Biology subjects

Qiao, N.

Publications and source records attributed to Qiao, N..

10 recordsLinked to original sources

bioRxiv2026

2-arm-PEG-oligocations transiently shield the liver sinusoids to mitigate off-target hepatic expression of mRNA lipid nanoparticles

Ionizable lipid nanoparticles (iLNPs) are powerful platforms for mRNA-based vaccines and immunotherapies; however, their intrinsic liver tropism compromises both safety and efficacy. Off-target hepatic protein expression from delivered mRNA raises safety concerns, and hepatic clearance limits efficient iLNP delivery to target organs. In this study, we address these challenges in mouse models by stealth-coating the liver sinusoidal endothelial (LSE) wall, the primary gateway for nanoparticle entry into the liver. Specifically, oligocations conjugated with two-armed PEG (2-arm-PEG-oligocations), a clinically relevant material used in oligonucleotide delivery trials, were employed to transiently anchor PEG to the LSE wall with balanced affinity, ensuring robust coating followed by gradual biliary clearance. This approach reduced hepatic protein expression from iLNPs, subsequently administered either systemically or locally, by more than tenfold. Importantly, the strategy preserved iLNP accumulation in the spleen, a key target organ for vaccines, effectively redirecting iLNPs from the liver to the spleen. Consequently, in vaccine applications, pre-injection of the 2-arm-PEG-oligocation preserved or even enhanced vaccination efficacy while minimizing concerns associated with antigen expression in the liver. In applications involving cytokine mRNA therapy, specifically intratumoral interleukin-12 (IL-12) mRNA administration, systemic pre-injection of the 2-arm-PEG-oligocation successfully reduced off-target hepatic IL-12 expression and subsequent systemic IL-12 exposure, while maintaining antitumor efficacy. Collectively, these results demonstrate that LSE-wall stealth coating is a generalizable strategy to improve both the safety and efficacy of iLNP-based mRNA vaccines and immunotherapies.

bioengineering↗

bioRxiv2025

Combinational adjuvants delivered by ink-jet potentiate naked mRNA vaccines for robust protection against infectious diseases

The use of lipid nanoparticles (LNPs) to enhance delivery and immunogenicity of mRNA vaccines raises safety concerns due to strong reactogenicity. Developing naked mRNA vaccines, which can be delivered through liquid jet-injection to the skin, may offer a safer alternative, but their immunogenicity has been limited. To address this challenge, we screened clinically relevant adjuvants to enhance the potency of naked mRNA jet-injection vaccines in mouse models. Although many adjuvants showed little or inhibitory effects on inducing humoral or cellular immunity--despite their known ability to potentiate conventional non-mRNA vaccine platforms--we identified a combination of aluminum phosphate and CpG oligonucleotide that substantially enhanced both humoral and cellular immune responses with increased IgG2a/IgG1 ratio. This adjuvanted naked mRNA vaccine induced IgG2a and cellular immunity at levels comparable to those achieved with LNPs but with lower local and systemic reactogenicity. Mechanistic investigations revealed a key role for type I interferon signaling during immunization. Notably, this vaccine formulation in mice virtually eliminated infection by both SARS-CoV-2 and influenza A virus (A/California/7/2009 H1N1) in plaque-forming assays. Collectively, the adjuvanted naked mRNA jet-injection vaccine represents a novel, effective, and safe platform against infectious diseases, with potential for clinic-ready translational applications.

bioengineering↗

bioRxiv2025

Divergent stem cell mechanisms governing the primary body axis and appendage regeneration in the axolotl

Exploring the fundamental mechanisms of organ regeneration is crucial for advancing regenerative medicine. The axolotl tail represents a unique opportunity to study regeneration of the primary axis including segmented muscle, vertebrae and skin. During tail development, muscle stem cells (MuSCs) displayed expected specificity to the muscle lineage. Tail amputation, however, induced expansion of MuSC potential yielding clonal contribution to muscle, connective tissue including cartilage, pericytes, and fibroblasts. This expanded potential was not observed during limb regeneration, and cross-transplantation showed these differences in potential are intrinsic. ScRNA-Seq profiling revealed that tail MuSCs revert to an embryonic mesoderm-like state. Through genetic manipulation involving the over-expression of constitutively active TGF-{beta} receptors or Smad7 (antagonist of TGF-{beta} signaling) in MuSCs, we demonstrated that the levels of TGF-{beta} signal determine the fate outcome of MuSCs to connective tissue lineage or muscle respectively. Our findings illustrate a fundamental difference between regeneration of primary axis versus limb and offers a novel stem cell source for regeneration of axial skeletal tissues.

