Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 487 records · Page 27Linked to original sources

bioRxiv2024

Linkage: an interactive shiny app and R package for linking of DNA regulatory peaks to genes

SummaryExisting studies have demonstrated that the integration analysis of transcriptomic and epigenomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, such investigations on large-scale sequencing data remain challenging for researchers or clinicians with limited bioinformatics knowledge. To facilitate the interpretation of gene regulatory landscape, we developed an R Shiny application and R package [Linking of atac-seq to gene expression data (Linkage)] for exploring and visualizing potential cis-regulatory elements (CREs) of genes based on ATAC-seq and RNA-seq data. Linkage offers six modules to systematically identify, annotate, and interpret potential gene regulatory elements from the whole genome step by step. Linkage can provide interactive visualization for the correlation between chromatin accessibility and gene expression. More than that, Linkage identifies transcription factors (TFs) that potentially drive the chromatin changes through identifying TF binding motifs within the CREs and constructing trans-regulatory networks of the target gene set. This powerful tool enables researchers to conduct extensive multiomics integration analysis and generate visually appealing visualizations that effectively highlight the relationship between genes and corresponding regulatory elements. With Linkage, users can obtain publishable results and gain deeper insights into the gene regulatory landscape. Availability and implementation Linkage is freely available as a Shiny web application (https://xulabgdpu.org.cn/linkage) and an R package (https://github.com/XuLab-GDPU/Linkage). The documentation is available at (https://aicplane.github.io/Linkage-tutorial/).

bioinformatics↗

bioRxiv2024

Genome-Wide Transcriptome Dynamics in Auxin Homeostasis During Fruit Development in Strawberry (F. x ananassa)

The plant hormone auxin plays a crucial role in regulating important functions in strawberry fruit development. Although a few studies have described the complex auxin biosynthetic and signaling pathway in wild diploid strawberry (Fragaria vesca), the molecular mechanisms underlying auxin biosynthesis and crosstalk in octoploid strawberry fruit development are not fully characterized. To address this knowledge gap, comprehensive transcriptomic analyses were conducted at different stages of fruit development and compared between the achene and receptacle to identify developmentally regulated auxin biosynthetic genes and transcription factors during the fruit ripening process. Similar to wild diploid strawberry, octoploid strawberry accumulates high levels of auxin in achene compared to receptacle. Consistently, genes functionating in auxin biosynthesis and conjugation, such as TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS (TAAs), YUCCA (YUCs), and GRETCHEN HAGEN 3 (GH3s) were found to be primarily expressed in the achene, with low expression in the receptacle. Interestingly, several genes involved in auxin transport and signaling like PIN-FORMED (PINs), AUXIN/INDOLE-3-ACETIC ACID proteins (Aux/IAAs), TRANSPORT INHIBITOR RESPONSE 1 / AUXIN-SIGNALING F-BOX (TIR/AFBs) and AUXIN RESPONSE FACTOR (ARFs) were more abundantly expressed in the receptacle. Moreover, by examining DEGs and their transcriptional profiles across all six developmental stages, we identified key auxin-related genes co-clustered with transcription factors from the NAM-ATAF1,2-CUC2/ WRKYGQK motif (NAC/WYKY), BASIC REGION/ LEUCINE ZIPPER motif (bZIP), and APETALA2/Ethylene Responsive Factor (AP2/ERF) groups. These results elucidate the complex regulatory network of auxin biosynthesis and its intricate crosstalk within the achene and receptacle, enriching our understanding of fruit development in octoploid strawberries.

developmental biology↗

bioRxiv2024

Massively parallel interrogation of human functional variants modulating cancer immunosurveillance

