Biology subjects

Xu, Z.

Publications and source records attributed to Xu, Z..

At least 307 records · Page 17Linked to original sources

bioRxiv2023

PerturbSci-Kinetics: Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens

Here we described PerturbSci-Kinetics, a novel combinatorial indexing method for capturing three-layer single-cell readout (i.e., whole transcriptomes, nascent transcriptomes, sgRNA identities) across hundreds of genetic perturbations. Through PerturbSci-Kinetics profiling of pooled CRISPR screens targeting a variety of biological processes, we were able to decipher the complexity of RNA regulations at multiple levels (e.g., synthesis, processing, degradation), and revealed key regulators involved in miRNA and mitochondrial RNA processing pathways. Our technique opens the possibility of systematically decoding the genome-wide regulatory network underlying RNA temporal dynamics at scale and cost-effectively.

genomics↗

bioRxiv2023

The Effect of Electrical Resonance in Neurons upon the Instability of Electrical Nerve Stimulations

Repetitive electrical nerve stimulation can induce a long-lasting perturbation of the axons membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanisms underlying stimulus-induced instability is still an open question due to the lack of a proper theoretical model. This study proposes a new method based on a Circuit-Probability theory to reveal the interlinkages between the electrical resonance of neurons and the instability of neural response. Supported by in vivo investigations, this new model predicts several key characteristics of stimulus-induced instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons.

neuroscience↗

bioRxiv2023

Telomerase-independent survival leads to a mosaic of complex subtelomere rearrangements in Chlamydomonas reinhardtii

Telomeres and subtelomeres, the genomic regions located at chromosome extremities, are essential for genome stability in eukaryotes. In the absence of the canonical maintenance mechanism provided by telomerase, telomere shortening induces genome instability. The landscape of the ensuing genome rearrangements is not accessible by short-read sequencing. Here, we leverage Oxford Nanopore Technologies long-read sequencing to survey the extensive repertoire of genome rearrangements in telomerase mutants of the model green microalga Chlamydomonas reinhardtii. In telomerase mutant strains grown for [~]700 generations, most chromosome extremities were capped by short telomere sequences that were either recruited de novo from other loci or maintained in a telomerase-independent manner. Other extremities did not end with telomeres but only with repeated subtelomeric sequences. The subtelomeric elements, including rDNA, were massively rearranged and involved in breakage-fusion-bridge cycles, translocations, recombinations and chromosome circularization. These events were established progressively over time and displayed heterogeneity at the subpopulation level. New telomere-capped extremities composed of sequences originating from more internal genomic regions were associated with high DNA methylation, suggesting that de novo heterochromatin formation contributes to restore chromosome end stability in C. reinhardtii. The diversity of alternative strategies to maintain chromosome integrity and the variety of rearrangements found in telomerase mutants are remarkable and illustrate genome plasticity at short timescales.

genomics↗

bioRxiv2022

Retrieving the near-complete genome of a threatened bird from wild frozen samples

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Collecting samples from frozen dead animals in the wild provides an alternative approach to obtaining high-quality nucleic acids. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) of a recent death found in the mountainous region in Gansu, China. We generated a near-complete genome assembly (OTswu) having only 18 gaps scattering in 8 out of the 40 assembled chromosomes. Unlikely most other bird genome assemblies, OTswu contains complete chromosome models (2n = 80). We demonstrated that the great bustard genome likely retained the ancestral avian karyotype. We also characterized the DNA methylation landscapes of OTswu which are strongly correlated with GC content and gene expression. Our phylogenomic analysis suggested Otidiformes and Musophagiformes were sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard was found the lowest among the four available Otidiformes genomes, possibly due to population declines during past glacial periods. As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriching for metabolism. Finally, we identified an extremely young evolutionary stratum on the sex chromosome, a rare case among Neoaves. Together, our study combining long-read sequencing and RNA-seq technology provides a working strategy for conducting multi-omic analyses for threatened species by retrieving high-quality nucleic acids from dead animals frozen in the wild.

genomics↗

bioRxiv2022

Investigation of a mathematical model describing global cancer growth and treatment: An inhomogeneous model based on the generalized logistic equation

