Biology subjects

Du, N.

Publications and source records attributed to Du, N..

8 recordsLinked to original sources

bioRxiv2026

Genetic canalization of nutrient resorption: evidence from a widespread grass under effective salt stress

Nutrient resorption is a key plant strategy for nutrient conservation, yet whether it responds plastically to non-nutrient stressors such as salinity remains unresolved. Using a common garden experiment with 110 genotypes of the cosmopolitan grass Phragmites australis, we first established that the imposed salinity treatment induced strong multi-level stress responses: above-ground biomass declined by >60%, sodium accumulated 3- to 5-fold across all leaf stages, and 484 metabolites showed significant differential accumulation, including canonical markers of osmotic and oxidative stress. Against this backdrop of confirmed stress, we found that nutrient resorption efficiency (NuRE) remained largely unaffected by salinity. Instead, NuRE was strongly correlated with phylogeographic lineage, ecotype, and latitude of origin, demonstrating evolutionary canalization rather than short-term acclimation. Element-specific regulatory patterns were also evident: while phosphorus resorption followed concentration-dependent control regardless of stress, nitrogen control was disrupted under salinity, and potassium resorption showed no such dependence. Our findings reveal that intraspecific variation in nutrient resorption is predominantly shaped by historical adaptation and geographic context, not by plasticity to salinity. This genetic canalization of a key functional trait implies that predictions of nutrient cycling under global change must account for the phylogeographic composition of plant populations.

ecology↗

bioRxiv2025

BayesCurveFit: Enhancing Curve Fitting in Drug Discovery with Bayesian Inference

Accurate curve fitting is central to quantifying dose-response relationships in drug discovery. However, commonly used regression-based methods are ill-suited for early prescreening assays, where measurements are sparse and experimental noise can dominate. Bayesian approaches have been developed previously, but they are rarely optimized for such data-limited, noise-prone conditions. We present BayesCurveFit, a Bayesian framework specifically designed for robust dose-response inference under data scarcity. The workflow integrates calibrated initialization, stochastic optimization, adaptive posterior sampling, and probabilistic mixture modeling within a unified Bayesian pipeline, enabling reliable parameter estimation and uncertainty quantification from few observations. By modeling residuals with a Gaussian-Laplace hybrid distribution, BayesCurveFit remains robust to outliers where ordinary least squares and conventional regression methods fail. Simulation studies and real screening benchmarks demonstrate that BayesCurveFit outperforms state-of-the-art regression-based methods in recovering true dose-response relationships under limited sampling. In addition, a Bayesian measure of significance - posterior error probability - offers interpretable probabilistic confidence for response classification. Together, these features establish a general and easy-to-use Bayesian framework for analyzing dose-response data in early screening experiments.

bioinformatics↗

bioRxiv2024

Predicting the Impact of Dialyzer Choice and Binder Dialysate Flow Rate on Bilirubin Removal

Liver failure is the 12th leading cause of death worldwide. Protein bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin dialysate and detoxifying sorbent columns to remove these toxins. Systems like the Molecular Adsorbent Recirculating System (MARS) and BioLogic-DT have existed since the 1990s, but survival benefit in randomized controlled trials have not been consistent. Thus, a new generation of binder dialysis systems, including Open Albumin Dialysis (OPAL) and the Advanced Multi-Organ Replacement System (AMOR) are being developed. Optimal conditions for binder dialysis have not been established. We developed and validated a computational model of bound solute dialysis using established thermodynamic theories. Our objective is to improve AMOR therapy. We confirmed our models validity by predicting the impact of changing between two benchtop dialysis setups using different polysulfone dialyzers (F3 and F6HPS). We then applied it to predict the impact of varying dialysate flow rate on toxin removal. We found that bilirubin removal is independent of dialysate flow rate within the clinically relevant range (20 mL/min - 800 mL/min), matching our models predictions. At very low dialysate flow rates (2 mL/min), bilirubin removal declines, deviating from the thermodynamic model. This model may be useful to achieving optimal clinical outcomes by setting optimal dialyzer and flow rate conditions. Further improvement is possible by accounting for toxin adsorption onto the dialyzer membrane.

