Biology subjects

Jing, G.

Publications and source records attributed to Jing, G..

10 recordsLinked to original sources

bioRxiv2026

Extracellular DDR1 targeting remodels collagen-rich tumor stroma and local immunity

Collagen rich stroma can restrict immune cell access and sustain immune exclusion in solid tumors, but strategies that directly target extracellular matrix organizing receptors remain limited. Here, we develop cross reactive nanobody Fc proteins against extracellular discoidin domain receptor 1 (DDR1) and identify DDR1nb 3 Fc as a lead molecule with activity in immunocompetent mammary and pancreatic tumor models, including an orthotopic pancreatic model. Tumor control is accompanied by altered collagen architecture and remodeling of the intratumoral immune compartment rather than consistent direct tumor-cell growth inhibition. Single cell profiling with mouse level compositional and pseudobulk analyses reveals reproducible pathway level remodeling of tumor associated neutrophils, while Ly6G positive cell depletion significantly modifies the antitumor effect of DDR1nb 3 Fc. These findings identify extracellular DDR1 as an actionable regulator of collagen rich tumor stroma and support its targeting as a strategy to reshape the tumor microenvironment and promote tumor control.

immunology↗

bioRxiv2026

Mechanical confinement drives monocyte-to-macrophage differentiation

Cells in vivo experience mechanically diverse microenvironments in which physical confinement is a pervasive but poorly understood regulator of their behavior and fate. Whether and how mechanical confinement governs immune cell differentiation remains unknown. Here, we reveal that a mechanical cue -- long-term confinement is sufficient to drive monocyte-to-macrophage differentiation through a mechanoepigenetic pathway. In vivo, differentiating monocytes exhibited flattened nuclei in the liver capsule, indicative of confinement by surrounding stromal and parenchymal structures. Using a custom cell confiner to recapitulate this confined niche, we found that confinement induces macrophage-like protrusive architectures, enhances motility, and upregulates macrophage-associated genes in RAW264.7 and THP-1 monocyte-lineage cells. Notably, extending this paradigm to primary murine bone-marrow, human umbilical-cord, and tissue-derived hepatic-associated monocytes yielded similar outcomes, thus enhancing phagocytic capacity, directly demonstrating that mechanical confinement can program monocytes into macrophages. Mechanistically, we found that confinement activates KDM6B, leading to H3K27me3 demethylation, which derepresses macrophage-specific transcriptional programs. Pharmacological inhibition of KDM6B with GSK-J4 restored H3K27me3 and blocked macrophage differentiation both in vitro and in vivo. These findings define a KDM6B-H3K27me3 axis that links nuclear mechanics to transcriptional reprogramming, positioning mechanical confinement as a "super-enhancer-like" cue for engineer macrophage function in therapeutic and bioengineering contexts.

cell biology↗

bioRxiv2025

RamEx: An R package for high-throughput microbial ramanome analyses with accurate quality assessment

Microbial single-cell Raman spectroscopy (SCRS) has emerged as a powerful tool for label-free phenotyping, enabling rapid characterization of microbial diversity, metabolic states, and functional interactions within complex communities. However, high-throughput SCRS datasets often contain spectral anomalies from noise and fluorescence interference, which obscure microbial signatures and hinder accurate classification. Robust algorithms for outlier detection and microbial ramanome analysis remain underdeveloped. Here, we introduce RamEx, an R package specifically designed for high-throughput microbial ramanome analyses with robust quality control and phenotypic classification. At the core of RamEx is the Iterative Convolutional Outlier Detection (ICOD) algorithm, which dynamically detects spectral anomalies without requiring predefined thresholds. Benchmarking on both simulated and real microbial datasets--including pathogenic bacteria, probiotic strains, and yeast fermentation populations--demonstrated that ICOD achieves an F1 score of 0.97 on simulated datasets and 0.74 on real datasets, outperforming existing approaches by at least 19.8%. Beyond anomaly detection, RamEx provides a modular and scalable workflow for microbial phenotype differentiation, taxonomic marker identification, metabolic fingerprinting, and intra-population heterogeneity analysis. It integrates Raman-based species-specific biomarkers, enabling precise classification of microbial communities and facilitating functional trait mapping at the single-cell level. To support large-scale studies, RamEx incorporates C++ acceleration, GPU parallelization, and optimized memory management, enabling the rapid processing of over one million microbial spectra within an hour. By bridging the gap between high-throughput Raman-based microbial phenotyping and computational analysis, RamEx provides a comprehensive toolkit for exploring microbial ecology, metabolic interactions, and antibiotic susceptibility at the single-cell resolution. RamEx is freely available under the MIT license at https://github.com/qibebt-bioinfo/RamEx.