developmental biology↗

bioRxiv2024

Causal Associations of Circulating Helicobacter pylori Antibodies with 5 Skin Disorders: A Mendelian Randomization Analysis

Helicobacter pylori (H. pylori) infection, a prevalent bacterial ailment affecting the stomach, has been linked to diverse gastrointestinal disorders. Nevertheless, the correlation between H. pylori infection and skin disorders has not been definitively established. We use Mendelian randomization analysis to explore causal relationships with seven pylori antibody levels and five skin diseases, such as rosacea, psoriasis, alopecia areata, pruritus and urticaria. The primary analytical method employed was inverse variance weighted (IVW), and a sensitivity analysis was executed to evaluate the robustness of the primary findings. The results revealed a positive causal relationship between H. pylori catalase antibody levels and AA (IVW: OR = 1.32, 95% CI = 1.07 to 1.61, P = 0.008), as well as a positive causal relationship between H. pylori UreA antibody levels and pruritus (IVW: OR = 1.17, 95% CI = 1.05 to 1.31, P = 0.006). These effects were robust to sensitivity analyses. Our results suggested that H. pylori infection was a risk factor for AA and pruritus. This finding may have implications for the prevention and treatment of these skin disorders.

genomics↗

bioRxiv2022

PanGu Drug Model: Learn a Molecule Like a Human

Recent achievements in large-scale pre-trained models like GPT-3 and PanGu- showed amazing performances in many downstream tasks, which makes AI friendlier toward industrial users. Deep learning has been recognized as the most promising technology for pharmaceuticals, a powerful molecule pre-trained model could save researchers tons of time. In chemistry classes, the students learn two molecule representations, the molecular formula and the structure formula, and learn to translate them from one way to the other. Inspired by this, we developed a novel deep learning architecture using a graph-to-sequence asymmetric conditional variational autoencoders, called PanGu Drug Model, which can appropriately characterize the molecule from both representations and improve the performance of downstream drug discovery tasks. After pretrained with 1.7 billion small molecules, our proposed model achieved the state-of-the-art results in 20 drug discovery tasks, such as molecule property prediction (predict ADMET properties, compound-target interactions, drug-drug interactions and chemical reaction productivity), molecule generation and molecule optimization. A new drug screening library of 100 million drug-like small molecules with 99.68% novelty was generated by PanGu molecule generator, which could efficiently produce novel compounds with similar physiochemical properties to given distribution, this library could be used to supplement existing compound databases. In addition, PanGu molecule optimizer could optimize the chemical structures of starting molecule with improved molecular property of interest. An automatic multi-objective optimization web application implemented by PanGu Drug Model is available at http://www.pangu-drug.com/.

biochemistry↗

bioRxiv2021

AutoGGN: A Gene Graph Network AutoML tool for Multi-Omics Research

Omics data identifies biological characteristics from genetic to phenotypic levels during the life span. Molecular interaction networks have a fundamental impact on life activities. Integrating omics data and molecular interaction networks will help researchers delve into comprehensive information underlying the data. Here, we proposed a new multimodal method called AutoGGN to aggregate multi-omics data and molecular interaction networks based on graph convolutional neural networks. We evaluated AutoGGN using two different tasks: cancer type classification and single-cell stage classification. On both tasks, AutoGGN showed better performance compared to other methods, the trend is relevant to the ability of utilizing much more information from biological data. The phenomenon indicated AutoGGN has the potential to incorporate valuable information from molecular interaction networks and multi-omics data effectively. Furthermore, in order to provide a better understanding of the mechanism of prediction results, we assessed the explanation using SHAP module and identified the key genes contributing to the prediction of classification, which will provide insights for the downstream design of biological experiments.

