Biology subjects

zhao, q.

Publications and source records attributed to zhao, q..

6 recordsLinked to original sources

bioRxiv2026

Ancient admixture catalyzes homoploid hybrid speciation and intense genomic erosion in Asian langurs

Ancient admixture catalyzes evolutionary innovation, yet its long-term genomic consequences for newly formed lineages remain poorly understood. Here, based on 53 genomes covering 19 species of the Asian langur genus Trachypithecus, we explore admixtures role in shaping a reticulated radiation. Genome-wide analyses of phylogenomic triplet topologies demonstrate that phylogenetic discordance across this radiation is primarily driven by widespread introgression rather than incomplete lineage sorting. To mitigate historical noise and resolve the ancestral species tree, we anchored our phylogenetic analysis on the X-linked recombination desert (XLRD), which exhibits an 84.5% introgression reduction compared to autosomes. We identify Delacours langur as a clear case of homoploid hybrid speciation, arising from ancient admixture with [~]70:30 genomic contributions from ancestral northern and southern limestone langur lineages. This hybrid species fixed key reproductive isolation loci; notably, alternate inheritance of pigmentation genes (SLC45A4, HPS5, ADCY10) systematically coupled with a fixed RNF175 chimeric allele to drive its diagnostic pelage phenotype. This illustrates how introgressed multi-gene networks rapidly establish prezygotic visual barriers and profound phenotypic divergence. However, subsequent spatial isolation within fragmented karst landscapes forced a major conversion of genetic burden into realized load. Genome-wide, over 82% of loss-of-function variants occur in a homozygous state, reflecting the expression of lethal recessive mutations within long runs of homozygosity. Together, our findings demonstrate that ancient admixture can trigger homoploid hybrid speciation, yet subsequent ecological restriction locks derived lineages into severe, long-term genomic erosion, revealing a fundamental trade-off in reticulate evolutionary radiations.

evolutionary biology↗

bioRxiv2025

USP-ddG: A Unified Structural Paradigm with Data Efficacy and Mixture-of-Experts for Predicting Mutational Effects on Protein-Protein Interactions

Accurately estimating changes in binding free energy ({Delta}{Delta}G) is essential for understanding protein-protein interactions (PPIs) and guiding rational protein design. Existing deep learning approaches benefit from pre-training on large structural datasets, but they often require heavy computation and overlook data efficacy. In addition, prior models typically assume fixed backbone structures and ignore conformational dynamics, limiting generalization. We present USP-ddG, a unified structural paradigm for{Delta}{Delta} G prediction. USP-ddG integrates a dual-channel architecture with three complementary components: (i) inverse folding-based log-odds ratio, (ii) empirical energy terms from FoldX, and (iii) a geometric encoder with Gaussian noise to capture relaxed conformations. To enhance representation power, we introduce a framework that integrates feed-forward network (FFN) and Mixture-of-Experts (MoE) to model domain-invariant and -specific features, respectively. We further propose CATH-guided Folding Ordering (CFO), a data efficacy strategy that organizes samples to mitigate catastrophic forgetting and data distribution bias. USP-ddG consistently outperforms existing state-of-the-art (SoTA) methods on the SKEMPI v2.0 benchmark, including the challenging hold-out CATH test set. It achieves superior accuracy on both single- and multi-point mutations and demonstrates strong performance in antibody affinity optimization against H1N1 and HER2, and in assessing SARS-CoV-2 variants to hACE2. Ablation studies confirm the benefit of each component. These results highlight USP-ddG as a robust and data-efficient framework for modeling mutational effects on PPIs. AvailabilityUSP-ddG is publicly available at https://github.com/ak422/USP-ddG.

molecular biology↗

bioRxiv2023

TriTan: An efficient triple non-negative matrix factorisation method for integrative analysis of single-cell multiomics data

MotivationSingle-cell multi-omics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multi-modal data are either computationally expensive or limited in interpretation ans scope. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. ResultsHere, we propose TriTan (Triple inTegrative fast non-negative matrix factorisation), an efficient joint factorisation method for single-cell multiomics data. TriTan implements a highly efficient triple non-negative matrix factorisation algorithm which greatly enhances its computational speed, and facilitates interpretation by clustering both the cells and features simultaneously as well as identifying signature feature sets for each cell cluster. Additionally, three matrix factorisation produced by TriTan helps in finding associations of features across modalities, facilitating the prediction of cell type specific regulatory networks. We applied TriTan to single-cell multi-modal data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses that can be conducted utilising the outputs from TriTan. Availabilityhttps://github.com/maxxxxxxxin/TriTan online.

bioinformatics↗

bioRxiv2022

Integrating Network Pharmacology And Experimental Verification To Explore The Mechanism Of Qionggui Power Against Atherosclerosis

Qionggui Power (QP), a classic prescription in Traditional Chinese Medicine (TCM), has shown potential in the treatment of atherosclerosis during the past decades. However, the mechanism that mediates these cardiovascular benefits remains to be fully elucidated. Here, we investigated the effects and mechanisms of QP against atherosclerosis with network pharmacology approaches and in vitro model. The active ingredients and related targets of QP were collected from public databases. The hub targets and signaling pathways of QP against AS were defined by extensive application of bioinformatics approaches, including the protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). The predicted major targets were validated in LPS-stimulated murine macrophages RAW264.7. The anti-inflammatory properties of QP were also evaluated in this model. In silico investigation of QP resulted in the identification of 18 active ingredients and 49 chemical targets intersecting with AS-related genes. And KEGG pathway analysis revealed a high enrichment in the Lipid and Atherosclerosis pathway of these chemical targets. Biochemical analysis showed marked effects of QP on the expression of predicted chemical targets (PPARr, CAT, PTGS2) and LPS-induced inflammatory genes (IL1, IL6, and TNF). And these inhibitory effects were linked to the suppression of the NF-{kappa}B signaling pathway, which was activated by the LPS stimulus. Our findings revealed the therapeutic potential of QP in the prevention and treatment of atherosclerosis. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

pharmacology and toxicology↗

bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗

bioRxiv2021

An enzyme-based system for the extraction of small extracellular vesicles from plants

Plant-derived nanovesicles (NVs) and extracellular vesicles (EVs) are considered to be the next generation of nanocarrier platforms for biotherapeutics and drug delivery. However, EVs exist not only in the extracellular space, but also within the cell wall. Due to the limitation of isolation methods, the extraction efficiency is low, resulting in the waste of a large number of plants, especially rare and expensive medicinal plants.There are few studies comparing EVs and NVs. To overcome these challenges, we proposed and validated a novel method for the isolation of plant EVs by degrading the plant cell wall with enzymes to release the EVs in the cell wall, making it easier for EVs to break the cell wall barrier and be collected. We extracted EVs from the roots of Morinda officinalis by enzymatic degradation(MOEVs) and nanoparticles by grinding method (MONVs) as a comparison group. The results showed smaller diameter and higher yield of MOEVs.Both MOEVs and MONVs were readily absorbed by endothelial cells without cytotoxicity and promoted the expression of miR-155. The difference is that the promotion of miR-155 by MOEVs is dose-effective. More importantly, MOEVs and MONVs are naturally characterized by bone enrichment. These results support that EVs in plants can be efficiently extracted by enzymatic cell wall digestion and also confirm the potential of MOEVs as therapeutic agents and drug carriers.

pharmacology and toxicology↗