Biology subjects

Shi, J.

Publications and source records attributed to Shi, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

A mobile circular DNA element drives memory-related processes in mice

Extrachromosomal circular DNAs (ecDNAs) are double stranded, closed loop DNA molecules that act independently of the genome. Here we report the discovery of a brain-enriched ecDNA, ecCldn34d, that regulates the transcriptional state of plasticity-related genes and is temporally associated with the stability of fear memories. ecDNA therefore represents a novel class of mobile DNA elements that are involved in experience-dependent neuroadaptation.

neuroscience↗

bioRxiv2025

Local translation of circular RNAs is required for synaptic activity and memory

Circular RNAs (circRNAs) are enriched in synapses and implicated in cognitive processes, and recent studies have shown that circRNAs can encode micropeptides, which suggests that there may be novel synaptic proteoforms in the brain that await discovery. Here we report widespread learning-induced local circRNA translation in the prefrontal cortex of male C57BL/6 mice. More than 1500 synapse-enriched circRNAs contain active IRES elements, with 842 interacting with the ribosome and 241 exhibiting direct evidence of activity-induced translation. We discovered a synapse-enriched micropeptide (P1) that is derived from a single exon circRNA, the mRNA host of which encodes an enzyme associated with protein repair. Although P1 is only a third of the size of the full-length protein, it is locally expressed, enzymatically active, and interacts with plasticity-related proteins, including CaMKII. In addition to direct effects on synaptic activity, targeted P1 knockdown impairs whereas its overexpression enhances fear extinction memory. These findings shed new light on the dark proteome in the brain and reveal local circRNA translation as a novel mechanism of plasticity and memory.

neuroscience↗

bioRxiv2025

A Generative Foundation Model for Antibody Design

Antibodies are indispensable components of the immune system, yet the design of high-affinity antibodies remains a time-consuming and experimentally intensive process. To address this challenge, we present IgGM, a novel generative foundation model designed to accelerate high-affinity antibody engineering. IgGM learns the complex relationships underlying the binding interactions between antigens and antibodies, as well as the mapping between antibody sequences and structures. By conditioning on different inputs, IgGM supports a wide range of antibody design tasks, including complex structure prediction, inverse design, affinity maturation, framework optimization, humanization, and de novo antibody design. It is compatible with both conventional antibodies and nanobodies, and allows user-defined CDR loop lengths for flexible design. To prioritize candidates, we introduce a frequency-based computational screening strategy that enhances design efficiency. Extensive evaluation through both in silico benchmarks and in vitro experiments across diverse antigens such as PD-L1, Protein A, TNF-, IL-33, SARS-CoV-2 RBD and its variants demonstrates that IgGM consistently generates antibodies or nanobodies with high measured affinity. These results underscore IgGMs versatility and effectiveness as a powerful tool for next-generation antibody discovery and optimization.

bioinformatics↗

bioRxiv2025

Total variation of quantitative phase map reveals cellular Young's modulus

Collective cell migration is a fundamental physiological process involved in wound healing, development, and tissue regeneration. Though the role of the environmental stiffness in collective cell migration has been extensively investigated, the effects of cellular stiffness have been less studied due to the lack of high-throughput in situ methods for characterizing cellular stiffness. Here, we characterize the cellular Youngs modulus in situ with a large field of view and in real time using use quantitative phase microscopy. We found that standard deviation and total variation of the phase are inversely related to the cellular Youngs modulus, while the total variation of phase has finer mechanical resolution. Integrating the total variation of phase with cell segmentation algorithms, we efficiently analyzed the cellular Youngs modulus for cells in a monolayer. Using this system, we found the cells at the wound frontier are much softer than that of the cells in the inner region of monolayer in in vitro wound healing assay. At single cellular level, the Youngs modulus of leader cells, boundary cells, and inner cells are significantly distinct from each other. To conclude, our method would help elucidate the essential role of cellular stiffness in collective cell migration and has the potential to further benefit the progress of mechanobiology.

biophysics↗

bioRxiv2025

The host protein cyclophilin A inhibits HIV-1 nuclear entry by decreasing capsid elasticity

Binding of the host protein cyclophilin A (CypA) to the HIV-1 capsid exerts a variety of effects on infection, including enhancement of reverse transcription, stabilization of the capsid, and promotion of nuclear entry. For several HIV-1 mutants, CypA binding inhibits nuclear entry by an unknown mechanism. We recently demonstrated that HIV-1 cores are elastic and that HIV-1 mutants with inelastic capsids are impaired for nuclear entry and infection of nondividing cells. Here we show that CypA prevents infection of nondividing cells by such mutants and inhibits their entry into the nucleus. CypA binding to mutant cores further reduced their elasticity in vitro, and this effect was reversed by suppressor mutations that restored nuclear entry. We suggest that HIV-1 nuclear entry involves temporal modulation of capsid elasticity by host proteins prior to and during traversal of the nuclear pore.