bioinformatics↗

bioRxiv2020

Fast-Bonito: A Faster Basecaller for Nanopore Sequencing

Oxford Nanopore Technologies (ONT) is a promising sequencing technology that could generate relatively longer sequencing reads compared to the next generation sequencing (NGS) technology. The base calling process is very important for TGS. It translates the original electrical signals from the sequencer to the nucleotide sequence. By doing that, the base calling could significantly influence the accuracy of downstream analysis. Bonito is a recently developed basecaller based on deep neuron network, the neuron network architecture of which is composed of a single convolutional layer followed by three stacked bidirectional GRU layers. Although Bonito achieved the state-of-the-art accuracy, its speed is so slow that it is not likely to be used in production. We therefore implement Fast-Bonito, which introduces systematic optimization to speed up Bonito. Fast-Bonito archives 53.8% faster than the original version on NVIDIA V100 and could be further speed up by HUAWEI Ascend 910 NPU, achieving 565% faster than the original version. The accuracy of Fast-Bonito is also slightly higher than the original Bonito.

bioinformatics↗

bioRxiv2020

Regeneration of Functional Retinal Ganglion Cells by Neuronal Identity Reprogramming

Degeneration of retinal ganglion cells (RGCs) and their axons underlies vision loss in glaucoma and various optic neuropathies. There are currently no treatments available to restore lost vision in patients affected by these diseases. Regenerating RGCs and reconnecting the retina to the brain represent an ideal therapeutic strategy; however, mammals do not have a reservoir of retinal stem/progenitor cells poised to produce new neurons in adulthood. Here, we regenerated RGCs in adult mice by direct lineage reprogramming of retinal interneurons. We successfully converted amacrine and displaced amacrine interneurons into RGCs, and observed that regenerated RGCs projected axons into brain retinorecipient areas. They convey visual information to the brain in response to visual stimulation, and are able to transmit electrical signals to postsynaptic neurons, in both normal animals and in a diseased model. The generation of functional RGCs in adult mammals points to a therapeutic strategy for vision restoration in patients.

neuroscience↗

bioRxiv2020

AutoOmics: An AutoML Tool for Multi-Omics Research

Deep learning is very promising in solving problems in omics research, such as genomics, epigenomics, proteomics, and metabolics. The design of neural network architecture is very important in modeling omics data against different scientific problems. Residual fully-connected neural network (RFCN) was proposed to provide better neural network architectures for modeling omics data. The next challenge for omics research is how to integrate informations from different omics data using deep learning, so that information from different molecular system levels could be combined to predict the target. In this paper, we present a novel multimodal approach that could efficiently integrate information from different omics data and achieve better accuracy than previous approaches. We evaluate our method in four different tasks: drug repositioning, target gene prediction, breast cancer subtyping and cancer type prediction, and all the four tasks achieved state of art performances. The multimodal approach is implemented in AutoGenome V2 and is also powered with all the previous AutoML convenience to facilitate biomedical researchers.

bioinformatics↗

bioRxiv2019

AutoGenome: An AutoML Tool for Genomic Research

Deep learning have made great successes in traditional fields like computer vision (CV), natural language processing (NLP) and speech processing. Those achievements greatly inspire researchers in genomic study and make deep learning in genomics a very hot topic. Convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used for genomic sequence prediction problems; multiple layer perception (MLP) and auto-encoders (AE) are frequently used for genomic profiling data like RNA expression data and gene mutation data. Here, we introduce a new neural network architecture, named residual fully-connected neural network (RFCN) and demonstrate its advantage for modeling genomic profiling data. We further incorporate AutoML algorithms and implement AutoGenome, an end-to-end automated genomic deep learning framework. By utilizing the proposed RFCN architectures, automatic hyper-parameter search and neural architecture search algorithms, AutoGenome can train high-performance deep learning models for various kinds of genomic profiling data automatically. To make researchers better understand the trained models, AutoGenome can assess the feature importance and export the most important features for supervised learning tasks, and the representative latent vectors for unsupervised learning tasks. We envision AutoGenome to become a popular tool in genomic studies.

genomics↗