bioinformatics↗

bioRxiv2024

Dynamic cycles between idea generation and evaluation during creative storytelling

Many theories support the notion that creative thinking involves a dynamic transition between idea generation and evaluation, but there is limited evidence of how these two stages interact. The dual process model proposes that spontaneous thinking and deliberate thinking drive these stages and the transitions between them, but there is a debate over whether they operate in parallel or in sequence. To address these gaps, we conducted a functional magnetic resonance imaging (fMRI) study in 41 college students during a creative storytelling task. By analyzing the dynamic brain state, we link the brain states with idea generation and evaluation. The transition patterns between brain states provide evidence for dynamic circulation between idea generation and evaluation during creative storytelling. Using a deep learning approach, we demonstrate an alternating interaction between spontaneous and deliberate thinking, driving the idea generation and evaluation stages and the transitions between them. These findings deepen our understanding of the cognitive and neural mechanisms underlying creative thinking.

neuroscience↗

bioRxiv2023

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

Protein language models have shown remarkable success in learning biological information from protein sequences. However, most existing models are limited by either autoencoding or autoregressive pre-training objectives, which makes them struggle to handle protein understanding and generation tasks concurrently. We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework. Our key technical contribution is an exploration of the compatibility and the potential for joint optimization of the two types of objectives, which has led to a strategy for training xTrimoPGLM at an unprecedented scale of 100 billion parameters and 1 trillion training tokens. Our extensive experiments reveal that 1) xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. The model also facilitates an atomic-resolution view of protein structures, leading to an advanced 3D structural prediction model that surpasses existing language model-based tools. 2) xTrimoPGLM not only can generate de novo protein sequences following the principles of natural ones, but also can perform programmable generation after supervised fine-tuning (SFT) on curated sequences. These results highlight the substantial capability and versatility of xTrimoPGLM in understanding and generating protein sequences, contributing to the evolving landscape of foundation models in protein science. Trained weight for the xTrimoPGLM model, and downstream datasets are available at https://huggingface.co/proteinglm.

synthetic biology↗

bioRxiv2022

Microsecond pulse electrical stimulation modulates cell migration

Wound healing is a complicated process for maintaining skin integrity after injury, for which electrical stimulations (ES) are ascribed to promote wound healing by facilitating cell migration. Time-shortening of the stimulation treatment from current hours to minutes for efficient wound healing but free of cell damage in return, is however rather a challenge. Here, a novel mechanism of ultrashort pulse electric field (PEF), microsecond PEF at higher voltage, is proposed and realized to promote wound healing under a much short time (seconds) for the total treatment. We revealed that microsecond PEF regulated actin cytoskeleton reorganization and focal adhesion turnover, promoting fibroblasts migration in 2D cell cultures under the pulse stimulation. This accelerated fibroblast migration was accompanied by the mutual promotion with extracellular matrix (ECM) alignment in 3D microenvironments, which cooperatively benefit the eventual wound healing, and these findings were further confirmed by the enhanced skin wound healing in a classic mouse model. Additionally, we coined an actin- and collagen-dependent mechanism of microsecond PEF-mediated wound healing. The quantitative mechanism proposed here for our novel microsecond pulse electric filed (sPEF) methodology orients the new practical electric treatment in a wide range of biomedical applications, such as wound healing, regenerative medicine, and tissue engineering.

