Biology subjects

xu, k.

Publications and source records attributed to xu, k..

3 recordsLinked to original sources

bioRxiv2025

Prognosis prediction using autophagy gene expression in osteosarcoma

Defining the transition from localized to metastatic osteosarcoma (OS) before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the pre-metastatic tumor microenvironments own record of molecular barcode exposures encoded in cell-intrinsic stress states. We analyzed autophagy-dependent transcriptional datasets from 139 individuals to develop Auto-RS, a computational classifier that can integrate age and sex to deliver individualized risk management. The prognosis-interpretable features of Auto-RS recapitulate established molecular trajectories of metastasis at the single-cell level, capturing tumor cells intrinsic shift from proliferative to invasive states and revealing cooperative programs among cancer-associated fibroblasts and immune cells. More significantly, Auto-RS can expose chemotherapy vulnerabilities of newer drugs, providing a framework to prioritize therapeutics without direct testing in children. This framework brings a potential inflection point, where metastasis prediction and therapeutic stratification may converge to improve OS outcomes.

cell biology↗

bioRxiv2025

HPV16 E2 protein possesses intrinsic helicase activity and sterically hinders E1 function through direct interaction

HPV16 E2 protein is a key regulatory protein essential for viral replication, yet no enzymatic activity had been attributed to it until now. In this study, we report for the first time that E2 possesses intrinsic ATP-dependent DNA unwinding activity. Mutational analysis identified residues K299, Y303, and K306 as critical for this helicase function. We further demonstrate that podophyllotoxin directly binds to E2 and inhibits its unwinding activity with an IC50 of 0.1074 {micro}M, mediated primarily by residues Q320 and H342. Comparative analysis revealed that the ATPase and helicase activities of E2 are considerably weaker than those of E1. Notably, we discovered that E2 potently inhibits the helicase activity of E1. This suppression is facilitated by the N-terminal domain of E2 (amino acids 1-245) through direct interaction with E1, with residue E39 playing a critical role. Our findings not only unveil a previously unrecognized enzymatic function of E2 but also suggest its role as a potential antiviral target. Moreover, the observed inhibitory effect of E2 on E1 highlights a novel regulatory mechanism for HPV DNA replication. Synopsis O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/676238v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@12fd0aforg.highwire.dtl.DTLVardef@2dc50forg.highwire.dtl.DTLVardef@e46f5eorg.highwire.dtl.DTLVardef@14b87da_HPS_FORMAT_FIGEXP M_FIG C_FIG E2 protein has traditionally been recognized primarily for its DNA-binding and transcriptional regulatory functions. This study provides the first evidence that E2 protein possesses intrinsic enzymatic activity, identifies a small-molecule inhibitor targeting this activity, and reveals a novel mechanism of E1-E2 interaction. O_LIHPV16 E2 Protein Exhibits ATPase and Helicase Activities C_LIO_LIIdentification of Key Amino Acid Residues for HPV16 E2 Protein Enzymatic Activity C_LIO_LIPPT Effectively Inhibits E2 Protein Helicase Activity In Vitro C_LIO_LIE2 Protein Exhibits Weaker Enzymatic Activity Than E1 and Inhibits E1 Helicase Activity C_LIO_LIE2 Inhibits E1 Helicase Activity Through Protein-Protein Interaction C_LI

molecular biology↗

bioRxiv2017

Is chaos making a difference? Synchronization transitions in chaotic and non-chaotic neuronal networks

Chaotic dynamics of neural oscillations has been shown at the single neuron and network levels, both in experimental data and numerical simulations. Theoretical studies over the last twenty years have demonstrated an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to relevant network behavior and whether the dynamical richness of neural networks are sensitive to the dynamics of isolated neurons, still remain open questions. We investigated transition dynamics of a medium-sized heterogeneous neural network of neurons connected by electrical coupling in a small world topology. We make use of an oscillatory neuron model (HB+Ih) that exhibits either chaotic or non-chaotic behavior at different combinations of conductance parameters. Measuring order parameter as a measure of synchrony, we find that the heterogeneity of firing rate and types of firing patterns make a greater contribution than chaos to the steepness of synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in process of network synchronization transitions. Moreover, the macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as the multi-stable behavior, where the network dynamically switches between a number of different semi-synchronized, metastable states.

biophysics↗