insights. Firstly, phenotypic heterogeneity from interactions mediated by substrate-engaged CAR T cells dominate killing outcomes, while those initiated in suspension contribute minimally. This emphasizes that a subset of the CAR T cell population is dominating the population outcome. Secondly, the model can be used to predict the killing as a function of time upon perturbation of the system. In this case, biasing the arrival process, toward more engagement in the adherent mode, can tune the rate of killing. Collectively, insights from the model framework imply that CAR T cell accumulation and killing efficiency depend not on the sum of individual T cell interactions, but rather on the weighted, time-dependent sum of heterogeneous interactions that are environmentally modulated. The model facilitates predictive insights into how contact sequences modulate CAR T cell responses and may provide strategies to alter therapeutic regimens.

biophysics↗

Self-inactivating AAV-CRISPR at different ages enables sustained amelioration of Huntington's disease deficits in BAC226Q mice

Huntingtons disease (HD) is a monogenic autosomal dominant neurodegenerative disorder caused by a CAG repeat expansion in the first exon of the HTT gene, yielding a gain-of-toxic-function mutant Huntingtin protein mHTT. CRISPR/Cas9 is a potentially powerful therapeutic tool for treating HD by eliminating mutant HTT (mHTT) gene. We developed a specific SaCas9 guide RNA to target human mHTT, and a self-inactivating gene editing system that abolishes SaCas9 after a short transient expression for high gene editing efficiency and maximal safety to prevent off-target effects. Both conventional and the new self-inactivating gene editing systems achieved successful elimination of mHTT gene, 60-90% mHTT protein and 90% of mHTT aggregation in BAC226Q HD mouse brains, which resulted in significant long-term rescue of neural pathology, motor deficits, weight loss and shortened lifespan. These beneficial effects were observed when gene editing was applied before, at and well after the on-set of pathological and behavioral abnormalities. These proof-of-concept data demonstrate that gene editing can be a highly effective therapeutic approach for HD and other inherited neurodegenerative diseases. One Sentence SummarySelf-inactivating CRISPR for mutant huntingtin in HD mice achieved long-term rescue of neural pathology, motor deficits, weight loss and survival.

neuroscience↗

Analysis of Wheat Spike Morphological Traits Using 2D Imaging

The morphological structure of wheat spikes plays a central role in wheat yield. Wheat spike morphology, closely associated with crop yield, has attracted considerable attention in the fields of genetics and breeding. However, traditional measurement methods can only measure simple traits, and precise phenotypes remain difficult to obtain, constraining the study and improvement of complex spike-related traits. This study utilized deep learning technologies to develop a pipeline, called SpikePheno, for the acquisition of precise wheat spike phenotypes. Our pipeline demonstrated high accuracy in spike segmentation, achieving a mean Intersection over Union (mIoU) of 0.948. Additionally, our method accurately identified spikelet counts, achieving an R2 of 0.9923. Using experimental data of 221 wheat cultivars from various regions of China grown in Zhao County, Hebei Province, our pipeline extracted 45 different phenotypes and studied their correlations with thousand grain weight (TGW) and spike yield. Our findings indicate that precise measurement of spike area, spikelet area, and other phenotypic traits enables a clearer understanding of the correlation between spike morphology and wheat yield. Through hierarchical clustering based on spike morphology, we categorized wheat spikes into six classes and identified phenotypic differences between these classes and their impact on TGW and yield. Furthermore, this study revealed phenotypic differences between wheat cultivars from different geographical regions and over different decades, with an increase in large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, providing an important basis for future wheat breeding efforts.

plant biology↗

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGenes architecture integrates over 600 specialized tools through a Model Context Protocol (MCP), enabling it to reason across diverse data modalities including genomics, protein networks, pharmacology, clinical records and literature evidence, to generate and prioritize target discovery hypotheses. We implemented a strategy combining a knowledge graph-based Tool RAG with an advanced agent selection mechanism to enable dynamic, context-aware tool deployment. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, an original benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor activity in patient-derived organoid and tumor fragment models mirroring human clinical exposures. These findings demonstrate OriGenes potential as a scalable and adaptive platform for AI-driven discovery of mechanistically grounded therapeutic targets, offering a new paradigm to accelerate drug development.

bioinformatics↗

Lineage-Traced PTBP1 Depletion in Mature Astrocytes Reveals Distinct Splicing Alterations Without Neuronal Features