bioengineering↗

bioRxiv2020

Species-resolved sequencing of low-biomass microbiomes by 2bRAD-M

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based (e.g., 16S/18S-rRNA) or whole-metagenome sequencing (WMS) approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective metagenome-sequencing strategy which only sequences ~1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles for low-biomass and highly degraded samples. For mock communities, 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with (i) low biomass of merely 1 pg of total DNA, (ii) high host DNA contamination (99%), and (iii) severely fragmented DNA (50-bp) from degraded samples. Tests of 2bRAD-M on stool, skin and environment-surface samples deliver successful reconstruction of comprehensive, high-resolution microbial profiles with agreement across 16S-rRNA, WMS and existing literature. In addition, it enables microbial profiling in formalin-fixed paraffin-embedded (FFPE) cervical tissue samples which were recalcitrant to conventional approaches due to the low amount and heavy degradation of microbial DNA, and discriminated healthy tissue, pre-invasive cancer and invasive cancer via species-level microbial profiles with 91.1% accuracy. Therefore, 2bRAD-M greatly expands the reach of microbiome sequencing.

microbiology↗

bioRxiv2020

A scale-free, fully connected global transition network underlies known microbiome diversity

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here we propose as solution a search-based microbiome transition network. By traversing a composition-similarity based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiome at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes.

microbiology↗

bioRxiv2020

Longitudinal multi-omics along gingivitis development reveal a suboptimal-health gum state with periodontitis-like microbiome

Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis. However the mechanistic roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we integrated the longitudinal multi-omics data from plaque metagenome, metabolome and salivary cytokines in 40 adults who transit from naturally-occurring gingivitis (NG), to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites and salivary cytokines emerged as early as 24-72 hours after pause of oral hygiene, defining an asymptomatic sub-optimal health (SoH) stage. SoH also features a steep and synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum-inflammation mechanism by health-promoting microbial residents. Global, cross-cohort meta-analysis revealed a high Microbiome-based Periodontitis Index at SoH state, due to its convergent taxonomical and functional profiles towards those of periodontitis. In contrast, caries SoH features a microbial signature very distinct from caries. Thus SoH is a universal state of polymicrobial inflammations with disease-specific features, which is key to maintaining a disease-preventive plaque.

microbiology↗

bioRxiv2020

Dynamic Mechanical Cue Facilitate Collective Responses of Crowded Cell Population

Collective cell behavior is essential for tissue growth, development and function, e.g. heartbeat1, immune responses2 and cerebral consciousness3. In recent years, studies on population cells uncover that collective behavior emerges in both inter- and intra-cellular activities, e.g. synchronized signal cascade4, and collective migration5. As the movement and shape transition of cells within the crowded environment of biological tissue can generate mechanical cues at the cell-cell interface, which may affect the signaling cascade6,7, we suspect that the inter- and intra-cellular collective behavior interplay with one another and cooperatively regulate life machinery. To verify our hypothesis, we study the collective responses of fibroblasts in a confluent cell monolayer (CCM). Our results demonstrate that cells in CCM show distinctive behavior as compared to the stand-alone (SA) cells, suggesting effect of inter-cellular interactions. Upon periodic TNF- stimulation, collective behavior emerges simultaneously in NF-{kappa}B signaling cascade and nuclear shape fluctuations in CCM but not SA cells. We then model the inter-cellular interactions in CCM using a customized microfluidic device, and discover a feedback loop intrinsic to CCM, in which dynamic mechanical cues and mechano-signaling act as link connecting the inter- and intra-cellular collective activities. We found that mechano-signaling triggered by the dynamic mechanical cues causes collective nuclear shape fluctuation (NSF), which subsequently facilitates the collective behavior in NF-{kappa}B dynamics. Furthermore, our studies reveal that regardless of the input TNF- periodicity, cellular responses of single fibroblasts are elevated when the dynamic mechanical cues synergize with the chemical inputs, and inhibited when there is phase-mismatching. We, therefore, postulate that besides the biological significance of mechano-signaling in regulating collective cell responses, the induction of dynamic mechanical cues to human body may be a potential therapeutic approach, allowing us to regulate the action of single cells to achieve optimal tissue performance.

biophysics↗