Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 541 records · Page 30Linked to original sources

bioRxiv2023

Human Induced Pluripotent Stem Cell based Hepatic-Modeling of Lipid metabolism associated TM6SF2 E167K variant

BACKGROUND AND AIMSTM6SF2 rs58542926 (E167K) is associated with an increase in the prevalence of Metabolic Disfunction-Associated Steatotic Liver Disease (MASLD). Despite all the investigation related to the role of this variant in lipid metabolism, conflicting results in mouse studies underscore the importance of creating a human model for understanding the TM6SF2 mechanism. Therefore, the aim of this study is to generate a reliable human in vitro model that mimic the effects of the TM6SF2 E167K mutation and can be used for future mechanism studies. APPROACH AND RESULTSWe performed gene editing on human-induced pluripotent stem cells (iPSC) derived from a healthy individual to obtain the cells carrying the TM6SF2 E167K mutation. After hepatic differentiation, a decrease in TM6SF2 protein expression was observed in the mutated-induced hepatocyte. An increase in intracellular lipid droplets and a decrease in the efflux of cholesterol and ApoB100 were also observed. Transcriptomics analysis showed up-regulation of genes related to the transport, flux, and oxidation of lipids, fatty acids, and cholesterol in TM6SF2 E167K cells. Additionally, signs of cellular stress were observed in the ER and mitochondria. CONCLUSIONSOur findings indicate that induced hepatocytes generated from iPSC carrying the TM6SF2 E167K recapitulate the effects observed in human hepatocytes from individuals with the TM6SF2 mutation. This study characterizes an in vitro model that can be used as a platform to help in the identification of potential clinical targets and therapies and to understand the mechanism by which the TM6SF2 E167K variant leads to vulnerability to MASLD.

cell biology↗

bioRxiv2023

Imputation approaches and quality standards for single-cell epigenetic age predictions

DNA methylation describes the addition of methyl groups, often between CpG dinucleotides. Single-cell bisulfite sequencing technologies allow the measurement of DNA methylation levels within individual cells. Epigenetic clocks are statistical models for computing biological age from DNA methylation levels, and have been used for detecting age variations in various disease contexts. However, there have been no attempts to apply epigenetic clocks to single-cell methylation data in humans. Thus, we questioned whether pre-built epigenetic clocks could be applied to single-cell methylation data; if so, how can we perform data quality control and imputation. We concluded that 1) linear regression-based epigenetic clocks can be applied to bisulfite-sequencing data, 2) data quality control can be used to reach the desired level of prediction accuracy, 3) first-principle imputation strategies could be used for missing data on selected CpG methylation sites, and 4) machine learning-based imputation tools could be used for accuracy-based age predictions. We built the first training-free, reference data-free framework for estimating epigenetic age in human single-cells, which would provide a foundation for future single-cell methylation-based age analyses.

bioinformatics↗

bioRxiv2023

Function-associated scRNA-seq on single lung cancer organoids unravels the immune landscape of tumor parenchyma

In vitro models coupled with multimodal approaches are urgently needed to decipher the local tumor immune microenvironment (TIME) owing to the heterogeneous nature of immune cells and their diverse spatial distributions. Here we generate primary lung cancer organoids (pLCOs) by isolating the tumor cell clusters, including the infiltrated immune cells, from dissected lung cancer samples. A FascRNA-seq platform allowing both phenotypic evaluation and the scRNA-seq of all the single cells in an organoid was developed to dissect the TIME in individual pLCOs. Our analysis on 171 individual pLCOs derived from 7 patients revealed that pLCOs retained the fundamental features as well as intra-tumor heterogeneity of local TIME in the parenchyma of parental tumor tissues, providing a series of models with consistent genetic background but various TIME. Linking the single cell transcriptome data of individual pLCOs with their responses to ICB allowed us to confirm the pivotal role of CD8+ Ts in ICB induced anti-tumor immunity, to identify the potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity.

cancer biology↗

bioRxiv2023

High-resolution ultrastructure and morphology of marine ammonia-oxidizing archaea Nitrosopumilus maritimus SCM1 revealed by Cryo-ET and STEM-EDS

Nitrososphaerota, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their significance in Earths ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant question unanswered of how the machinery of these organisms underpins metabolic functions. In this study, we combined chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaerota. Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules in which one type is possibly composed of polyphosphate, while the other type is polyhydroxyalkanoate. Collectively, these findings provide visual evidence for the resilience of AOA in the vast oligotrophic marine environment.

microbiology↗

bioRxiv2023

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗

bioRxiv2023

A Day in the Life of Arabidopsis: 24-Hour Time-lapse Single-nucleus Transcriptomics Reveal Cell-type specific Circadian Rhythms

