Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 433 records · Page 24Linked to original sources

bioRxiv2019

Angiocrine FSTL1 insufficiency leads to atrial and vein wall fibrosis via SMAD3 activation

Angiocrine factors, mediating the endothelial-mural cell interaction in vascular wall construction as well as maintenance, are incompletely characterized. Here we show that loss of follistatin-like protein 1 (FSTL1) in endothelial cells (Fstl1ECKO) led to an increase of pulmonary vascular resistance, resulting in the heart regurgitation especially with tricuspid valves. However, this abnormality was not detected in mutant mice with Fstl1 deletion in smooth muscle cells or hematopoietic cells. We further showed that there was excessive alpha-smooth muscle actin (SMA) associated with atrial endocardia, heart valves, veins and microvessels after the endothelial FSTL1 deletion. Consistently, there was an increase of collagen deposition as demonstrated in livers of Fstl1ECKO mutants. The SMAD3 phosphorylation was significantly enhanced and pSMAD3 staining was colocalized with SMA in vein walls, suggesting the activation of TGF{beta} signaling in vascular mural cells of Fstl1ECKO mice. The findings imply that endothelial FSTL1 is critical for the homeostasis of atria and veins and its insufficiency may favor cardiovascular fibrosis leading to heart failure.

developmental biology↗

bioRxiv2019

Designer DNA architecture offers precise and multivalent spatial pattern-recognition for viral sensing and inhibition

DNA, when folded into nanostructures of customizable shapes, is capable of spacing and arranging external ligands in a desired geometric pattern with nanometer-precision. This allows DNA to serve as an excellent, biocompatible scaffold for complex spatial pattern-recognizing displays. In this report, we demonstrate that a templated designer DNA nanostructure achieves multi-ligand display with precise spatial pattern-recognition, representing a unique strategy in synthesizing potent viral sensors and inhibitors. Specifically, a star-shaped DNA architecture, carrying five molecular beacon-like motifs, was constructed to display ten dengue virus envelope protein domain-III (ED3)-binding aptamers into a 2D pattern precisely matching the pentagonal arrangement of ED3 clusters on the dengue viral surface. The resulting spatial pattern recognition and multivalent interactions achieve high dengue-binding avidity, conferring direct, highly-sensitive, facile, low-cost, and rapid sensing as well as potent viral inhibition capability. Our molecular-platform design strategy could be adapted to detect and combat other disease-causing pathogens, including bacteria and microbial-toxins, by generating the requisite ligand patterns on customized DNA nanoarchitectures.

bioengineering↗

bioRxiv2019

Metabolite sequestration enables rapid recovery from nutrient depletion

Microbes adapt their metabolism to take advantage of nutrients in their environment. Such adaptations control specific metabolic pathways to match energetic demands with nutrient availability. Upon depletion of nutrients, rapid pathway recovery is key to release cellular resources required for survival in the new nutritional condition. Yet little is known about the regulatory strategies that microbes employ to accelerate pathway recovery in response to nutrient depletion. Using the fatty acid catabolic pathway in Escherichia coli, here we show that fast recovery can be achieved by rapid release of a transcriptional regulator from a metabolite-sequestered complex. With a combination of mathematical modelling and experiments, we show that recovery dynamics depend critically on the rate of metabolite consumption and the exposure time to nutrient. We constructed strains with re-wired transcriptional regulatory architectures that highlight the metabolic benefits of negative autoregulation over constitutive and positive autoregulation. Our results have wide-ranging implications for our understanding of metabolic adaptations, as well as guiding the design of gene circuitry for synthetic biology and metabolic engineering.