Anti-PD-1/PD-L1 immune checkpoint blockade (ICB) therapy has revolutionized clinical cancer treatment, while abnormal PD-L1 or HLA-I expression in patients can significantly impact the therapeutic efficacy. Somatic mutations in cancer cells that modulate these critical immune regulators are closely associated with tumor progression and ICB response. However, a systematic interpretation of cancer immune-related mutations is still lacking. Here, we harnessed the ABEmax system to establish a large-scale sgRNA library encompassing approximately 820,000 sgRNAs that target all feasible Serine/Threonine/Tyrosine residues across the human genome, which systematically unveiled thousands of novel mutations that decrease or augment PD-L1 or HLA-I expression. Notably, we revealed functional mutations that co-regulate PD-L1 and HLA-I expression, represented by the clinically relevant mutation SETD2_Y1666, and verified that it can benefit from immunotherapy in vivo. Our findings generate an unprecedented resource of functional residues regulating cancer immunosurveillance, meanwhile, offer valuable guidance for clinical diagnosis, ICB therapy, and the development of innovative drugs in cancer treatment.

cancer biology↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗

bioRxiv2024

A rat model of cirrhosis with well-differentiated hepatocellular carcinoma induced by thioacetamide

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, and commonly associated with hepatic fibrosis or cirrhosis. This study aims to establish a rat model mimicking the progression from liver fibrosis to cirrhosis and subsequently to HCC using thioacetamide (TAA). We utilized male Lewis rats, treating them with intra-peritoneal injections of TAA. These rats received bi-weekly injections of either 200 mg/kg TAA or saline (as a control) over a period of 34 weeks. The development of cirrhosis and hepatocarcinogenesis was monitored through histopathological examinations, biochemical markers, and immunohistochemical analyses. Our results demonstrated that chronic TAA administration induced cirrhosis and well-differentiated HCC, characterized by increased fibrosis, altered liver architecture, and enhanced hepatocyte proliferation. Biochemical analyses revealed significant alterations in liver function markers, including elevated alpha-fetoprotein (AFP) levels, without affecting kidney function or causing significant weight loss or mortality in rats. This TAA-induced cirrhosis and HCC rat model successfully replicates the clinical progression of human HCC, including liver function impairment and early-stage liver cancer characteristics. It presents a valuable tool for future research on the mechanisms of antitumor drugs in tumor initiation and development.

pathology↗

bioRxiv2024

Unlocking TAS2R14 activation through intricate multi-ligand binding networks

Bitter taste receptors, particularly TAS2R14, play central roles in discerning a wide array of bitter substances, ranging from dietary components to pharmaceutical agents1-3. In addition, TAS2R14 has broad expression in non-gustatory tissues, suggesting its important roles in selective physiological processes and therapeutic potential4. Here, we present cryo-electron microcopy structures of TAS2R14 in complex with flufenamic acid and aristolochic acid, coupling with G protein subtypes gustducin and Gi. Distinct from most known GPCRs, agonists of TAS2R14 bind to multiple intracellular pockets. We highlight the cholesterol molecules occupying an upper transmembrane site, typically the orthosteric pocket in other GPCRs, also binding in the entrance to an intracellular agonist binding pocket, suggesting an endogenous modulatory function5. The structural and mutagenesis analysis illuminate the receptors broad-spectrum ligand recognition and activation via intricate multiple ligand-binding sites. Furthermore, we unveil the structural configuration of gustducin and its interaction with TAS2R14, as well as the coupling mode of Gi1. This investigation should be instrumental in advancing our knowledge of the mechanisms underlying bitter taste recognition and response, with broader relevance to sensory biology and pharmacology.

biophysics↗

bioRxiv2024

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

evolutionary biology↗

bioRxiv2024

Engineering probiotic Escherichia coli Nissle 1917 to block transfer of multiple antibiotic resistance genes by exploiting a type I CRISPR-Cas system