Tumor growth is manifestation of the evolution of a complex system. Researchers have limited scope of modelling studies on specific aspects or stages of the process. It has led to unsatisfactory explanation of clinical observations. We hereby demonstrated that an inhomogeneous model built on the generalized logistic equation could serve better. It was developed to describe the whole process of tumor progression, clinically observed independence of index tumor growth from spread of the disease and growth deceleration during early stage of solid tumors. It was validated by simulating the coexistence of exponential and sigmoidal growth in chronic lymphocytic leukaemia, theories of tumor heterogeneity, as well as by accommodating notions pertaining to tumor treatment and prognosis. We thought therefore it was an interesting and not unjustifiable description of actual tumor growth in human body and hoped it might encourage more researchers to look at tumor modelling from a clinical perspective.

cancer biology↗

bioRxiv2022

Cell-type-specific co-expression inference from single cell RNA-sequencing data

The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimers disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.

bioinformatics↗

bioRxiv2022

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Using neuroimaging, connectome, transcriptome, and computational modeling, we mapped cortical thinning patterns primarily located in lateral frontal and parietal heteromodal nodes during childhood and adolescence, which is structurally constrained by white matter network architecture and is particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible while using another independent dataset. Our findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

neuroscience↗

bioRxiv2022

Global soil metagenomics reveals ubiquitous yet previously-hidden predominance of Deltaproteobacteria in nitrogen-fixing microbiome

Biological nitrogen fixation is a fundamental process sustaining all life on earth. In the soil, Alphaproteobacteria, Betaproteobacteria, and Cyanobacteria are traditionally considered the major groups of nitrogen fixation population. This conventional knowledge has been accumulated by numerous PCR amplicon surveys for nitrogenase genes. However, this prevalent view appears to be inconsistent with results of recent PCR-free meta-omics investigations. Here, we show the global distribution and diversity of terrestrial diazotrophic microbiome, updated by fully leveraging the planetary collections of soil metagenomes along with recently expanded culture collections. After extensive analysis of 1,445 soil metagenomic samples, we proved that the previously-undervalued Anaeromyxobacteraceae and Geobacteraceae within Deltaproteobacteria are ubiquitous and predominant groups of diazotrophic microbiome in the soils, spanning a broad spectrum of environments, with different geographic origins and land usage types, including aerobic (croplands, grasslands) and anaerobic ones (paddy soils, sediments). Our results delineate a revised scheme of soil nitrogen fixation, which is inclusive of both conventional diazotrophs and the so far undervalued Deltaproteobacteria.

microbiology↗

bioRxiv2022

ZNF524 directly interacts with telomeric DNA and supports telomere integrity

Telomeres are nucleoprotein structures at the ends of linear chromosomes. In humans, they consist of TTAGGG repeats, which are bound by dedicated proteins such as the shelterin complex. This complex blocks unwanted DNA damage repair at telomeres, e.g. by suppressing non-homologous end joining (NHEJ) through its subunit TRF2. We here describe ZNF524, a zinc finger protein that directly binds telomeric repeats with nanomolar affinity and reveal the base-specific sequence recognition by co-crystallization with telomeric DNA. ZNF524 localizes to telomeres and specifically maintains the presence of the TRF2/RAP1 subcomplex at telomeres without affecting other shelterin members. Loss of ZNF524 concomitantly results in an increase in DNA damage signaling and recombination events. Overall, ZNF524 is a direct telomere-binding protein involved in the maintenance of telomere integrity.

cell biology↗

bioRxiv2022

Study of Pinelliae Rhizoma hepatotoxicity based on complex network algorithm improvement

Important protein identification methods based on centrality have reached a high level of accuracy. However, there is a need to improve centrality algorithms because they currently consider the nature of protein-protein interaction (PPI) network topology but not protein properties. To improve the centrality algorithm, we introduce the weighted PageRank algorithm, which represents node importance, and the protein interaction combined_score, which represents PPI network edge importance in the STRING database to construct a weighted PPI network. We constructed yeast protein networks for simulations to validate the improved algorithm. Finally, we studied the hepatotoxicity of Pinelliae Rhizoma by applying the PageRank and Edge Clustering (PEC) algorithm. Our study shows that the PageRank and Edge Clustering algorithm can pre-screen important targets and has superior accuracy, sensitivity, and specificity to other centrality algorithms.