systems biology↗

bioRxiv2019

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (n=1,980), we predicted 34,797 PAIs which showed strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites were enriched in enhancers or near expression QTLs. Genes whose promoters were involved in PAIs were more actively expressed, and gene pairs with promoter-promoter interactions were enriched for co-expression. Integration of the predicted PAIs with GWAS data highlighted interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

genetics↗

bioRxiv2019

An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT

The deconvolution of transcriptomic data from heterogeneous tissues in cancer studies remains challenging. Available software faces difficulties for accurately estimating both component-specific proportions and expression profiles for individual samples. To address these challenges, we present a new R-implementation pipeline for the more accurate and efficient transcriptome deconvolution of high dimensional data from mixtures of more than two components. The pipeline utilizes the computationally efficient DeMixT R-package with OpenMP and additional cancer-specific biological information to perform three-component deconvolution without requiring data from the immune profiles. It enables a wide application of DeMixT to gene expression datasets available from cancer consortium such as the Cancer Genome Atlas (TCGA) projects, where, other than the mixed tumor samples, a handful of normal samples are profiled in multiple cancer types. We have applied this pipeline to two TCGA datasets in colorectal adenocarcinoma (COAD) and prostate adenocarcinoma (PRAD). In COAD, we found varying distributions of immune proportions across the Consensus Molecular Subtypes, from the highest to the lowest being CMS1, CMS3, CMS4 and CMS2. In PRAD, we found the immune proportions are associated with progression-free survival (p<0.01) and negatively correlated with Gleason scores (p<0.001). Our DeMixT-centered analysis protocol opens up new opportunities to investigate the tumor-stroma-immune microenvironment, by providing both proportions and component-specific expressions, and thus better define the underlying biology of cancer progression.\n\nAvailability and implementation: An R package, scripts and data are available: https://github.com/wwylab/DeMixTallmaterials.

bioinformatics↗

bioRxiv2019

Identification of CP77 as the third orthopoxvirus SAMD9L inhibitor with a unique specificity for a rodent SAMD9L

Orthopoxviruses (OPXVs) have a broad host range in mammalian cells, but Chinese hamster ovary (CHO) cells are non-permissive for vaccinia virus (VACV). Here, we revealed a species-specific difference in host restriction factor SAMD9L as the cause for the restriction and identified orthopoxvirus CP77 as a unique inhibitor capable of antagonizing Chinese hamster SAMD9L (chSAMD9L). Two known VACV inhibitors of SAMD9 and SAMD9L (SAMD9&L), K1 and C7, can bind human and mouse SAMD9&L, but neither can bind chSAMD9L. CRISPR-Cas9 knockout of chSAMD9L from CHO cells removed the restriction for VACV, while ectopic expression of chSAMD9L imposed the restriction for VACV in a human cell line, demonstrating that chSAMD9L is a potent restriction factor for VACV. Contrary to K1 and C7, cowpox virus CP77 can bind chSAMD9L and rescue VACV replication in cells expressing chSAMD9L, indicating that CP77 is yet another SAMD9L inhibitor but has a unique specificity for chSAMD9L. Binding studies showed that the N-terminal 382 amino acids of CP77 were sufficient for binding chSAMD9L and that both K1 and CP77 target a common internal region of SAMD9L. Growth studies with nearly all OPXV species showed that the ability of OPXVs in antagonizing chSAMD9L correlates with CP77 gene status and that a functional CP77 ortholog was maintained in many OPXVs, including monkeypox virus. Our data suggest that species-specific difference in rodent SAMD9L poses a barrier for cross-species OPXV infection and that OPXVs have evolved three SAMD9L inhibitors with different specificities to overcome this barrier. IMPORTANCESeveral OPXV species, including monkeypox virus and cowpox virus, cause zoonotic infection in humans. They are believed to use wild rodents as the reservoir or intermediate hosts, but the host or viral factors that are important for OPXV host range in rodents are unknown. Here, we showed that the abortive replication of several OPXV species in a Chinese hamster cell line was caused by a species-specific difference in the host antiviral factor SAMD9L, indicating that SAMD9L divergence in different rodent species poses a barrier for cross-species OPXV infection. While the Chinese hamster SAMD9L could not be inhibited by two previously identified OPXV inhibitors of human and mouse SAMD9L, it can be inhibited by cowpox virus CP77, indicating that OPXVs encode three SAMD9L inhibitors with different specificity. Our data suggest that OPXV host range in broad rodent species depends on three SAMD9L inhibitors with different specificities.