bioengineering↗

bioRxiv2023

High red/far-red ratio promotes root colonization of Serratia plymuthica A21-4 in tomato by root exudates-stimulated chemotaxis and biofilm formation

Effective colonization on plant roots is a prerequisite for plant growth promoting rhizobacterias (PGPR) to exert beneficial activities. Light is essential for plant growth, development and stress response. However, how light modulates root colonization of PGPR remains unclear. Here, we found that high red/far red(R/FR) light promoted and low R/FR light inhibited the colonization and growth enhancement of Serratia plymuthica A21-4 on tomato. Non-targeted metabolomic analysis of root exudates collected from different R/FR ratio treated tomato seedlings with A21-4 inoculation by ultra performance liquid chromatography-tandem mass spectrometry showed that 64 primary metabolites including amino acids, sugars and organic acids in high R/FR light-grown plants significantly increased compared with those determined for low R/FR light-grown plants. Among them, 7 amino acids, 1 organic acid and 1 sugar obviously induced the chemotaxis and biofilm formation of A21-4 compared to the control. Furthermore, exogenous addition of five artificial root exudate compontents (leucine, methionine, glutamine, 6-aminocaproic acid and melezitose) regained and further increased the colonization and growth promoting ability of A21-4 in tomato under low R/FR light and high R/FR light, respectively, indicating their involvement in high R/FR light-regulated the interaction of tomato root and A21-4. Taken together, our results, for the first time, clearly demonstrate that high R/FR light-induced root exudates play a key role in chemotaxis, biofilm formation and root colonization of A21-4. This study provides new insights into the interactions of plant-PGPR under different light conditions and can help promote the combined application of light supplementation and PGPR to facilitate crop growth and health in green agricultural production.

microbiology↗

bioRxiv2023

Bcl-xL is translocated to the nucleus via CtBP2 to epigenetically promote metastasis

Besides its mitochondria-based anti-apoptotic role, Bcl-xL also travels to the nucleus to promote cancer metastasis by upregulating global histone H3 trimethyl Lys4 (H3K4me3) and TGF{beta} transcription. How Bcl-xL is translocated into the nucleus and how nuclear Bcl-xL regulates H3K4me3 modification are not understood. Here, we report that C-terminal Binding Protein 2 (CtBP2) binds Bcl-xL via its N-terminus and translocates Bcl-xL into the nucleus. Knockdown of CtBP2 by shRNA decreases the nuclear portion of Bcl-xL and reverses Bcl-xL-induced cell migration and metastasis in mouse models. Furthermore, knockout of CtBP2 suppresses Bcl-xL transcription. The binding between Bcl-xL and CtBP2 is required for their interaction with MLL1, a histone H3K4 methyltransferase. Pharmacologic inhibition of MLL1 enzymatic activity reverses Bcl-xL-induced H3K4me3 and TGF{beta} mRNA upregulation as well as cell invasion. Moreover, cleavage under targets and release using nuclease (CUT&RUN) coupled with next generation sequencing reveals that H3K4me3 modifications are particularly enriched in the promotor region of genes encoding TGF{beta} and its signaling pathway in the cancer cells overexpressing Bcl-xL. Altogether, the metastatic function of Bcl-xL is mediated by its interaction with CtBP2 and MLL1.

cancer biology↗

bioRxiv2022

Discovery of a SARS-CoV-2 Broadly-Acting Neutralizing Antibody with Activity against Omicron and Omicron + R346K Variants

