Biology subjects

Niu, D.

Publications and source records attributed to Niu, D..

9 recordsLinked to original sources

bioRxiv2026

Spatiotemporal atlas resolves the mesenchymal-epithelial stem cell axis governing human hair follicle aging and pathology

How adult stem cell populations coordinate tissue regeneration across physiological and pathological states remains unclear. Hair follicles contain epithelial and mesenchymal stem cells that codetermine hair regeneration. Here, we integrate a multimodal spatiotemporal atlas with functional validation to define the mesenchymal-epithelial stem cell dynamics governing human hair follicle homeostasis, aging, and androgenetic alopecia. Aging is characterized by passive depletion of hDSC-derived regenerative cues, whereas AGA involves a shift of hDSC toward an inhibitory signaling hub that promotes HFSC dysfunction. We further pinpoint hDSC-secreted PTN as a key regenerative factor that promotes hair growth and counteracts androgen-induced growth suppression. This study provides a holistic framework of adult stem cell crosstalk across physiological and pathological states, offering a mechanistic roadmap to reinstate tissue regeneration.

cell biology↗

bioRxiv2024

Mesoscale chromatin confinement facilitates target search of pioneer transcription factors in live cells

Pioneer transcription factors (PTFs) possess the unique capability to access closed chromatin regions and initiate cell fate changes, yet the underlying mechanisms remain elusive. Here, we characterized the single-molecule dynamics of PTFs targeting chromatin in living cells, revealing a notable "confined target search" mechanism. PTFs like FOXA1, FOXA2, SOX2, OCT4 and KLF4 sampled chromatin more frequently than non-pioneer factor MYC, alternating between fast free diffusion in the nucleus and slower confined diffusion within mesoscale zones. Super-resolved microscopy showed closed chromatin organized as mesoscale nucleosome-dense domains, confining FOXA2 diffusion locally and enriching its binding. We pinpointed specific histone-interacting disordered regions, distinct from DNA-binding domain, crucial for confined target search kinetics and pioneer activity within closed chromatin. Fusion to other factors enhanced pioneer activity. Kinetic simulations suggested transient confinement could increase target association rate by shortening search time and binding repeatedly. Our findings illuminate how PTFs recognize and exploit closed chromatin organization to access targets, revealing a pivotal aspect of gene regulation.

molecular biology↗

bioRxiv2022

Contrasting roles of landscape compositions in shaping functional traits of arthropod community in subtropical vegetable fields

Agricultural intensification and land use transformation are among the main driving forces of the unprecedented decline of biodiversity and ecosystem services in croplands. Trait-based approaches provide a unique framework to detect the potential mechanisms of how this intensification affects biodiversity and alter ecosystem services. However, the potential relationship between arthropod traits and various types of habitats is still poorly understood, especially in subtropical vegetable agroecosystems. Here, we conducted a trait-based approach to evaluate the variable roles of different habitats on functional trait diversity and the structure of the arthropod community in brassica vegetable crops. Twenty-three conventional cruciferous vegetables fields were sampled over two years in three regions in Fujian, China. We found that the increasing proportion of non-brassica vegetable plantations and water bodies negatively affected the functional diversity of arthropods, whereas forest and grassland habitats showed a positive correlation, indicating habitat filtering for certain traits or trait combinations. This study demonstrates the importance of landscape composition as an ecological filter for vegetable arthropod community, and identifies how the proportion of different habitats selected for or against specific functional traits. Our findings support that increasing forest and grassland areas adjacent to vegetable fields can play a vital role in promoting the functional diversity of arthropod communities. Since the natural enemy assemblages supported by these habitats bear combinations of diverse traits adapted to disturbance, they have the potential to enhance pest suppression in the highly variable environment of vegetable crops.

ecology↗

bioRxiv2021

Construction of a potent pan-vaccine based on the evolutionary tendency of SARS-CoV-2 spike protein.

SARS-CoV-2 continued to spread globally along with different variants. Here, we systemically analyzed viral infectivity and immune-resistance of SARS-CoV-2 variants to explore the underlying rationale of viral mutagenesis. We found that the Beta variant harbors both high infectivity and strong immune resistance, while the Delta variant is the most infectious with only a mild immune-escape ability. Remarkably, the Omicron variant is even more immune-resistant than the Beta variant, but its infectivity increases only in Vero E6 cells implying a probable preference for the endocytic pathway. A comprehensive analysis revealed that SARS-CoV-2 spike protein evolved into distinct evolutionary paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance, resulting in a narrow spectrum of the current single-strain vaccine. In light of these findings and the phylogenetic analysis of 2674 SARS-CoV-2 S-protein sequences, we generated a consensus antigen (S6) taking the most frequent mutations as a pan-vaccine against heterogeneous variants. As compared to the ancestry SWT vaccine with significantly declined neutralizations to emerging variants, the S6 vaccine elicits broadly neutralizing antibodies and full protections to a wide range of variants. Our work highlights the importance and feasibility of a universal vaccine strategy to fight against antigen drift of SARS-CoV-2.