Functional circadian clock is fundamental to the adaptation and survival of organisms. In land plants, the comprehensive profiling of circadian gene expression at the single-cell level is largely unknown partly due to the challenges in obtaining precisely-timed single cells from plant cells embedded within cell walls. To bridge this gap, we employed single-nucleus RNA sequencing (snRNA-seq) on twelve seedling samples collected at 2-hour intervals over a 24-hour day in Arabidopsis, yielding a total of over 130,000 nuclei. From this data, we found that three cell clusters in the shoot share a coherent rhythm, while more than 3,000 genes display cell-type specific rhythmic expression. Only 19 genes are oscillating in more than ten different cell types, and the majority of them are well-documented core oscillators, suggesting the snRNA-seq circadian data could be used to identify key circadian regulators in a broad range of plant systems. Our data provides the first comprehensive resource for plant circadian rhythms at the single-cell level (hosted at https://zhailab.bio.sustech.edu.cn/sc_circadian).

plant biology↗

bioRxiv2023

Differential effects of MATR3 variants on its cryptic splicing repression function

MATR3 is an RNA-binding protein implicated in neurodegenerative and neurodevelopmental diseases. However, little is known regarding the role of MATR3 in cryptic splicing within the context of functional genes and how disease-associated variants impact this function. We show that loss of MATR3 leads to cryptic exon inclusion in many transcripts. We reveal that ALS-linked S85C pathogenic variant reduces MATR3 solubility but does not impair RNA binding. In parallel, we report a novel neurodevelopmental disease-associated M548T variant, located in the RRM2 domain, which reduces protein solubility and impairs RNA binding and cryptic splicing repression functions of MATR3. Altogether, our research identifies cryptic events within functional genes and demonstrates how disease-associated variants impact MATR3 cryptic splicing repression function.

molecular biology↗

bioRxiv2023

Sequence-based GWAS in 180 000 German Holstein cattle reveals new candidate genes for milk production traits

BackgroundThe use of genome-wide association studies (GWAS) has led to the identification of numerous quantitative trait loci and candidate genes in dairy cattle. To obtain sufficient power of GWAS and to identify quantitative trait nucleotides, whole-genome sequence data is required. Sequence data facilitates the identification of potential causal variants; however, sequencing of whole genomes is still expensive for a large number of animals. Imputation is a quick and efficient way of obtaining sequence data from large datasets. Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causative variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 252,285 cattle were imputed to the sequence level and large-scale GWAS was carried out to identify new association signals. ResultsWe confirmed many known and identified 30 previously unreported candidate genes for milk, fat, and protein yield. While all of the genes were functionally associated with the traits, some showed pleiotropic effects as well. Specifically, association with mammary gland development, fatty acid synthesis, metabolism of lipids, or milk production QTLs in other farm animals has been reported. Variants associated with these genes explained a large percentage of genetic variance, compared to random ones. ConclusionsOur findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. Although milk production traits in cattle are comprehensively researched, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown in our study. With constantly growing sample sizes, we expect more insights into the genetic architecture of production traits in the future.

genomics↗

bioRxiv2023

Spatially resolved molecular and cellular atlas of the mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding function of the brain. Here, using snRNA-seq and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters with single-cell resolution involving over 6 million cells as well as for 29,655 genes. We have identified new astrocyte clusters, and demonstrated that distinct cell clusters exhibit preference for cortical subregions. In addition, we identified 155 genes exhibiting regional specificity in the brainstem, and 513 long non-coding RNA exhibited regional specificity in the adult brain. Parcellation of brain regions based on spatial transcriptomic information showed large overlap with that by traditional method. Furthermore, we have uncovered 411 transcription factor regulons with spatiotemporal specificity during development. Thus, our study has discovered genes and regulon with spatiotemporal specificity, and provided a high-resolution spatial transcriptomic atlas of the mouse brain.

neuroscience↗

bioRxiv2023

Effects of greening-induced warming and cooling on tree phenology in temperate and boreal forests

Tree phenology, periodic biological events in trees, is highly sensitive to climate change. It has been reported that forest greening can influence the local climate by altering the seasonal surface energy budget. However, tree phenological responses to forest greening remains poorly understood at large spatial scales. Combining remote-sensing derived phenological and leaf area indices since 2001, herein we show that forest greening led to earlier spring (-1.05 {+/-} 0.17 d) and autumn phenology (-1.95 {+/-} 0.14 d) in temperate and boreal forests. Our results show that forest greening in winter and spring decreased surface albedo and thus resulted in biophysical warming that caused earlier spring leaf phenology. In contrast, forest greening in summer and autumn triggered biophysical cooling by enhancing evapotranspiration, which led to earlier autumn leaf phenology. These findings suggest that forest greening could significantly alter tree phenology through seasonal biophysical impacts. Therefore, it is essential to incorporate these complicated biophysical impacts of greening into tree phenology models to accurately predict future shifts in tree phenology under future climate change.

