Biology subjects

Mo, W.

Publications and source records attributed to Mo, W..

13 recordsLinked to original sources

bioRxiv2026

Z-RNA-ZBP1 axis drives Tau-mediated neurodegeneration

Although the causes of Tau aggregates vary, once Tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimers disease (AD) being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting neurotoxicity of Tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in Tau-related neurodegeneration. Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements (TEs) that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from Heterochromatin Protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in AD patients was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged Tau-transgenic mice (24-month-old), highlighting the therapeutic potential of ZBP1 inhibition to strive against neurodegeneration in tauopathies.

neuroscience↗

bioRxiv2026

Oxidation-Driven mtDNA B-Z Transition Activates ZBP1 to Mediate Acetaminophen Hepatotoxicity

Acetaminophen (APAP) overdose induces mitochondrial damage in hepatocytes, leading to secondary toxicity that resists to N-acetylcysteine (NAC) treatment and culminates in hepatocyte death and acute liver failure (ALF)1. The underlying mechanisms remain poorly understood, often necessitating liver transplantation2. Here, we identify oxidative modification-driven B-to-Z transitions in mtDNA as the central pathological driver of APAP-induced ALF. Upon APAP exposure, oxidized mtDNA fragments leak into the cytosol, activating ZBP1 signaling via its Z domain. Genetic inhibition of ZBP1 mitigates liver damage and improves survival. Using synthetic 12-bp dGdC duplexes, we demonstrate that 8-oxoG substitution, even under physiological salt concentrations, is sufficient to induce Z-DNA formation, enabling specific ZBP1 binding through its Z domain. The 8-oxoG repair enzyme OGG13, activated by TH107854, removes 8-oxoG modifications and reverses Z-DNA to B-DNA conformation. In mice with lethal APAP toxicity, delayed NAC treatment results in 50% mortality. In contrast, TH10785 monotherapy increases survival to 90%, while its combination with NAC achieves 100% survival. These results define the oxidized mt Z-DNA-ZBP1 axis as a critical driver of APAP hepatotoxicity, providing fundamental insights into DNA conformational dynamics and therapeutic opportunities in drug-induced liver failure.

cell biology↗

bioRxiv2025

Research on Crop Phenotype Prediction Methods Based on SNP-context and Whole-genome Features Embedding

Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype. To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500--3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500--3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4--768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies. The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features do at specific context lengths, particularly for traits with low-to-moderate heritability (h2[isin](0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (h2[isin](0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers. Our code is available on https://github.com/oliveSpring/Crop_DNA_Embedding.git

genetics↗

bioRxiv2025

ZBP1 Senses Splicing Aberration through Z-RNA to promote Cell Death

RNA splicing, a highly regulated process performed by the spliceosome, is crucial for eukaryotic gene expression and cellular function. Numerous cellular stresses including oncogenic insults dysregulate RNA splicing, often provoking inflammatory responses and cell death. However, the molecular signal produced by spliceosome aberration and how cell sensed and respond to it remain elusive. Here we show that spliceosome inhibition induces the widespread formation of unique, left-handed nucleic acids, Z-form nucleic acids (Z-NAs) from the nucleus. These Z-NAs were double-stranded RNA (dsRNA), rather than DNA-RNA hybrids, that were predominantly derived from transcripts of mis-spliced intronic RNA. Spliceosome inhibition induced the egress of these Z-RNA from the nucleus to the cytoplasm in an active manner. Sensing of the accumulation of Z-RNA in the cytosol by the host sensor ZBP1 triggered cell death, mainly for RIPK3-MLKL dependent necroptosis. Collectively, these findings delineate a previously uncharacterized mechanism in which Z-NA sensing by ZBP1 responds to global aberrations of RNA splicing to trigger inflammatory cell death.

cell biology↗

bioRxiv2024

A ZBP1 isoform blocks ZBP1-mediated cell death

ZBP1 is an interferon-induced nucleic acid (NA) sensor that senses unusual Z-form NA (Z-NA), a type of left-handed nucleic acid. More than that, the binding of ZBP1 with Z-NA promotes cell death and inflammation. However, the mechanisms that dampen ZBP1 activation to fine-tune inflammatory responses are unclear. Here we characterize a short isoform of ZBP1 (referred to as ZBP1-S) as an intrinsic suppresser of the inflammatory signaling mediated by full-length ZBP1. Compared with ZBP1, ZBP1-S protein has Z domains but no RHIM domains. Mechanistically, ZBP1-S depresses ZBP1-mediated cell death by competitive binding with Z-NA for Z domains of ZBP1. Cells from mice (Rip1D325A/D325A) with Cleavage-resistant RIP1-induced autoinflammatory (CRIA) syndrome are alive but sensitive to IFN-induced and ZBP1-depedent cell death. Intriguingly, Rip1D325A/D325A cells go death spontaneously when ZBP1-S was deleted, indicating the cell death driven by ZPB1 is under the check of ZBP1-S. Thus, our findings reveal that alternative splicing of Zbp1 represents an autogenic inhibition for regulating ZBP1 signaling and indicate that uncoupling of Z-NA with ZBP1 could be an effective strategy against auto-inflammations. HighlightO_LIZBP1-short isoform is expressed synchronously with ZBP1. C_LIO_LIZBP1-short isoform counteracts ZBP1 mediated cell death. C_LIO_LIZBP1-S suppresses ZBP1 signaling in an Z-domain dependent manner. C_LIO_LIZBP1-S prevents the autoactivation of ZBP1 in Rip1D325A/D325A cells. C_LI