pharmacology and toxicology↗

bioRxiv2022

NaWRKY3 is a master transcriptional regulator of defense network against Alternaria alternata in Nicotiana attenuata

WRKY transcription factors are involved in plant defense responses against pathogens. However, no WRKYs have been reported yet in resistance of Nicotiana species to Alternaria alternata, a necrotrophic fungal pathogen causing brown spot disease. Here, we found that silencing NaWRKY3 lead to wild tobacco Nicotiana attenuata highly susceptible to A. alternata. Combination of transcriptome, electrophoretic mobility shift, ChIP-qPCR and dual-LUC analyses, we uncovered that NaWRKY3 bound to many defense genes promoter and activated their expression. Target genes included: 1) lipoxygenases 3, ACC synthase 1 and ACC oxidase 1, three key enzyme genes for JA and ethylene biosynthesis which were critical for A. alternata resistance; 2) feruloyl-CoA 6-hydroxylase 1 (NaF6H1), the key enzyme gene for phytoalexins against A. alternata, scopoletin and scopolin; and 3) three A. alternata resistance genes, long non-coding RNA (LncRNA L2), NADPH oxidase (NaRboh D) and berberine bridge-like (NaBBL28). Silencing LncRNA L2 reduced A. alternata-induced levels of JA and NaF6H1 expression. NaRboh D-silenced plants were strongly impaired in ROS production and stomata closure responses. NaBBL28 was the first A. alternata resistance BBLs identified and was involved in HGL-DTGs hydroxylation. Finally, NaWRKY3 could bind to its own promoter but acted as a transcriptional repressor. Thus we demonstrated that NaWRKY3 is a fine-tuned master regulator of defense network against A. alternata in N. attenuata by regulating different signaling pathways and defense metabolites. For the first time, such an important WRKY was identified in Nicotiana species, providing new insight into defense mechanism of Nicotiana plants to A. alternata.

molecular biology↗

bioRxiv2022

Using Attention-based Deep Learning to Predict ERG:TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome including gene fusion between TMPRSS2 and members of the ETS family of transcription factors specially ERG. The expanding wealth of pathology whole slide images (WSIs) and the increasing adoption of deep learning (DL) approaches offer a unique opportunity for pathologists to streamline the detection of ERG:TMPRSS2 fusion status. Here, we used two large cohorts of digitized H&E-stained slides from radical prostatectomy specimens to train and evaluate a DL system capable of detecting the ERG fusion status and also detecting tissue regions of high diagnostic and prognostic relevance. Slides from the PCa TCGA dataset were split into training (n=318), validation (n=59), and testing sets (n=59) with the training and validation sets being used for training the model and optimizing its hyperparameters, respectively while the testing set was used for evaluating the performance. Additionally, we used an internal testing cohort consisting of 314 WSIs for independent assessment of the models performance. The ERG prediction model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.72 and 0.73 in the TCGA testing set and the internal testing cohort, respectively. In addition to slide-level classification, we also identified highly attended patches for the cases predicted as either ERG-positive or negative which had distinct morphological features associated with ERG status. We subsequently characterized the cellular composition of these patches using HoVer-Net model trained on the PanNuke dataset to segment and classify the nuclei into five main categories. Notably, a high ratio of neoplastic cells in the highly-attended regions was significantly associated with shorter overall and progression-free survival while high ratios of immune, stromal and stromal to neoplastic cells were all associated with longer overall and metastases-free survival. Our work highlights the utility of deploying deep learning systems on digitized histopathology slides to predict key molecular alteration in cancer together with their associated morphological features which would streamline the diagnostic process.

pathology↗

bioRxiv2022

Spatial Localization Ability of Planarians Identified Through the Light Maze Paradigm

Spatial localization ability is crucial for free-living animals to fit the environment. As shown by previous studies, planarians can be conditioned to discriminate directions. However, due to their simplicity and primitiveness, they had never been considered to have true spatial localization ability to retrieve locations of objects and places in the environment. Here, we introduce a light maze training paradigm to demonstrate that a planarian worm can navigate to a former recognized place from the start point, even if the worm is transferred into a newly produced maze. This finding identifies the spatial localization ability of planarians for the first time, which provides clues for the evolution of spatial learning. Since the planarians have a primitive brain with simple structures, this paradigm can also provide a simplified model for a detailed investigation of spatial learning.