The continual emergence of SARS-CoV-2 variants of concern, in particular the newly emerged Omicron (B.1.1.529) variant, has rendered ineffective a number of previously EUA approved SARS-CoV-2 neutralizing antibody therapies. Furthermore, even those approved antibodies with neutralizing activity against Omicron are reportedly ineffective against the subset of Omicron variants that contain a R346K substitution, demonstrating the continued need for discovery and characterization of candidate therapeutic antibodies with the breadth and potency of neutralizing activity required to treat newly diagnosed COVID-19 linked to recently emerged variants of concern. Following a campaign of antibody discovery based on the vaccination of Harbour H2L2 mice with defined SARS-CoV-2 spike domains, we have characterized the activity of a large collection of Spike-binding antibodies and identified a lead neutralizing human IgG1 LALA antibody, STI-9167. STI-9167 has potent, broad-spectrum neutralizing activity against the current SARS-COV-2 variants of concern and retained activity against the Omicron and Omicron + R346K variants in both pseudotype and live virus neutralization assays. Furthermore, STI-9167 nAb administered intranasally or intravenously provided protection against weight loss and reduced virus lung titers to levels below the limit of quantitation in Omicron-infected K18-hACE2 transgenic mice. With this established activity profile, a cGMP cell line has been developed and used to produce cGMP drug product intended for use in human clinical trials.

microbiology↗

bioRxiv2020

Discovery and Development of Human SARS-CoV-2 Neutralizing Antibodies using an Unbiased Phage Display Library Approach

SARS-CoV-2 neutralizing antibodies represent an important component of the ongoing search for effective treatment of and protection against COVID-19. We report here on the use of a naive phage display antibody library to identify a panel of fully human SARS-CoV-2 neutralizing antibodies. Following functional profiling in vitro against an early pandemic isolate as well as a recently emerged isolate bearing the D614G Spike mutation, the clinical candidate antibody, STI-1499, and the affinity-engineered variant, STI-2020, were evaluated for in vivo efficacy in the Syrian golden hamster model of COVID-19. Both antibodies demonstrated potent protection against the pathogenic effects of the disease and a dose-dependent reduction of virus load in the lungs, reaching undetectable levels following a single dose of 500 micrograms of STI-2020. These data support continued development of these antibodies as therapeutics against COVID-19 and future use of this approach to address novel emerging pandemic disease threats.

microbiology↗

bioRxiv2018

AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation

PurposeRadiation therapy (RT) is a common treatment for head and neck (HaN) cancer where therapists are often required to manually delineate boundaries of the organs-at-risks (OARs). The radiation therapy planning is time-consuming as each computed tomography (CT) volumetric data set typically consists of hundreds to thousands of slices and needs to be individually inspected. Automated head and neck anatomical segmentation provides a way to speed up and improve the reproducibility of radiation therapy planning. Previous work on anatomical segmentation is primarily based on atlas registrations, which takes up to hours for one patient and requires sophisticated atlas creation. In this work, we propose the AnatomyNet, an end-to-end and atlas-free three dimensional squeeze-and-excitation U-Net (3D SE U-Net), for fast and fully automated whole-volume HaN anatomical segmentation.\n\nMethodsThere are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning. We propose the AnatomyNet that has one down-sampling layer with the trade-off between GPU memory and feature representation capacity, and 3D SE residual blocks for effective feature learning to alleviate these challenges. Moreover, we design a hybrid loss function with the Dice loss and the focal loss. The Dice loss is a class level distribution loss that depends less on the number of voxels in the anatomy, and the focal loss is designed to deal with highly unbalanced segmentation. For missing annotations, we propose masked loss and weighted loss for accurate and balanced weights updating in the learning of the AnatomyNet.\n\nResultsWe collect 261 HaN CT images to train the AnatomyNet, and use MICCAI Head and Neck Auto Segmentation Challenge 2015 as the benchmark dataset to evaluate the performance of the AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3% on average. The proposed AnatomyNet takes only 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178 x 302 x 225. All the data and code will be availablea.\n\nConclusion1We propose an end-to-end, fast and fully automated deep convolutional network, AnatomyNet, for accurate and whole-volume HaN anatomical segmentation. The proposed Anato-myNet outperforms previous state-of-the-art methods on the benchmark dataset. Extensive experiments demonstrate the effectiveness and good generalization ability of the components in the AnatomyNet.

bioengineering↗