immunology↗

bioRxiv2024

A single-cell 3D spatiotemporal multi-omics atlas from Drosophila embryogenesis to metamorphosis

The development of a multicellular organism is a highly intricate process tightly regulated by numerous genes and pathways in both spatial and temporal manners. Here, we present Flysta3D, a comprehensive multi-omics atlas of the model organism Drosophila, spanning its developmental lifespan from embryo to pupa. Our datasets encompass 3D single-cell spatial transcriptomic, single-cell transcriptomic, and single-cell chromatin accessibility information. By integrating these multi-dimensional data, we constructed cell state trajectories that uncover the detailed profiles of tissue development. With a focus on the central nervous system (CNS) and midgut, we dissected the spatiotemporal dynamics of gene regulatory networks, cell type diversity, and morphological changes from a multi-omics perspective. This extensive atlas provides an unprecedentedly rich resource and serves as a systematic platform for studying Drosophila development with integrated single-cell data at an ultra-high spatiotemporal resolution.

developmental biology↗

bioRxiv2023

Transfer of disulfide bond formation modules via yeast artificial chromosomes promotes the expression of heterologous proteins in Kluyveromyces marxianus

Kluyveromyces marxianus is a food-safe yeast with great potential for producing heterologous proteins. Improving the yield in K. marxianus remains a challenge, while incorporating large-scale functional modules poses a technical obstacle in engineering. To address these issues, linear and circular yeast artificial chromosomes of K. marxianus (KmYACs) were constructed and loaded with disulfide bond formation modules from Pichia pastoris or K. marxianus. These modules contained up to 7 genes with a maximum size of 15 kb. KmYACs carried telomeres either from K. marxianus or Tetrahymena. KmYACs were transferred successfully into K. marxianus and stably propagated without affecting the normal growth of the host, regardless of the type of telomeres and configurations of KmYACs. KmYACs increased the overall expressions of disulfide bond formation genes and significantly enhanced the yield of various heterologous proteins. In high-density fermentation, the use of KmYACs resulted in a glucoamylase yield of 16.8 g/L, the highest reported level to date in K. marxianus. Transcriptomic and metabolomic analysis of cells containing KmYACs suggested increased FAD biosynthesis, enhanced flux entering the TCA cycle and a preferred demand for lysine and arginine as features of cells overexpressing heterologous proteins. Consistently, supplementing lysine or arginine further improved the yield. Therefore, KmYAC provides a powerful platform for manipulating large modules with enormous potential for industrial applications and fundamental research. Transferring the disulfide bond formation module via YACs proves to be an efficient strategy for improving the yield of heterologous proteins, and this strategy may be applied to optimize other microbial cell factories. Impact StatementIn this study, yeast artificial chromosomes of K. marxianus (KmYACs) were constructed and successfully incorporating modules for large-scale disulfide bond formation. KmYACs were stably propagated in K. marxianus without compromising the normal growth of the host, irrespective of the selection of telomeres (either Tetrahymena or K. marxianus) and configuration (either linear or circular). KmYACs notably enhanced the expressions of various heterologous proteins, with further yield improvement by supplementing lysine or arginine in the medium. Our findings affirm KmYAC as a robust and versatile platform for transferring large-scale function modules, demonstrating immense potential for both industrial applications and fundamental research.

synthetic biology↗

bioRxiv2022

Microglial Piezo1 senses Aβ fibrils stiffness to restrict Alzheimer's disease

The pathology of Alzheimers disease (AD) is associated with the extracellular amyloid-{beta} (A{beta}) plaques that perturb the mechanical properties of brain tissue. Microglia sense and integrate biochemical cues in their local microenvironment, intimate linking with AD progress. However, neither the microglial mechanosensing pathway nor its impact on AD pathogenesis is well studied. Here, we showed that the mechanosensitive ion channel Piezo1 is increased in microglia upon stiffness stimuli of A{beta} fibrils. The upregulation of Piezo1 in A{beta} plaque-associated microglia was observed in AD mouse models and human patients. Microglia lacking Piezo1 disturbed microglial clustering, phagocytosis, and compaction of A{beta} plaques, resulting in the exacerbation of A{beta} and neurodegenerative pathologies in AD. Conversely, pharmacological activation of Piezo1 ameliorated brain A{beta} burden and cognitive impairment in the 5xFAD mouse. Together, our results reveal Piezo1, as a mechanosensor of A{beta} fibrils stiffness in microglia, could represent a promising therapeutic target for AD.

