Biology subjects

Liu, c.

Publications and source records attributed to Liu, c..

5 recordsLinked to original sources

bioRxiv2026

Thermal Nano-Engineering of Ginger Extracellular Vesicles for Targeted Oral Therapy of Colitis

Plant-derived extracellular vesicles (PEVs) are promising candidates for oral drug delivery, yet their clinical translation is hindered by limited targeting precision and inconsistent systemic absorption. While surface engineering can enhance tissue accumulation, strategies that preserve biocompatibility and enable scalable production remain limited. Here, we introduce a simple thermal processing approach, boiling, to structurally reconfigure ginger EVs into functionally enhanced, thermally reassembled nanoparticles (B-GEVs). The surface architecture of B-GEVs is enriched with key vesicle trafficking regulators, including V-type proton ATPase subunit G, ARF1, and {beta}-adaptin-like protein. This specific composition drives their tissue-specific accumulation in the intestine and liver and potentiates clathrin-dependent cellular uptake in intestinal cells by 8.57-fold. Beyond superior intrinsic anti-inflammatory activity through NLRP3 inflammasome suppression, B-GEVs function as an efficient oral delivery platform. When loaded with TNF- siRNA, they enable a synergistic therapy that simultaneously modulates upstream inflammation and silences key downstream mediators, showing potent efficacy in colitis. Our findings position boiling as a natural strategy for enhancing the bioactivity and targeted oral delivery potential of ginger-derived EVs.

bioengineering↗

bioRxiv2025

Qimai: a multi-agent framework for zero-shot DNA-protein interaction prediction

Accurate prediction of DNA-protein interactions, a fundamental task in genomics, is limited by the poor generalization of existing models to novel proteins not seen during training. To address this challenge, we introduce Qimai, a modular AI agent framework that integrates deep learning predictions with biological evidence using Large Language Model (LLM) as reasoning engine. Qimai combines direct motif evidence from the query protein, indirect motif evidence from its interactors, and quantitative prediction from a new transformer-based DPI model to produce explainable predictions with confidence scores. On a benchmark of 78 unseen proteins, Qimai consistently outperforms standalone deep learning models across all metrics, increasing the Area Under Curve of the Precision-Recall (AUC-PR), the Area Under Curve of the Receiver Operating Characteristic (AUC-ROC), and Matthews Correlation Coefficient (MCC) by 17.6%, 15.6%, and 244% respectively compared to the best standalone model. Ablation analyses reveal that this gain is driven by the LLMs ability to dynamically weigh diverse evidence, with indirect motif evidence of co-factors particularly critical for unseen proteins. Qimai establishes a generalizable and interpretable paradigm for integrating heterogeneous data in predictive genomics. This framework is accessible via the Qimai web portal (https://qimai.wanglab.ucsd.edu/).

bioinformatics↗

bioRxiv2024

Env from EIAV vaccine delicately regulates NLRP3 activation via attenuating NLRP3-NEK7 interaction

The current equine infectious anemia virus (EIAV) vaccine causes attenuation of the inflammatory response to an appropriate level, compared to that produced by virulent EIAV. However, how the EIAV vaccine finely regulates the inflammatory response remains unclear. Using a constructed NLRP3-IL-1{beta} screening system, viral proteins from two EIAV strains (the attenuated vaccine and its virulent mother strain) were examined separately. Firstly, EIAV-Env was screened to direct binding P2X7(R) with notable K+ efflux trans-cellularly. Secondly, EIAV-Env was found to bind NLRP3 and/or NEK7 to trigger aggregation of NLRP3-NEK7 to form NLRP3-NEK7 complex in cells. Comparison of the two strains, we observed a significant reduction on vaccine-Env-initiated NLRP3-NEK7 complex formation, with no difference in Env triggering P2X7(R)-mediated ion fluxes. Thirdly, reciprocally mutation on four stable varied amino acids between two strains produced an anticipated outcome on NLRP3-IL-1{beta}-axis activation. As the attenuated vaccine was shown evolved as a natural quasispecies of the virulent EIAV, its precise and adaptable regulation via spatial proximity-dependent intracellular activation might present a "win-win" virus-host adaption, offering an alternative strategy on HIV vaccine development. Author SummaryHere, we report that EIAV-Env mediates NLRP3 inflammasome activation through two distinct pathways. The first pathway involves a transcellular mechanism driven by K+ flux, which couples Env-P2X7 interaction. The second pathway entails direct intracellular binding between Env and NLRP3, promoting the assembly of NLRP3-NEK7 and subsequent inflammasome formation. Notably, we observed a marked difference in NLRP3 inflammasome activation between the vaccine and virulent strains, which was reflected in the extent of Env-mediated NLRP3-NEK7 aggregation. This study not only enhances our understanding of lentivirus-host immune interactions but also contributes to the broader discourse on virus evolution and host-induced inflammation.

immunology↗

bioRxiv2022

Predicting regional somatic mutation rates using DNA motifs

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-specific regions can accurately predict cancer types. (The code is available from https://github.com/Wang-lab-UCSD/SomaticMutation) Significance StatementThe relationship between DNA motifs and somatic mutation rates in cancers is not fully understood, especially at high resolution. Here we developed an interpretable neural network model to successfully predict somatic mutation rates using DNA motifs in 13 diverse cancers and identified the most informative motifs. Furthermore, we showed that the genomic regions with significant higher mutation rates than the predicted values can be used for cancer classification.

bioinformatics↗

bioRxiv2022

Systems-level identification of key transcription factors in immune cell specification

Transcription factors (TFs) are crucial for regulating cell differentiation during the development of the immune system. However, the key TFs for orchestrating the specification of distinct immune cells are not fully understood. Here, we integrated the transcriptomic and epigenomic measurements in 73 mouse and 61 human primary cell types, respectively, that span the immune cell differentiation pathways. We constructed the cell-type-specific transcriptional regulatory network and assessed the global importance of TFs based on the Taiji framework, which is a method we have previously developed that can infer the global impact of TFs using integrated transcriptomic and epigenetic data. Integrative analysis across cell types revealed putative driver TFs in cell lineage-specific differentiation in both mouse and human systems. We have also identified TF combinations that play important roles in specific developmental stages. Furthermore, we validated the functions of predicted novel TFs in murine CD8+ T cell differentiation and showed the importance of Elf1 and Prdm9 in the effector versus memory T cell fate specification and Kdm2b and Tet3 in promoting differentiation of CD8+ tissue resident memory (Trm) cells, validating the approach. Thus, we have developed a bioinformatic approach that provides a global picture of the regulatory mechanisms that govern cellular differentiation in the immune system and aids the discovery of novel mechanisms in cell fate decisions.

systems biology↗