Biology subjects

Xu, Z. Z.

Publications and source records attributed to Xu, Z. Z..

3 recordsLinked to original sources

bioRxiv2026

Bidirectional hybridization between Ulva prolifera and U. linza (Ulvophyceae, Chlorophyta): Evidence for compatibility and paternal chloroplast inheritance

Ulva prolifera and U. linza are closely related species, with abundant adult thalli and reproductive cells co-occurring extensively in time and space during the Yellow Sea green tides. Elucidating their hybridization compatibility is crucial for species delimitation, assessing interspecific gene flow, and evaluating the ecological impacts of green tides. Previous studies suggested asymmetric gamete compatibility (only U. prolifera mt+ x U. linza mt-), but lacked sex-linked markers to reliably identify hybrid diploids and their reproductive modes, and did not examine chloroplast inheritance. Here, we performed bidirectional crosses using sexual strains of different geographic origins from both parents, with sex-linked markers to quantify progeny genotypes, examine fertility and reproduction pathway of F1 hybrids, and trace chloroplast inheritance using the species-specific petB marker. Our results showed that: (1) F1 hybrids were obtained in both cross directions, with significantly higher frequency in the direct cross (U. linza mt+ x U. prolifera mt-), indicating no complete reproductive isolation in either direction; the biased compatibility likely reflected genetic background differences among the limited strain combinations in a single study. (2) A considerable number of germinated progeny arose from parthenogenesis of parental gametes. (3) F1 hybrids from both crosses could undergo meiosis to form gametes and develop into gametophytes; additionally, F1 from the reciprocal cross produced diploid spores for asexual reproduction, suggesting meiotic disturbance. (4) Chloroplasts were maternally inherited in selfing of U. prolifera parent, but in all F1 hybrids they were paternally inherited, indicating a potential reversal of the inheritance pattern due to interspecific hybridization. These findings provided new insights into the potential for genetic exchange between U. prolifera and U. linza.

genetics↗

bioRxiv2024

RNA-ligand interaction scoring via data perturbation and augmentation modeling

RNA-targeting drug discovery is undergoing an unprecedented revolution. Despite recent advances in this field, developing data-driven deep learning models remains challenging due to the limited availability of validated RNA-small molecule interactions and the scarcity of known RNA structures. In this context, we introduce RNAsmol, a novel sequence-based deep learning framework that incorporates data perturbation with augmentation, graph-based molecular feature representation and attention-based feature fusion modules to predict RNA-small molecule interactions. RNAsmol employs perturbation strategies to balance the bias between true negative and unknown interaction space thereby elucidating the intrinsic binding patterns between RNA and small molecules. The resulting model demonstrates accurate predictions of the binding between RNA and small molecules, outperforming other methods with average improvements of [~]8% (AUROC) in 10-fold cross-validation, [~]16% (AUROC) in cold evaluation (on unseen datasets), and [~]30% (ranking score) in decoy evaluation. Moreover, we use case studies to validate molecular binding hotspots in the prediction of RNAsmol, proving the models interpretability. In particular, we demonstrate that RNAsmol, without requiring structural input, can generate reliable predictions and be adapted to many RNA-targeting drug design scenarios.

bioinformatics↗

bioRxiv2023

BATTER: Accurate Prediction of Rho-dependent and Rho-independent Transcription Terminators in Metagenomes

Bacterial transcription termination is a critical yet underexplored mechanism of gene regulation in microbial ecosystems. Existing computational tools, however, primarily focus on predicting transcript 3 ends generated by Rho-independent terminators (RITs) in model species, leaving significant gaps in understanding those generated by Rho-dependent terminators (RDTs), especially in non-model species. To address these limitations, we developed BATTER (BActeria Transcript Three Prime End Recognizer), a comprehensive computational tool for bacterial transcript 3 termini prediction. BATTER builds on the observation that conserved stem-loop structures are frequently associated with 3 ends of primary transcripts generated by both RIT and RDT mechanisms across distantly related bacterial species. BATTER demonstrated its advantage compared to existing tools. It enabled a comprehensive analysis of 42,905 representative bacterial genomes, uncovering that stem-loop structures exhibit clade-specific properties with greater variations between species than between gene families. Notably, BATTER uncovered that certain Cyanobacteria lineages, despite lacking Rho homologs, harbor Rho utilization (RUT) site-like sequences near 3 ends, with preliminary experimental validation in E. coli suggesting their partial functionality in transcription termination. Additionally, BATTER systematically identified pervasive premature termination events in antimicrobial resistance (AMR) genes, highlighting their regulatory roles in translation protection and drug efflux. This study advances our understanding of transcription termination across diverse bacterial lineages and provides a robust computational approach for exploring transcription regulation in complex microbial ecosystems.

bioinformatics↗