microbiology↗

bioRxiv2019

HBEGF+ macrophages identified in rheumatoid arthritis promote joint tissue invasiveness and are reshaped differentially by medications

Macrophages tailor their function to the signals found in tissue microenvironments, taking on a wide spectrum of phenotypes. In human tissues, a detailed understanding of macrophage phenotypes is limited. Using single-cell RNA-sequencing, we define distinct macrophage subsets in the joints of patients with the autoimmune disease rheumatoid arthritis (RA), which affects ~1% of the population. The subset we refer to as HBEGF+ inflammatory macrophages is enriched in RA tissues and shaped by resident fibroblasts and the cytokine TNF. These macrophages promote fibroblast invasiveness in an EGF receptor dependent manner, indicating that inflammatory intercellular crosstalk reshapes both cell types and contributes to fibroblast-mediated joint destruction. In an ex vivo tissue assay, the HBEGF+ inflammatory macrophage is targeted by several anti-inflammatory RA medications, however, COX inhibition redirects it towards a different inflammatory phenotype that is also expected to perpetuate pathology. These data highlight advances in understanding the pathophysiology and drug mechanisms in chronic inflammatory disorders can be achieved by focusing on macrophage phenotypes in the context of complex interactions in human tissues. One Sentence SummaryA newly identified human macrophage phenotype from patients with the autoimmune condition RA is found to promote joint tissue invasiveness and demonstrates variable sensitivities to anti-inflammatory medications used to treat the disease.

immunology↗

bioRxiv2019

Deciphering tea tree chloroplast and mitochondrial genomes of Camellia sinensis var. assamica

Tea is the most popular non-alcoholic caffeine-containing and the oldest beverage in the world. Despite its enormous industrial, cultural and medicinal values, the chloroplast (cp) and mitochondrial (mt) genomes are not available for Camellia sinensis var. assamica. In this study, we de novo assembled the cp genome sequence of C. sinensis var. assamica into a circular contig of 157,100 bp in length with an overall GC content of 37.29%, comprising a large single-copy region (LSC, 86,649 bp) and a small single-copy region (SSC, 18,285 bp) separated by a pair of inverted repeats (IRs, 26,083 bp). We annotated a total of 141 cp genes, of which 87 are protein-coding genes, 46 are tRNA genes, and eight are rRNA genes. We also de novo assembled the mt genome of C. sinensis var. assamica into two complete circular scaffolds (702,253 bp and 178,082 bp) with overall GC contents of 45.63% and 45.81%, respectively. We annotated a total of 71 mt genes, including 44 protein-coding genes, 24 tRNAs, and 3 rRNAs. Comparative analysis suggests repeat-rich nature of the mt genome compared to the cp genome, for example, with the characterization of 37,878 bp and 149 bp of long repeat sequences and 665 and 214 SSRs, respectively. We also detected 478 RNA-editing sites in 42 protein-coding mt genes, which are [~]4.4-fold more than 54 RNA-editing sites detected in 21 protein-coding cp genes. The high-quality cp and mt genomes of C. sinensis var. assamica presented in this study will become an invaluable resource for a range of genetic, functional, evolutionary and comparative genomic studies in tea tree and other Camellia species of the Theaceae family.

genomics↗

bioRxiv2019

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank, and identified 75 significant vQTLs with P<2.0x10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

genetics↗

bioRxiv2018

Garcinia cambogia extract removes calcium oxalate kidney stones in both genetic and non-genetic Drosophila models of nephrolithiasis

