Biology subjects

LIU, Z.

Publications and source records attributed to LIU, Z..

4 recordsLinked to original sources

bioRxiv2025

Architecture and mechanism of a dual-enzyme retron system in prokaryotic immunity

Retrons are bacterial genetic retroelements encoding a reverse transcriptase (RT) and a non-coding RNA (ncRNA)-multi-copy single-stranded DNA (msDNA) hybrid. Diverse effector proteins or domains are found to associate with retrons, typically forming tripartite toxin-antitoxin systems involved in anti-phage defense. Although retrons have attracted growing interest in genome editing technologies, the mechanisms underlying most retron-mediated immune systems remain poorly understood. Here, we characterized a distinct quaternary retron system, Ec78, harboring a dual-enzymatic effector complex, in which the PtuA ATPase and PtuB nuclease act in concert to mediate phage clearance. The cryo-EM structure of the Ec78 complex adopts a flower-basket-like architecture, with two Ec78 retrons engaging the PtuAB effector complexes through a unique msDNA-insertion assembly mechanism. Interestingly, a sensing loop on the RT protein tightly monitors the length of the msDNA, which is likely responsible for phage detection and the subsequent release of the toxic effector complex. We further determined the cryo-EM structure of the retron-unbound effector complex, revealing an arginine-lysine finger loop on the PtuB nuclease that undergoes an ordered-to-disordered transition for enzymatic activation. Together, our work not only delineates the molecular basis underlying the Ec78 system in antiviral defense but also highlights the mechanistic diversity of retron systems in prokaryotic immunity.

biochemistry↗

bioRxiv2025

RPGR regulates motile cilia by interfering with actin dynamics

Cilia are highly conserved cellular organelles extruding from the surface of cell types carrying either sensory (signaling) or motile functions. These include photoreceptor cells and airway epithelia, where they function in light sensation and mucociliary clearance respectively. Retinitis pigmentosa GTPase regulator (RPGR) variants affect both photoreceptor sensory cilia and airway motile cilia, leading to retinitis pigmentosa (RP) and in some cases, primary ciliary dyskinesia (PCD), both debilitating conditions. Not all patients develop PCD and it is unclear which RPGR variants predispose patients to PCD and why this happens. In this study, using nasal biopsy samples of patients with RPGR-related RP, we leverage 2D organoid cell culturing, super-resolution microscopy, and live cell imaging to characterize the multiciliated cells from patients with different RPGR variants, healthy human nasal and bronchial multiciliated cells with CRISPR-modified RPGR function. We demonstrate for the first time that multiciliated cells with RPGR variants may have reduced ciliation, shorter cilia, significantly impaired cilia beat, or cilia beat incoordination, which could lead to defective mucociliary clearance and lung disease. In addition, we show the regulation of motile cilia by RPGR involves F-actin, as evidenced by temporarily reduced Gelsolin and undissolved condensed actin meshwork at the apical surface of RPGR-deficient multiciliated cells. In support, we show that the motile cilia defect can be ameliorated by treating with the actin polymerization inhibitor Latrunculin A. Though PCD was observed only in patients with variants that affect both main isoforms (RPGR1-19 and RPGRORF15), patients with variants affecting only RPGRORF15 also showed cilia and airway anomalies. Though all RPGR variants affected motile cilia in one way or another, RPGR loss of function variants affecting both isoforms are associated with more severe cilia and systemic phenotypes, the mechanisms of which involve the accumulation of apical F-actin. One Sentence Summary: Loss of RPGR, through the mechanism of apical F-actin accumulation in airway multiciliated cells, leads to reduced ciliation with short cilia that have an impaired beat, leading to defective mucociliary clearance.

cell biology↗

bioRxiv2024

Identification and Classification of Fungal GPCR Gene Families

G protein-coupled receptors (GPCRs) are transmembrane proteins crucial for signal transduction in eu-karyotes, responding to diverse extracellular signals. Researchers have found 14 distinct types of GPCRs in fungi but their distribution among numerous fungal species remained largely unexamined. Our study identified and classified GPCRs in 1,357 fungal species, and shed light on GPCR distribution in fungi. The predominant class detected in fungi was Pth11-like GPCRs, exclusively found in Pezizomycotina and are notably acknowl-edged for their involvement in fungal pathogenicity. Our analysis suggested that Pezizomycotina ancestor possessed a more extensive array of Pth11-like GPCRs, but over time, some species underwent consider-able reductions in these GPCRs in conjunction with genome contractions. Additionally, we identified 2,089 mammalian homologs in Rhodopsin, Glutamate, and Frizzled classes across 594 fungal species, thereby augmenting the recognized fungal GPCR classes by three classes thought to be mammalian-specific. Utilizing a custom-built convolutional neural network (CNN) for the identification of fungal GPCRs, we discovered several potential novel fungal GPCRs. Moreover, anticipated interactions between these prospective new GPCRs and G-alpha proteins, as simulated by AlphaFold Multimer, offered further confirmation for these findings.

genomics↗

bioRxiv2018

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context and depend on the available HLA protein-peptide bound structures. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan.

bioinformatics↗