microbiology↗

bioRxiv2021

A Comparative Analysis of Novel Deep Learning Models and Ensemble Learning Models to Predict the Allergenicity of Food Allergens

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model - transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the AUC of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training cost is the highest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

bioinformatics↗

bioRxiv2021

Landscape composition mediates suppression of major pests by natural enemies in conventional cruciferous vegetables

BackgroundConservation biological control provides an environment-friendly approach to improve the efficacy of natural enemies. Although numerous studies have demonstrated the potential of semi-natural habitats in promoting biological control in organic or unsprayed agroecosystems, few studies were conducted in conventional agricultural fields. In this study, we investigated the effects of landscape composition on the major pests of cruciferous vegetables and on the assemblages of their natural enemies in southeastern China. ResultsHabitat diversity, particularly increasing grassland proportion in the landscape, had a positive impact in controlling both small-sized pests (aphids, leaf miners, thrips and flea beetles) and Plutella xylostella. This increasing proportion also promoted greater abundance and diversity of canopy-dwelling predators, more forests supported a higher diversity of airborne enemies (parasitoids and canopy-dwelling predators) as well as a higher abundance of ground-dwelling predators. A general increase in habitat diversity was beneficial to parasitoids and ground-dwelling predators. Additionally, the proportion of forest, grassland, and non-cruciferous vegetable area, as well as habitat diversity, affected the compositions of natural enemy communities. Moreover, inconsistent effects of non-cruciferous and grassland habitats were found between sampling regions for small-sized pests and canopy-dwelling predators. Moreover, the scale at which pests and natural enemies abundance and richness responded most to landscape composition varied with their feeding range and dispersal ability. ConclusionOur study provides evidence that increasing the amount of semi-natural habitats and habitat diversity can result in lower pest and higher natural enemy abundance in conventional cruciferous agroecosystems. Regional conditions and spatial scales also should be considered in designing the agricultural landscape mosaic.

ecology↗

bioRxiv2021

Spray-induced gene silencing for disease control is dependent on the efficiency of pathogen RNA uptake

Recent discoveries show that fungi can take up environmental RNA, which can then silence fungal genes through environmental RNA interference. This discovery prompted the development of Spray-Induced Gene Silencing (SIGS) for plant disease management. In this study, we aimed to determine the efficacy of SIGS across a variety of eukaryotic microbes. We first examined the efficiency of RNA uptake in multiple pathogenic and non-pathogenic fungi, and an oomycete pathogen. We observed efficient double-stranded RNA (dsRNA) uptake in the fungal plant pathogens Botrytis cinerea, Sclerotinia sclerotiorum, Rhizoctonia solani, Aspergillus niger, and Verticillium dahliae, but no uptake in Colletotrichum gloeosporioides, and weak uptake in a beneficial fungus, Trichoderma virens. For the oomycete plant pathogen, Phytophthora infestans, RNA uptake was limited, and varied across different cell types and developmental stages. Topical application of dsRNA targeting virulence-related genes in the pathogens with high RNA uptake efficiency significantly inhibited plant disease symptoms, whereas the application of dsRNA in pathogens with low RNA uptake efficiency did not suppress infection. Our results have revealed that dsRNA uptake efficiencies vary across eukaryotic microbe species and cell types. The success of SIGS for plant disease management can largely be determined by the pathogen RNA uptake efficiency.

microbiology↗

bioRxiv2020

Co-infection of influenza A virus enhances SARS-CoV-2 infectivity

The upcoming flu season in the northern hemisphere merging with the current COVID-19 pandemic raises a potentially severe threat to public health. Through experimental co-infection of IAV with either pseudotyped or SARS-CoV-2 live virus, we found that IAV pre-infection significantly promoted the infectivity of SARS-CoV-2 in a broad range of cell types. Remarkably, increased SARS-CoV-2 viral load and more severe lung damage were observed in mice co-infected with IAV in vivo. Moreover, such enhancement of SARS-CoV-2 infectivity was not seen with several other viruses probably due to a unique IAV segment as an inducer to elevate ACE2 expression. This study illustrates that IAV has a special nature to aggravate SARS-CoV-2 infection, and prevention of IAV is of great significance during the COVID-19 pandemic.

microbiology↗

bioRxiv2020

A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Strategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

bioinformatics↗