Many multidrug-resistant (MDR) bacteria evolved through accumulation of antibiotic-resistance genes (ARGs). Although the potential risk of probiotics as reservoirs of ARGs has been recognized, strategies for blocking transfer of ARGs while using probiotics have rarely been explored. The probiotic Escherichia coli Nissle 1917 (EcN) has long been used for treating intestinal diseases. Here, we showed frequent transfer of ARGs into EcN both in vitro and in vivo, raising its potential risk of accumulating antibiotic resistance. Given that no CRISPR-Cas system is found in natural EcN, we integrated the endogenous type I-E CRISPR-Cas system derived from E. coli BW25113 into EcN, and showed that the engineered EcN was able to efficiently cleave multiple ARGs (i.e., mcr-1, blaNDM-1 and tet(X)). By co-incubation of EcN expressing Cas3-Cascade and that expressing Cas9, we showed that the growth of the former strain outcompeted the latter strain, demonstrating better clinical application prospect of EcN expressing the type I-E CRISPR-Cas system. Finally, the engineered EcN exhibited immunity against transfer of targeted ARGs in the intestine of a model animal (i.e. zebrafish). Our work provides a new strategy for restricting transfer of ARGs in EcN, paving the way for safe use of this probiotic and development of probiotics as living therapeutics.

microbiology↗

bioRxiv2024

Linkage equilibrium between rare mutations

Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination, and discuss their implications for the rates of horizontal gene transfer in bacteria.

evolutionary biology↗

bioRxiv2024

Targeting Fatty Acid Desaturase I Inhibits Renal Cancer Growth Via ATF3-mediated ER Stress Response

Monounsaturated fatty acids (MUFAs) play a pivotal role in maintaining endoplasmic reticulum (ER) homeostasis, an emerging hallmark of cancer. However, the role of polyunsaturated fatty acid (PUFAs) desaturation in persistent ER stress driven by oncogenic abnormalities remains elusive. Fatty Acid Desaturase 1 (FADS1) is a rate-limiting enzyme controlling the bioproduction of long-chain PUFAs. Our previous research has demonstrated the significant role of FADS1 in cancer survival, especially in kidney cancers. We explored the underlying mechanism in this study. We found that pharmacological inhibition or knockdown of the expression of FADS1 effectively inhibits renal cancer cell proliferation and induces cell cycle arrest. The stable knockdown of FADS1 also significantly inhibits tumor formation in vivo. Mechanistically, we show that while FADS1 inhibition induces ER stress, its expression is also augmented by ER-stress inducers. Notably, FADS1-inhibition sensitized cellular response to ER stress inducers, providing evidence of FADS1s role in modulating the ER stress response in cancer cells. We show that, while FADS1 inhibition-induced ER stress leads to activation of ATF3, ATF3-knockdown rescues the FADS1 inhibition-induced ER stress and cell growth suppression. In addition, FADS1 inhibition results in the impaired biosynthesis of nucleotides and decreases the level of UPD-N-Acetylglucosamine, a critical mediator of the unfolded protein response. Our findings suggest that PUFA desaturation is crucial for rescuing cancer cells from persistent ER stress, supporting FADS1 as a new therapeutic target.

cancer biology↗

bioRxiv2024

Deep learning based genomic breeding of pest-resistant grapevine

Crop pests have profoundly deleterious effects on crop yield and food security. However, conventional pest control depends heavily on the utilization of insecticides, which develops strong pesticide resistance and concerns of food safety. Crop and their wild relatives display diverse levels of pest resistance, indicating the feasibility for breeding of pest-resistant crop varieties. In this study, we integrate deep learning (DL)/machine learning (ML) algorithms, plant phenomics and whole genome sequencing (WGS) data to conduct genomic selection (GS) of pest-resistance in grapevine. We employ deep convolutional neural networks (DCNN) to accurately calculate the severity of damage by pests on grape leaves, which achieves a classification accuracy of 95.3% (Visual Geometry Group 16, VGG16, for binary trait) and a correlation coefficient of 0.94 in regression analysis (DCNN with Pest Damage Score, DCNN-PDS, for continuous trait). We apply DL models to predict and integrate phenotype (both binary and continuous) along with WGS data from 231 grape accessions, conducting Genome-Wide Association Studies (GWAS). This analysis detects a total of 69 QTLs, encompassing 139 candidate genes involved in pathways associated with pest resistance, including jasmonic acid (JA), salicylic acid (SA), ethylene, and other related pathways. Furthermore, through the combination with transcriptome data, we identify specific pest-resistant genes, such as ACA12 and CRK3, which play distinct roles in resisting herbivore attacks. Machine learning-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting the leaf area damaged by pests as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

plant biology↗

bioRxiv2024

Circular RNA circASH1L(4,5) protects microRNA-129-5p from target-directed microRNA degradation in human skin wound healing