animal behavior and cognition↗

bioRxiv2022

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data

With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (CCIs) from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. To address the issue, here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively.

bioinformatics↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

Discovery of benzophenanthridine derivatives with potent activity against multidrug resistant Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the pathogen responsible for tuberculosis (TB), is the leading cause of bacterial disease-related death worldwide. Current antibiotic regimens for the treatment of TB remain dated and suffer from long treatment times as well as the development of drug-resistance. As such, the search for novel chemical modalities that have selective or potent anti-Mtb properties remains an urgent priority, particularly against multidrug resistant (MDR) Mtb strains. Herein, we design and synthesize 35 novel benzo[c]phenanthridine derivatives (BPD). The two most potent compounds, BPD-6 and BPD-9, accumulated within the bacterial cell and exhibited strong inhibitory activity (MIC90 [~] 2-10 M) against multiple Mycobacterium strains, while remaining inactive against a range of other Gram-negative and Gram-positive bacteria. BPD-6 and BPD-9 were also effective in reducing Mtb viability within infected macrophages. The two BPD compounds displayed comparable efficacy to rifampicin, a critical frontline antibiotic used for the prevention and treatment of TB. Importantly, BPD-6 and BPD-9 inhibited the growth of multiple MDR Mtb clinical isolates, suggesting a completely novel mechanism of action compared to existing frontline TB dugs. The discovery of BPDs provides novel chemical scaffolds for anti-TB drug discovery. TOC/GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/515485v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1536acaorg.highwire.dtl.DTLVardef@1881e83org.highwire.dtl.DTLVardef@18b2f58org.highwire.dtl.DTLVardef@d15f1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

A Model of High-Speed Endovascular Sonothrombolysis with Vortex Ultrasound-Induced Shear Stress to Treat Cerebral Venous Sinus Thrombosis

This research aims to demonstrate a novel vortex ultrasound enabled endovascular thrombolysis method designed for treating cerebral venous sinus thrombosis (CVST). This is a topic of significant importance since current treatment modalities for CVST still fail in as many as 20-40% of the cases and the incidence of CVST has increased since the outbreak of the COVID-19 pandemic. Compared with conventional anticoagulant or thrombolytic drugs, sonothrombolysis has the potential to remarkably shorten the required treatment time owing to the direct clot targeting with acoustic waves. However, previously reported strategies for sonothrombolysis have not demonstrated clinically meaningful outcomes (e.g., recanalization within 30 minutes) in treating large, completely occluded veins or arteries. In this paper, we demonstrated a new vortex ultrasound technique for endovascular sonothrombolysis utilizing wave-matter interaction-induced shear stress to enhance the lytic rate substantially. Our in vitro experiment showed that the lytic rate was increased by at least 64.3 % compared with the nonvortex endovascular ultrasound treatment. A 3.1 g, 7.5 cm long, completely occluded in vitro 3D model of acute CVST was fully recanalized within 8 minutes with a record-high lytic rate of 237.5 mg/min for acute bovine clot in vitro. Furthermore, we confirmed that the vortex ultrasound causes no vessel wall damage over ex vivo bovine veins. This vortex ultrasound thrombolysis technique potentially presents a new life-saving tool for severe CVST cases that cannot be efficaciously treated using existing therapies.

bioengineering↗

bioRxiv2022

Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents

Predictions on bioactivities of herbs and herbal compounds could greatly aid drug development, but require integration of available information on herb and compound effects from various databases. We present Herb-paths, an integrated network connecting information on health-beneficial effects of herbs and herbal constituents. The statistical models included in Herb-paths allow for the calculation of the association strength of herbal (constituents) with health-beneficial effects, for both known and novel effects, and give insight into the major bioactive compounds and molecular mechanisms driving the effects. Herb-paths predictions were tested and validated using a case-study of Panax notoginseng and its derived medicinal extract of Panaxatriol saponins (PTS), used in traditional Chinese medicine to treat stroke and other cardiovascular and cerebrovascular thromboembolic (CCT) disorders. Results showed that Herb-paths predicted known and novel associations between PTS/Notoginseng and CCT phenotypes and diseases, including stroke. Predicted novel associations, such as MoyaMoya disease, are promising putative leads for treatment with PTS/Notoginseng, to be tested in further studies.

bioinformatics↗