ecology↗

bioRxiv2023

Intratumoral Delivery of Chlorine Dioxide Exploits its ROS-like Properties: A Novel Paradigm for Effective Cancer Therapy

Our study underscores the potential of chlorine dioxide, resembling reactive oxygen species, in promoting tissue regeneration and executing similar functions. Direct intratumoral administration of chlorine dioxide conveniently and sustainably kills cancer cells, without the risk of developing drug resistance. Given that tumors are viewed as non-healing wounds, the regenerative capabilities of chlorine dioxide offer an innovative method to expedite wound healing and enhance patient outcomes in cancer treatment. Compared to traditional methods, chlorine dioxide treatment reduces complications and risks, presenting a safer alternative. Notably, higher concentrations of chlorine dioxide in promoting tumor ablation can particularly demonstrate the potential to disrupt tumor vasculature. We advocate for the intratumoral injection of chlorine dioxide to achieve direct tumor ablation and boost antitumor immunity while minimizing harm to healthy tissues. Although initial findings indicate that chlorine dioxide stimulates an antitumor immune response, further investigation is essential. As oncology advances with the use of immune checkpoint inhibitors, intratumoral chlorine dioxide administration emerges as a promising strategy, potentially extending survival and alleviating treatment burdens. Future research should focus on refining chlorine dioxide protocols, exploring synergistic therapies, and understanding its systemic immune effects, thereby establishing chlorine dioxides role in cancer therapy. HighlightsO_LIChlorine dioxide demonstrates potent ROS-like capabilities for both cancer elimination and tissue regeneration, offering a and effective treatment approach. C_LIO_LIDirect intratumoral administration of chlorine dioxide conveniently and sustainably kills cancer cells, without the risk of developing drug resistance. C_LIO_LIChlorine dioxide can enhance tumor ablation potential by disrupting tumor vasculature, contributing to its overall effectiveness. C_LIO_LIChlorine dioxide promotes an immune response against cancer, significantly enhancing its therapeutic effectiveness. C_LIO_LIIntratumoral injections of chlorine dioxide represent an innovative and promising strategy for efficacious cancer therapy. C_LI

cancer biology↗

bioRxiv2023

Random forest classifiers trained on simulated data enable accurate short read-based genotyping of structural variants in the alpha globin region at Chr16p13.3

In regions where reads dont align well to a reference, it is generally difficult to characterize structural variation using short read sequencing. Here, we utilize machine learning classifiers and short sequence reads to genotype structural variants in the alpha globin locus on chromosome 16, a medically-relevant region that is challenging to genotype in individuals. Using models trained only with simulated data, we accurately genotype two hard-to-distinguish deletions in two separate human cohorts. Furthermore, population allele frequencies produced by our methods across a wide set of ancestries agree more closely with previously-determined frequencies than those obtained using currently available genotyping software.

bioinformatics↗

bioRxiv2023

De novo design and optimization of aptamers with AptaDiff

Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA/RNA libraries using in vitro methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiffs superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, Surface Plasmon Resonance (SPR) experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of 87.9% and 60.2% in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers. Key PointsO_LIWe proposed AptaDiff, the first in silico aptamer design method based on the diffusion model. Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data. C_LIO_LIAptadiff can optimize aptamers through affinity-guided generation via Bayesian optimization within a motif-dependent latent space, and the affinity of the optimized aptamers to the target protein is better than the best experimental candidate from traditional SELEX screening. C_LIO_LIAptadiff consistently outperforms the current state-of-the-art method in terms of quality and fidelity across high-throughput screening data targeting distinct proteins. C_LI

bioinformatics↗

bioRxiv2023

MPRAbase: A Massively Parallel Reporter Assay Database

Massively parallel reporter assays (MPRAs) represent a set of high-throughput technologies that measure the functional effects of thousands of sequences/variants on gene regulatory activity. There are several different variations of MPRA technology and they are used for numerous applications, including regulatory element discovery, variant effect measurement, saturation mutagenesis, synthetic regulatory element generation or characterization of evolutionary gene regulatory differences. Despite their many designs and uses, there is no comprehensive database that incorporates the results of these experiments. To address this, we developed MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. The MPRAbase web interface (http://www.mprabase.com) serves as a centralized user-friendly repository to download existing MPRA data for independent analysis and is designed with the ability to allow researchers to share their published data for rapid dissemination to the community.