microbiology↗

bioRxiv2025

Structural and mechanistic insights into bacterial hydrazine biosynthesis

Nitrogen-nitrogen (N-N) bond-containing motifs are prevalent in both clinical drugs and natural products. Bacterial hydrazine synthetases catalyze N-N bond formation by coupling an amino acid and a hydroxylamine via a distinctive O-aminoacyl-hydroxylamine intermediate. Despite its wide occurrence, the structural and mechanistic basis of this process has remained elusive. Here, we report the first crystal structures of the O-aminoacyl-hydroxylamine synthetase component of a bacterial hydrazine synthetase, captured in binary and ternary complexes with substrates and catalytic intermediates. These structures reveal the molecular determinants of substrate recognition and, together with biochemical and computational analyses, establish a detailed mechanistic framework for the hydrazine synthetase family. Leveraging these insights, we expanded hydrazine biosynthesis through the targeted discovery of novel hydrazine synthetases and implemented a chemoenzymatic synthesis strategy. This work provides fundamental insights into hydrazine synthetases and lays the groundwork for their rational engineering as versatile biocatalysts.

microbiology↗

bioRxiv2025

Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations

The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wls membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt-Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine extended atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein- protein interactions and guiding experimental validation or therapeutic targeting.

biochemistry↗

bioRxiv2025

Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI. Key architectural modifications included a simplified two-layer residual block without bottleneck layers, selective shortcut connections, and a sigmoid-based classification head for independent prediction of each alteration, designed to reduce computational complexity while maintaining predictive accuracy. The model was trained and evaluated on 495 WSIs from the Sherlock-Lung study (70% training with 10% internal test set for 10-fold cross-validation, and 30% held-out validation set for final evaluation). For the held-out validation data, our model achieved high areas under the receiver operating characteristic curve [AUROC] values =0.84-0.93 for detecting 11 features: EGFR, KRAS, TP53, RBM10 mutations, MDM2 amplification, kataegis, CDKN2A deletion, ALK fusion, whole-genome doubling, and EGFR hotspot mutations (p.L858R and p.E746_A750del). Performance was low to moderate for tumor mutational burden (AUROC=0.67), APOBEC mutational signature (AUROC=0.57), and KRAS hotspot mutations (p.G12C: AUROC=0.74, p.G12V: AUROC=0.55, p.G12D: AUROC=0.43). Compared to results from established architectures such as Inception-v3 on the same WSIs, our model demonstrated significantly improved performance for most features. With further optimization, our model could support triaging for molecular testing and inform precision treatment strategies for NS-LUAD patients.

pathology↗

bioRxiv2025

CDK4 Restricts Triple-Negative Breast Cancer Cell Migration via Phosphorylation-Driven Activation of Myo9b RhoGAP Function

Cyclin-Dependent Kinase 4 (CDK4) is a key regulator of cell cycle progression, driving the G0/G1-to-S phase transition through phosphorylation of Retinoblastoma 1 (RB1). Clinically, CDK4/6 inhibitors are under investigation in Triple Negative Breast Cancer (TNBC), a subtype characterized by invasiveness, aggressiveness and poor prognosis. While CDK4 is primarily targeted for its role in proliferation, emerging evidence suggests it may also regulate other cellular processes. In particular, the mechanisms by which CDK4 could influence cancer cell migration, remain largely unexplored, particularly in highly heterogenous cell line like MDA-MB-231. This study investigates whether CDK4 contributes to the regulation of TNBC cells migration and identifies the pathways involved in MDA-MB-231 cells, independently of its role in proliferation. We demonstrate that loss or inhibition of CDK4, using respectively CRISPR/Cas9 mediated CDK4 knockout and pharmacological CDK4/6 inhibitor, leads to enhanced migration capacities and reorganization of actin subcellular networks. Mechanistically, the absence of CDK4 results in decreased phosphorylation of Myo9b at serine 1935 (S1935), which enhances RhoA signaling, a key driver of cytoskeletal dynamics, leading to polarity defects and increased cell migration. These findings reveal a non-canonical function of CDK4 in limiting TNBC cell migration through the CDK4/CyclinD-Myo9b-RhoA signaling axis. This work highlights the broader cellular roles of CDK4 beyond its established function in proliferation and suggest that inhibition of Myo9b-RhoA pathway could reduce metastatic behaviour in TNBC treated with CDK4/6i, thereby informing future co-therapeutic strategies against aggressive cancer subtypes.

