Biology subjects

Zu, J.

Publications and source records attributed to Zu, J..

4 recordsLinked to original sources

bioRxiv2025

Synergistic neuroprotective and cognitive-enhancing effects of Walnut Peptide and Theanine in human brain organoid and mouse stress models

Stress is a prevalent mental health concern emerging predominantly in late adolescence or early adulthood. Since 2007, the Food and Drug Administration (FDA) has not approved any novel anxiolytic pharmaceuticals, fueling interest in nutritional supplements as alternative therapies for stress management. Building on prior zebrafish research, this study investigates the synergistic effects of Theanine (Th) and Walnut Peptide (WP) on stress mitigation and cognitive enhancement. Utilizing the human brain organoid stress (BO-stress) model, WP+ Th were observed to reduce stress and regulate expression of neurotransmitters, including Gamma-Aminobutyric Acid (GABA), serotonin (5-HT), dopamine (DA), and acetylcholine (Ach), as well as brain-derived neurotrophic factor (BDNF) and serotonin transporter (SERT). Subsequent in vivo study using C57BL/6J mouse-stress model demonstrated that the treatment (Th 85 mg/mL + WP 200 mg/mL), or administrated with vehicles, significantly improved their performance in stress and cognitive assessments, partially normalized neurotransmitter imbalances by modulating SERT and BDNF expression. These findings highlighted the potential of using WP + Th, particularly when delivered with vehicles (eg: powder/yogurt/milk), as a novel combined therapeutic approach for stress management and cognitive enhancement. We performed a correlation analysis between BO-stress model and mouse-stress model, revealing a alignement in the SERT levels.In addition, SERT was highly correlated with other markers in the mouse hippocampus and may represent a key target for modulating the balance between stress and cognition. Significance StatementThis study established an innovative human brain organoid-stress model and, in conjunction with mouse-stress model, elucidated the synergistic actions of Theanine and Walnut Peptide in mitigating stress and augmenting cognitive function. Their beneficial effects were mediated through the regulation of SERT and BDNF, presenting a promising non-pharmacological avenue for mental health care. In addition, species differences between humans and mice were also examined through correlation analysis of brain organoids and mouse models. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/640260v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@1a81886org.highwire.dtl.DTLVardef@e2281dorg.highwire.dtl.DTLVardef@1b7b167org.highwire.dtl.DTLVardef@dff9aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

DSA-DeepFM: A Dual-Stage Attention-Enhanced DeepFM Model for Predicting Anticancer Synergistic Drug Combinations

MotivationDrug combinations are crucial in combating drug resistance, reducing toxicity, and improving therapeutic outcomes in the treatment of complex diseases. As the number of available drugs grows, the potential combinations increase exponentially, making it impractical to rely solely on biological experiments to identify synergistic drug pairs. Consequently, machine learning methods are increasingly used to systematically screen for synergistic drug combinations. However, most current approaches prioritize predictive performance by integrating auxiliary information or increasing model complexity. By overlooking the biological mechanisms behind feature interactions, their effectiveness in predicting drug synergy can be limited. ResultsWe present DSA-DeepFM, a deep learning model that integrates a dual-stage attention (DSA) mechanism with Factorization Machines (FMs) to improve drug synergy prediction by addressing complex biological feature interactions. The model incorporates categorical and auxiliary numerical inputs, embedding them into high-dimensional spaces and then fusing them through the DSA mechanism to capture both field-aware and embedding-aware patterns. These patterns are then processed by a DeepFM module, which captures low-order and high-order feature interactions before making final predictions. Validation testing demonstrates that DSA-DeepFM significantly outperforms traditional machine learning and state-of-the-art deep learning models. Additionally, t-SNE visualizations confirm the models discriminative power at various stages. As a case study, we use our model to identify eight novel synergistic drug combinations, three of which are well-supported by existing wet-lab experiments, underscoring its practical utility and potential for future applications.

bioinformatics↗

bioRxiv2023

Insulin-mTOR hyperfunction drives C. elegans aging opposed by the megaprotein LPD-3

Insulin-mTOR signaling drives anabolic growth during organismal development, while its late-life dysregulation may detrimentally contribute to aging and limit lifespans. Age-related regulatory mechanisms and functional consequences of insulin-mTOR remain incompletely understood. Here we identify LPD-3 as a megaprotein that orchestrates the tempo of insulin-mTOR signaling during C. elegans aging. We find that an agonist insulin INS-7 is drastically over-produced in early life and shortens lifespan in lpd-3 mutants, a C. elegans model of human Alkuraya-Ku[c]inskas syndrome. LPD-3 forms a bridge-like tunnel megaprotein to facilitate phospholipid trafficking to plasma membranes. Lipidomic profiling reveals increased abundance of hexaceramide species in lpd-3 mutants, accompanied by up-regulation of hexaceramide biosynthetic enzymes, including HYL-1 (Homolog of Yeast Longevity). Reducing HYL-1 activity decreases INS-7 levels and rescues the lifespan of lpd-3 mutants through insulin receptor/DAF-2 and mTOR/LET-363. LPD-3 antagonizes SINH-1, a key mTORC2 component, and decreases expression with age in wild type animals. We propose that LPD-3 acts as a megaprotein brake for aging and its age-dependent decline restricts lifespan through the sphingolipid-hexaceramide and insulin-mTOR pathways.

physiology↗

bioRxiv2018

Evolutionary branching of host resistance induced by density-dependent mortality

This study explores the evolutionary dynamics of host resistance in a susceptible-infected model with density-dependent mortality. We assume that the resistant ability of susceptible host will adaptively evolve, a different type of host differs in its susceptibility to infection, but the resistance to a pathogen involves a cost such that a less susceptible host results in a lower birth rate. By using the methods of adaptive dynamics and critical function analysis, we find that the evolutionary outcome relies mainly on the trade-off relationship between host resistance and its fertility. Firstly, we show that if the trade-off curve is globally con-cave, then a continuously stable strategy is predicted. In contrast, if the trade-off curve is weakly convex in the vicinity of singular strategy, then the evolutionary branching of host resistance is possible. Moreover, the bifurcation analysis shows that independent of the trade-off curve, the values of continuously stable strategy and evolutionary branching point will always increase as the demographic parameters increase. Secondly, after evolutionary branching in the host resistance has occurred, we examine the coevolutionary dynamics of the dimorphic host population and find that for a type of concave-convex-concave trade-off curve, the final evolutionary outcome may contain a relatively higher susceptible host and a relatively higher resistant host, which can continuously stably coexist on a long-term evo-lutionary timescale. Numerical simulation further shows that eventually the equilibrium population densities of the dimorphic susceptible host might be very close to each other. Finally, we find that for a type of sigmoidal trade-off curve, due to the high cost in terms of the birth rate, always the branch with higher resistance will go extinct, the eventual evolutionary outcome includes a monomorphic host with relatively lower resistance. Particularly, in this case we find that the evolution of costly host resistance may reduce the equilibrium population density of susceptible host, instead it may increase the equilibrium population density of infected host.

evolutionary biology↗