genomics↗

bioRxiv2023

EMO: Predicting Non-coding Mutation-induced Up- and Down-regulation of Risk Gene Expression using Deep Learning

The challenge of understanding how alterations in non-coding DNA regulate gene expression is substantial, with far-reaching consequences for the advancement of human genetics and disease research. Accurately predicting the up- and down-regulation of gene expression quantitative trait loci (eQTLs) offers a potential avenue to accelerate the identification of associations between non-coding variants and phenotypic traits. However, current methods for predicting the impact of non-coding mutations on gene expression changes fail to predict the sign of eQTLs accurately. Additionally, the requirement for tissue-specific training models within these methods restricts their applicability, especially when extending predictive abilities to single-cell resolution. In this study, we present EMO, an innovative transformer-based pre-trained method, designed to predict the up- and down-regulation of gene expression caused by single non-coding mutations using DNA sequences and ATAC-seq data. EMO extends the effective prediction range up to 1Mbp between the non-coding mutation and the transcription start site (TSS) of the target gene. It demonstrates competitive prediction performance across various variant TSS distances and surpasses the state-of-the-art structure. To assess its effectiveness, EMO was fine-tuned using eQTLs from two brain tissues for external validation. We also evaluated EMOs transferability to single-cell resolution by fine-tuning it on eQTLs from six types of immune cells, achieving satisfactory results in each cell type (AUC > 0.860). Furthermore, EMO displayed promising potential in analyzing disease-associated eQTLs.

bioinformatics↗

bioRxiv2023

THER: Integrative Web Tool for Tumor Hypoxia Exploration and Research

Hypoxia is an important factor in the adaptation of tumor cells to their environment, contributes to their malignant progression, and affects tumor prognosis and drug sensitivity. Although there is a wealth of transcriptomic data stored in public databases, there is a lack of web-based tools for analyzing these data to explore the link between hypoxia and the mechanisms of tumorigenesis and progression. To this end, we have developed an interactive web-based tool called THER, which is designed to help users easily identify potential targets, mechanisms of action and effective drugs for treating hypoxic tumors. THER integrates 63 transcriptomic tumor hypoxia datasets from the Gene Expression Omnibus (GEO) database, covering 3 species, 18 tumor types and 42 cell line types. This web tool provides five modules that allow users to perform differential expression analysis, expression profiling analysis, correlation analysis, enrichment analysis and drug sensitivity analysis on different datasets based on different oxygen statuses. We expect that users will be able to use the tool to identify valuable biomarkers, further reveal the molecular mechanisms of tumor hypoxia, and identify effective drugs, thus providing a scientific basis for tumor diagnosis and treatment. THER is open to all users and can be accessed without login at https://smuonco.shinyapps.io/THER/.

cancer biology↗

bioRxiv2023

Monocyte-derived microglia with Dnmt3a mutation cause motor pathology in aging mice

Microglia are established in embryogenesis forming a self-containing cellular compartment resisting seeding with cells derived from adult definitive hematopoiesis. We report that monocyte-derived macrophages (MoM{Phi}) accumulate in the brain of aging mice with distinct topology, including the nigrostriatum and medulla, but not the frontal cortex. Parenchymal MoM{Phi} adopt bona fide microglia expression profiles. Unlike microglia, these monocyte-derived microglia (MoMg) are due to their hematopoietic origin targets of clonal hematopoiesis (CH). Using a chimeric transfer model, we show that hematopoietic expression of DNMT3AR822H, a prominent mutation in human CH, renders MoMg pathogenic promoting motor deficits resembling atypical Parkinsonian disorders. Collectively, these data establish in a mouse model that MoMg progressively seed the brains of aging healthy mice, accumulate in selected areas, and, when carrying a somatic mutation associated with CH, can contribute to brain pathology.

immunology↗

bioRxiv2023

Genome assembly and genomic architecture of a prominent cold-resistant rapeseed germplasm

Graphical AbstractThe genomic landscape of cold-tolerant winter rapeseed (Brassica napus, L) has been poorly characterized. We assembled a high-quality reference genome of a prominent cold-tolerant winter rapeseed cultivar, NTS57, and performed phylogenetic and pan-genomic analyses by integrating other reported Brassica napus accessions. The transcriptome analysis revealed that microtubule-associated biological pathways were much more active in NTS57 under cold stress than in the cold-sensitive variety. Whole genome methylation data analysis revealed that DNA demethylation on protein-coding genes and repetitive elements, especially at CHH sites, is essential for cold response in winter rapeseed.

genomics↗