cell biology↗

bioRxiv2025

StCEL-based platform enables detection and sequencing of native sialoglycoRNA

Glycosylated RNAs (glycoRNAs) are an emerging class of biomolecules characterized by glycans attached to RNA molecules. Currently, glycoRNA detection remains largely confined to cellular models, substantially impeding its functional characterization and clinical translation. Here, we developed sialyltransferase-mediated chemical enzymatic labeling (StCEL), in which azido-modified sialic acid was transferred to the natural sialic acid on sialoglycoRNA, enabling the detection of native sialoglycoRNA across diverse biological samples with high specificity and sensitivity. Using StCEL, we found that sialoglycoRNA was significantly upregulated in multiple cancer types. StCEL combined with a microplate reader was applied to detect sialoglycoRNA in large-scale samples, revealing that elevated sialoglycoRNA levels correlate positively with tumor malignancy and poor patient prognosis. Furthermore, StCEL-based sialoglycoRNA enrichment and sequencing identified dysregulated sialoglycoRNAs in tumor tissues. Collectively, StCEL establishes a multifunctional platform that enables sensitive and specific detection of sialoglycoRNA, alongside sequencing analysis, demonstrating significant potential for clinical translation.

molecular biology↗

bioRxiv2025

MOH: a novel multilayer multi-omics heterogeneous graph for single-cell clustering

Cell clustering is crucial in single-cell multi-omics research for identifying distinct cellular populations. Although there has been progress in integrating multi-omics data for clustering, combining more than two types of omics data remains challenging due to the diversity and heterogeneity of these datasets. Traditional approaches typically use heterogeneous graphs that integrate only two types of omics data, constructing graphs with genes and cells as nodes and a single type of edge representing their relationships. However, this method has limitations as it overlooks cell-cell interactions and struggles to capture complex cellular dynamics. Additionally, the graph structure must be redesigned whenever new omics data are introduced, limiting the scalability of these models. To address these issues, we introduce MOH, a novel single-cell clustering algorithm based on a multilayer multi-omics heterogeneous graph. MOH integrates three key single-cell omics types: scRNA-seq, scATAC-seq, and spatial transcriptomics. It constructs a multilayer heterogeneous graph to simultaneously extract and enhance representations from all three omics layers, incorporating both intra-layer and inter-layer edges to capture association and similarity relationships. This enriched representation leads to an accurate clustering results. Extensive experiments show that MOH outperforms six state-of-the-art methods on unsupervised clustering metrics, offering a precise and comprehensive analysis with consistent improvements across all evaluation criteria. Moreover, downstream analyses validate the results, revealing novel biological insights into immune disorder complications in cancer, cancer drug repurposing, and new signaling pathways, which merit further investigation and validation.

bioinformatics↗

bioRxiv2025

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal cells such as cancer. A key player in the immunologic recognition of non-self is the immune synapse, where T cell receptors (TCRs) scan peptides presented on major histocompatibility complex (MHC) molecules to detect and eliminate cells displaying non-self antigens. While this interaction is vital for vaccine and immunotherapy success, the underlying rules of TCR recognition remain poorly understood. This is only further challenged as the application of predictive models is very limited due to small training datasets. While traditional machine learning models excel at predicting neoantigen binding to MHC, they often struggle to accurately predict immunogenicity. To address these challenges, we developed a quantum computing approach using Quantum Convolutional Neural Networks (QCNNs). Here we present Quantum Convolutional Human Leukocyte Antigen (HLA) Immunogenic Peptide Prediction (Q-CHIPP), the first application of quantum hardware based on training/predicting both MHC binding and immunogenicity in a combinatorial approach. Additionally, we present a large scale use of quantum hardware at scale with 46 qubits. This study underscores how quantum technology can be used for biological modeling and presents a scalable QCNN design with the potential to overcome current computational bottlenecks as quantum hardware advances.

immunology↗

bioRxiv2025

Genome-Wide Investigation of Transcription Factor Occupancy and Dynamics Using cFOOT-seq

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences. Notably, cFOOT-seq, combined with FootTrack analysis, predicts TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.

molecular biology↗

bioRxiv2025

Deep-learning-assisted SICM for enhanced real-time imaging of nanoscale biological dynamics

Scanning Ion Conductance Microscopy (SICM) provides high-resolution, nanoscale imaging of living cells, but it is generally limited by a slow scan rate, making it challenging to capture dynamic processes in real time. To tackle this challenge, we propose an integrated data acquisition and computational framework that improves the temporal resolution of SICM by selectively skipping certain scan lines. A partial convolutional neural network (Partial-CNN) model is developed and trained on SICM images and their corresponding masks to reconstruct the complete images from the under-sampled data, ensuring the retention of structural integrity. This approach significantly reduces the image acquisition time (i.e., by 30-60%) without compromising quality, as validated through multiple quantitative metrics. Compared to conventional deep learning methods, the Partial-CNN demonstrates higher accuracy in reconstructing fine details and maintaining consistent height maps across skipped regions. We show that this method provides an increased temporal resolution and retains image fidelity, making it suitable for real-time dynamic SICM imaging and improving the smart scanning microscopy applications in time-resolved biological imaging. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/663539v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@562d32org.highwire.dtl.DTLVardef@1b12dcaorg.highwire.dtl.DTLVardef@13535f3org.highwire.dtl.DTLVardef@6904f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Lysosome-like structures in the nucleolus mediate H2Aub degradation under glucose starvation