Both circular RNAs (circRNA) and microRNAs (miRNA) have emerged to play important roles in health and disease. To understand their function in tissue repair, we profiled circRNA, linear RNA, and miRNA expression dynamics in human wound-edge keratinocytes across the wound healing process. Our investigation spotlighted circASH1L(4,5) and its engagement with miR-129-5p, both of which levels were increased with wound repair. Unlike conventional miRNA sponging, circASH1L enhanced miR-129 stability and silencing activity by protecting this miRNA from target-directed miRNA degradation (TDMD) triggered by NR6A1 mRNA. TGF-{beta} signaling, pivotal in wound healing, fostered circASH1L expression while suppressing NR6A1, thus enhancing the miR-129 abundance at the post-transcriptional level. Functionally, circASH1L and miR-129 enhanced keratinocyte migration and proliferation, crucial for re-epithelialization of human wounds. Collectively, our study uncovers circRNAs novel role as shields for miRNAs and sheds light on the physiological importance of regulated miRNA degradation in human skin wound healing.

molecular biology↗

bioRxiv2024

Engineered Migrasomes: A Robust, Thermally Stable Vaccination Platform

The increasing ability of pathogens and tumor cells to evade immune detection underscores the urgent need for novel vaccine platforms leveraging diverse biological mechanisms. Additionally, logistical challenges associated with cold-chain transportation significantly limit vaccine accessibility, especially in resource-limited regions. Recently, we identified migrasomes, specialized organelles generated during cell migration, which are inherently stable and enriched with immune-modulating molecules. To address the low yield of natural migrasomes, we engineered migrasome-like vesicles (eMigrasomes) using hypotonic shock combined with cytoskeletal disruption to enhance vesicle formation. The biogenesis of eMigrasomes relies on the core migrasome machinery, faithfully recapitulating the biophysical attributes of native migrasomes while significantly improving production efficiency. We demonstrate that eMigrasomes loaded with a model antigen elicit potent antibody responses and maintain structural integrity and immunogenic potential at room temperature. Furthermore, eMigrasomes displaying the SARS-CoV-2 Spike protein induce robust humoral immune responses, providing effective protection against viral infection. Our findings highlight the potential of utilizing migrasome biology and hypotonic shock-driven vesicle generation as an innovative, stable, and broadly accessible vaccine platform.

cell biology↗

bioRxiv2024

Interpretable antibody-antigen interaction prediction by bridging structure to sequence

With the application of personalized and precision medicine, more precise and efficient antibody drug development technology is urgently needed. Identification of antibody-antigen interactions is crucial to antibody engineering. The time-consuming and expensive nature of wet-lab experiments calls for efficient computational methods. Taking into account the non-overlapping advantage of current structure-dependent and sequence-only computational methods, we propose an interpretable antibody-antigen interaction prediction method, S3AI. The introduction of structural knowledge, combined with explicit modeling of chemical rules, establishes a sequence-to-function route in S3AI, thereby facilitating its perception of intricate molecular interactions through providing route and priors guidance. S3AI significantly and comprehensively outperforms the state-of-the-art models and exhibits excellent generalization when predicting unknown antibody-antigen pairs, surpassing specialized prediction methods designed for out-of-distribution generalization in fair comparisons. More importantly, S3AI captures the universal pattern of antibody-antigen interactions, which not only identifies the CDRs responsible for specific binding to the antigen but also unearths the importance of CDR-H3 for the interaction. Structure-free design and superior performance make S3AI ideal for large-scale, parallelized antibody optimization and screening, enabling the rapid and precise identification of promising candidates within the extensive antibody space.

bioinformatics↗

bioRxiv2024

DNA-based ForceChrono Probes for Deciphering Single-Molecule Force Dynamics in Living Cells