neuroscience↗

bioRxiv2022

Pore-C Simultaneously Captures Genome-wide Multi-way Chromatin Interaction and Associated DNA Methylation Status in Arabidopsis

In the past decade, genome-wide characterization of the three-dimensional chromatin structure in plants using high-throughput methods has greatly advanced our knowledge in plant genome architecture (Liu and Weigel, 2015; Ouyang et al., 2020). However, due to the limitation of Illumina short-read sequencing, the genome-wide contact map obtained by Hi-C/ChIA-PET is pairwise, and the multi-way interaction can only be inferred from the two-way data. To directly capture multi-way interaction in Arabidopsis, we applied a long-read-based method called Pore-C that directly sequences the DNA fragments joined by proximity-based ligation (Ulahannan et al., 2019).

molecular biology↗

bioRxiv2020

Single-nucleus full-length RNA profiling in plants incorporates isoform information to facilitate cell type identification

The broad application of large-scale single-cell RNA profiling in plants has been restricted by the prerequisite of protoplasting. We recently found that the Arabidopsis nucleus contains abundant polyadenylated mRNAs, many of which are incompletely spliced. To capture the isoform information, we combined 10x Genomics and Nanopore long-read sequencing to develop a protoplasting-free full-length single-nucleus RNA profiling method in plants. Our results demonstrated using Arabidopsis root that nuclear mRNAs faithfully retain cell identity information, and single-molecule full-length RNA sequencing could further improve cell type identification by revealing splicing status and alternative polyadenylation at single-cell level.

genomics↗

bioRxiv2020

Morphologic Classification and Automatic Nugent Scoring of Bacterial Vaginosis by Deep Neural Networks

BackgroundBacterial vaginosis (BV) was the most common condition for womens health caused by the disruption of normal vaginal flora and an overgrowth of certain disease-causing bacteria, affecting 30-50% of women at some time in their lives. Gram stain followed by Nugent scoring (NS) based on bacterial morphotypes under the microscope was long considered golden standard for BV diagnosis. This conventional manual method was often considered labor intensive, time consuming, and variable results from person to person. MethodsWe developed four convolutional neural networks (CNN) models, and evaluated their ability to automatic identify vaginal bacteria and classify Nugent scores from microscope images. All the CNN models were first trained with 23280 microscopic images labeled with Nugent scores from top experts. A separate set of 5815 images were evaluated by the CNN models. The best CNN model was selected to generalize its application on an independent sets of 1082 images collecting from three teaching hospitals. Different hardwares were used to take images in hospitals. ResultsOur model could classify three Nugent Scores from images with high three classification accuracy of 89.3% (with 82.4% sensitivity and 96.6% specificity) on the 5815 test images, which was better diagnostic yield than the top-level technologists and obstetricians in China. The ability of generalization for our model was strong that it obtained 75.1%, which was 6.6% higher than the average of technologists. ConclusionThe CNN model over performed human healthcare practitioners on accuracy, efficiency and stability for BV diagnosis using microscopic image-based Nugent scores. The deep learning model may offer translational application in automating diagnosis of bacterial vaginosis with proper supporting hardware.

microbiology↗

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2019

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations from exponential growth only at higher densities due to limited resources such as space and nutrients. However, recent pre-clinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models however, tumor growth data is limited by the lower limit of detection (i.e. a measurable lesion) and confounding variables, such as tumor microenvironment and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to consider the small number of cells in this low-density regime and use the moment approach for stochastic parameter estimation to calibrate the stochastic growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells depends on cell number. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging tumors and relapse.\n\nAuthor SummaryThe growth kinetics of cancer cells at very low cell densities are of utmost clinical importance as the ability of a small number of newly transformed or surviving cells to grow exponentially and thus, to \"take off\" underlies tumor formation and relapse after treatment. Mathematical models of stochastic tumor cell growth typically assume a stochastic birth-death process of cells impacted by limited nutrients and space when cells reach high density, resulting in the widely accepted logistic growth model. Here we present an in-depth investigation of alternate growth models adopted from ecology to describe potential deviations from a simple cell autonomous birth-death model at low cell densities. We show that our stochastic modeling framework is robust and can be used to identify the underlying structure of stochastic growth trajectories from both simulated and experimental data taken from a controlled in vitro setting in which we can capture data from the relevant low cell density regime. This work suggests that the assumption of cell autonomous proliferation via a constant exponential growth rate at low cell densities may not be appropriate for all cancer cell growth dynamics. Consideration of cooperative behavior amongst tumor cells in this regime is critical for elucidating strategies for controlling tumor cell growth.

cancer biology↗