Glucose starvation leads to the degradation of intracellular proteins to generate amino acids for energy production. However, it remains unclear which proteins undergo rapid degradation to supply amino acids as energy sources during glucose starvation. To investigate the molecular mechanism underlying the transition of carbon source from glucose to proteins under glucose starvation, we first employed quantitative proteomics to examine the impact of glucose deprivation on protein levels. We discovered that histone degradation produces the highest amount of glutamine during glucose starvation. Further investigation revealed that histone H2A undergo degradation in the nucleolus in the form of mono-ubiquitination (H2Aub). The nucleolus contains lysosome-like structures that degrade monoubiquitinated histones, potentially participating in the carbon source shift from glucose to amino acids for energy production. Through preliminary screening, we identified Midnolin as the receptor for H2Aub, mediating its degradation in nucleolar lysosome-like structures. Dysregulation of carbon source transition from glucose to proteins, through knockdown of Midnolin results in cell cycle arrest, affecting cellular growth and proliferation. In conclusion, our findings demonstrate that nucleolar lysosome-like structures degrading monoubiquitinated histones exist during glucose starvation, which may play crucial roles in the carbon source transition from glucose to proteins, providing new potential therapeutic targets for metabolic disorders.

cell biology↗

bioRxiv2025

Multi-season analysis reveals hundreds of drought-responsive genes in sorghum

Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghums remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1,500 transcriptome profiles, across a 3-year field study with replicated plots in Californias Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pan-gene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of drought-responsive genes that responded similarly in all three years of our field study. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

genomics↗

bioRxiv2025

Genome Mining and Characterization of a Heme-Dependent Enzyme Catalyzing Intermolecular Nitrogen-Nitrogen Bond Formation in Hydrazinosuccinic Acid Biosynthesis

Nitrogen-nitrogen (N-N) bond-forming enzymes are rare but play vital roles in both primary and secondary metabolism. Guided by a nitric oxide synthase (NOS)-based genome mining strategy, we report the discovery and characterization of a new heme-dependent enzyme system that catalyzes intermolecular N-N bond formation. Using both in vivo and in vitro reconstitution approaches, we demonstrated that a protein complex, comprising a heme enzyme and a 2[4Fe-4S] ferredoxin partner, mediates the coupling of the -amine group of L-aspartate with inorganic nitrogen oxide species, such as nitrite or nitric oxide, to generate hydrazinosuccinic acid, a key biosynthetic precursor in several natural product pathways. Structural modeling and site-directed mutagenesis suggest a plausible catalytic mechanism involving the formation of a reactive nitrogen intermediate, potentially a heme-bound nitrene species. These findings reveal a new family of N-N bond-forming biocatalysts that leverage inorganic nitrogen sources, offering valuable tools for genome mining and the synthetic biology.

biochemistry↗

bioRxiv2025

Human Neural Synergy when combining Stevia with a Flavor Modifer and the Neural effects of Sucrose vs Stevia

There is a drive to improve the acceptability of sweeteners like stevia by reducing their off-tastes. The main aim was to examine the synergistic neural effects of combining stevia with a flavor modifier and secondly to examine stevia vs. sucrose due to limited human neuroimaging data and concerns that sweeteners may be more addictive than sugar. In a within-subjects fMRI study, 34 healthy adults (Mean age = 25) tasted four conditions: stevia, stevia plus a flavor modifier, the modifier alone, and sucrose. We analyzed whole-brain responses and focused on regions of interest (ROIs) including the insula, postcentral gyrus, and hypothalamus (identified via meta-analysis of sweet taste processing), as well as the nucleus accumbens (NAcc) and amygdala due to their roles in reward and aversion. Stevia combined with the modifier evoked super-additive responses in the postcentral gyrus, parietal cortex, and occipital gyrus (p < 0.05, FWE-corrected). Compared to stevia alone, the stevia-modifier combination elicited reduced hypothalamic activity (p = 0.008) and the hypothalamus tracked pleasantness and mouth fullness only in this condition. The NAcc tracked mouth fullness more for the modified stevia than for stevia alone, and the amygdala tracked bitterness only in the plain stevia condition. Sucrose elicited higher postcentral gyrus activation than stevia (p = 0.01). We provide first evidence that combining stevia with a flavour modifier reveals synergistic neural activity associated with taste sensation, intensity and multisensory integration. Adding a modifier to stevia could increase unconscious desirability for stevia by masking its bitterness and increasing its mouth fullness.

neuroscience↗