Accurate measurement of mechanical forces in cells is key to understanding how cells sense and respond to mechanical stimuli, a central aspect of mechanobiology. However, accurately quantifying dynamic forces at the single-molecule level in living cells is a significant challenge. Here, weve developed the DNA-based ForceChrono probe to enable in-depth studies of integrin force dynamics at the single-molecule level in living cells. By illuminating two distinct mechanical points and circumventing the inherent fluctuations of single-molecule fluorescence, the ForceChrono probe enables analysis of the complex dynamics of mechanical forces at the single-molecule level, such as loading rates and durations. Our results refine previous broad estimates of cellular loading rates to a more precise range of 0.5 to 2 pN/s, shedding light on the specifics of cellular mechanics. In addition, this study reveals a critical link between the magnitude and duration of integrin forces, consistent with the catch-bond behavior demonstrated in vitro. The ForceChrono probe has distinct advantages, such as precise analysis of single-molecule force dynamics and robust resistance to fluorescence fluctuations, which will significantly advance our understanding of cell adhesion and mechanotransduction at the single-molecule level.

biophysics↗

bioRxiv2024

Development of alcoholic liver disease model for drug evaluation from human induced pluripotent stem cell derived liver organoids

Alcoholic liver disease (ALD) poses a significant health challenge, demanding comprehensive research efforts to enhance our comprehension and treatment strategies. However, the development of effective treatments is hindered by the limitations of existing liver disease models. Liver organoids, characterized by their cellular complexity and three-dimensional (3D) tissue structure closely resembling the human liver, hold promise as ideal models for liver disease research. In this study, we employ a meticulously designed protocol involving the differentiation of human induced pluripotent stem cells (hiPSCs) into liver organoids. This process incorporates a precise combination of cytokines and small molecule compounds within a 3D culture system to guide the differentiation process. Subsequently, these differentiated liver organoids are subjected to ethanol treatment to induce ALD, thus establishing a disease model. Rigorous assessment through a series of experiments reveals that this model partially replicates key pathological features observed in clinical ALD, including cellular mitochondrial damage, elevated cellular reactive oxygen species (ROS) levels, fatty liver, and hepatocyte necrosis. In addition, this model offers potential utility in screening drugs for ALD treatment. Taken together, the liver organoids model of ALD, derived from hiPSCs differentiation, emerges as an invaluable platform for advancing our understanding and management of ALD in clinical settings.

cell biology↗

bioRxiv2024

Cas1 mediates the interference stage in a phage-encoded CRISPR-Cas system

CRISPR-Cas systems are prokaryotic adaptive immune systems against invading phages and other mobile genetic elements, which function in three stages: adaptation, expression and interference. Interestingly, phages were also found to encode CRISPR-Cas systems to antagonize their hosts and establish infection. The Vibrio cholerae-infecting ICP1 phage was the first discovered phage with a functional CRISPR-Cas system against an antiphage region in the host genome. Nevertheless, this system lacks a domain essential for recruitment of helicase-nuclease Cas2/3 during target DNA cleavage, and how this system accomplishes the interference stage remains unknown. Here, surprisingly, we found that Cas1, a highly conserved component known to exclusively work in the adaptation stage, also mediates the interference stage through connecting Cas2/3 to the DNA bound-Cascade (Csy) complex of the ICP1 CRISPR-Cas system. A series of structures of Csy, Csy-dsDNA, Cas1-Cas2/3 and Csy-dsDNA-Cas1-Cas2/3 complexes collectively reveal the whole process of Cas1-mediated target DNA cleavage by the ICP1 CRISPR-Cas system. Together, these data support an unprecedented model in which Cas1 mediates the interference stage in a phage-encoded CRISPR-Cas system and also shed light on a unique model of primed adaptation.

microbiology↗

bioRxiv2024

Scalable summary statistics-based heritability estimation method with individual genotype level accuracy

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale datasets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. While the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared to other summary statistic methods and on par with methods that rely on individual-level data.

genomics↗