Astrocyte-to-neuron reprogramming via depletion of PTBP1, a potent repressor of neuronal splicing, has been proposed as a therapeutic strategy, but its efficacy remains debated. While some reported successful conversion, others disputed this, citing a lack of neuronal gene expression as evidence of failed reprogramming. This interpretation was further challenged, attributed to incomplete PTBP1 inactivation, fueling ongoing controversy. Mechanistic understanding of the conversion, or the lack thereof, requires investigating, in conjunction with lineage tracing, the effect of Ptbp1 loss of function in mature astrocytes on RNA splicing, which has not yet been examined. Here, we genetically ablated PTBP1 in adult Aldh1l1-Cre/ERT2 Ai14 mice to determine whether lineage traced Ptbp1 knockout astrocytes exhibited RNA splicing alterations congruent with neuronal differentiation. We found no widespread induction of neurons, despite a minuscule fraction of knockout cells showing neuron-like transcriptomic signatures. Importantly, PTBP1 loss in mature astrocytes induced splicing alterations unlike neuronal splicing patterns. These findings suggest that targeting PTBP1 alone is ineffective to drive neuronal reprogramming and highlight the need for combining splicing and lineage analyses. Loss of astrocytic PTBP1 is insufficient to induce neuronal splicing, contrasting with its well-known role in other non-neuronal cells, and instead affects a distinct astrocytic splicing program.

neuroscience↗

FoldBench: An All-atom Benchmark for Biomolecular Structure Prediction

Accurate prediction of biomolecular complex structures is fundamental for understanding biological processes and rational therapeutic design. Recent advances in deep learning methods, particularly all-atom structure prediction models, have significantly expanded their capabilities to include diverse biomolecular entities, such as proteins, nucleic acids, ligands, and ions. However, comprehensive benchmarks covering multiple interaction types and molecular diversity remain scarce, limiting fair and rigorous assessment of model performance and generalizability. To address this gap, we introduce FoldBench, an extensive benchmark dataset consisting of 1,522 biological assemblies categorized into nine distinct prediction tasks. Our evaluations reveal critical performance dependencies, showing that ligand docking accuracy notably diminishes as ligand similarity to the training set decreases, a pattern similarly observed in protein-protein interaction modeling. Furthermore, antibody-antigen predictions remain particularly challenging, with current methods exhibiting failure rates exceeding 50%. Among evaluated models, AlphaFold 3 consistently demonstrates superior accuracy across the majority of tasks. In summary, our results highlight significant advancements yet reveal persistent limitations within the field, providing crucial insights and benchmarks to inform future model development and refinement.

bioinformatics↗

Constructing a consensus serum metabolome

Blood analysis is the most common in biomedical applications and a reference metabolome will be critical for effective annotation and for guiding scientific investigations. However, compiling such a reference is hindered by many technical challenges, despite the availability of large amount of metabolomics data today. Based on a new set of data structures and tools, we have assembled a consensus serum metabolome (CSM) from over 100,000 mass spectrometry acquisitions of more than 200 million spectra. This provides a comprehensive survey of human blood chemistry, revealing the frequency dependent nature of metabolome and exposome. Major gaps are found between CSM and the current databases. The CSM enables community-level data alignment and significantly improves annotation quality of LC-MS metabolomics. HighlightsO_LIA reference of human biochemistry linked to observation frequency C_LIO_LIMajor gaps revealed in current databases and experimental methods C_LIO_LIEnabling cross-laboratory, cross-platform data alignment C_LIO_LIAccelerated and cumulative metabolite annotation C_LI

biochemistry↗

Allosteric modulation of dimeric GPR3 by ligands in the dimerization interface

Class C G-protein coupled receptors (GPCRs) are widely recognized to function as a dimer, whereas the existence, molecular mechanisms and functional importance of dimerization in class A GPCRs remain poorly explored. GPR3, GPR6 and GPR12 are closely related to cannabinoid receptors, and are associated with neurogenerative diseases such as Parkinsons and Alzheimers diseases. Here, we determined cryo-EM structures of GPR3-Gs complex in the active state and GPR3 alone in the inactive state. We observe that GPR3 forms symmetric dimers in both states, with the dimerization interface primarily involving TM5 and TM6. Upon activation, GPR3 undergoes an outward movement of TM6 and an inward movement of TM7, alongside stabilization of helix 8, which is disordered in the inactive state. Functionally, GPR3 dimerization attenuates Gs signaling. Furthermore, we identify AF64394 as a negative allosteric modulator that targets the GPR3 dimerization interface and allosterically shifts the -helical structure of ICL2 into a random coil structure, thereby impairing Gs coupling. Our findings provide structural and mechanistic insights into class A GPCR dimerization, offering potential therapeutic strategies targeting the dimerization interface of these receptors.

biochemistry↗