Kidney stone formers with family history have a high rate of stone recurrence after kidney stone removal surgery and there is no effective medication available for treatment. Here, we show that Garcinia cambogia extract (GCE) efficiently removes calcium oxalate kidney stones from Malpighian tubules in both genetic and non-genetic Drosophila models of nephrolithiasis, and hydroxycitrate -a major component of GCE, directly dissolves calcium oxalate stones in Drosophila Malpighian tubules ex vivo. Our study discovers a potential novel therapeutic strategy for the clinical treatment of nephrolithiasis and suggests that clinical-grade Garcinia cambogia extract could be used to treat patients with nephrolithiasis in the future.

pharmacology and toxicology↗

bioRxiv2018

DNA microscopy: Optics-free spatio-genetic imaging by a stand-alone chemical reaction

Analyzing the spatial organization of molecules in cells and tissues is a cornerstone of biological research and clinical practice. However, despite enormous progress in profiling the molecular constituents of cells, spatially mapping these constituents remains a disjointed and machinery-intensive process, relying on either light microscopy or direct physical registration and capture. Here, we demonstrate DNA microscopy, a new imaging modality for scalable, optics-free mapping of relative biomolecule positions. In DNA microscopy of transcripts, transcript molecules are tagged in situ with randomized nucleotides, labeling each molecule uniquely. A second in situ reaction then amplifies the tagged molecules, concatenates the resulting copies, and adds new randomized nucleotides to uniquely label each concatenation event. An algorithm decodes molecular proximities from these concatenated sequences, and infers physical images of the original transcripts at cellular resolution. Because its imaging power derives entirely from diffusive molecular dynamics, DNA microscopy constitutes a chemically encoded microscopy system.

bioengineering↗

bioRxiv2018

Ancestry-agnostic estimation of DNA sample contamination from sequence reads

Detecting and estimating DNA sample contamination are important steps to ensure high quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes.\n\nWe propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele-frequencies projected from reference genotypes onto principal component coordinates. We demonstrate this method robustly and accurately estimates contamination rates across different populations and contamination rates. We further demonstrate that in the presence contamination, quantitative estimates of genetic ancestry (e.g. principal component coordinates) can be substantially biased if contamination is ignored, and that our proposed method corrects for this bias. Our method is publicly available at http://github.com/Griffan/verifyBamID

bioinformatics↗

bioRxiv2018

Physiological and transcriptomic analysis of yellow leaf

As important deciduous tree, Populus deltoides Marsh possesses a high ornamental value for its leaves remaining yellow during the non-dormant period. However, little is known about the regulatory mechanism of leaf coloration in Populus deltoides Marsh. Thus, we analyzed physiological and transcriptional differences of yellow leaves (mutant) and green leaves (wild-type) of Populus deltoides Marsh. Physiological experiments showed that the contents of chlorophyll (Chl) and carotenoid are lower in mutant, the flavonoid content is not differed significantly between mutant and wild-type. Transcriptomic sequencing was further used to identify 153 differentially expressed genes (DEGs). Functional classifications based on Gene Ontology enrichment and Genomes enrichment analysis indicated that the DEGs were involved in Chl biosynthesis and flavonoid biosynthesis pathway. Among these, geranylgeranyl diphosphate (CHLP) genes associated with Chl biosynthesis showed down-regulation, while chlorophyllase (CLH) genes associated with Chl degradation were up-regulated in yellow leaves. The expression levels of these genes were further confirmed using quantitative real-time PCR (RT-qPCR). Furthermore, the measurement of the main precursors of Chl confirmed that CHLP is vital enzymes for the yellow leaf color phenotype. Consequently, the formation of yellow leaf color is due to disruption of Chl synthesis and catabolism rather than flavonoid content. These results contribute to our understanding of mechanisms and regulation of leaf color variation in poplar at the transcriptional level.

bioinformatics↗

bioRxiv2018

Fast, sensitive, and flexible integration of single cell data with Harmony

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of[~] 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.

bioinformatics↗

bioRxiv2018

Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

Extracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.\n\nSignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling cant tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

bioengineering↗

bioRxiv2018

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗

bioRxiv2018

OSCA: a tool for omic-data-based complex trait analysis

The rapid increase of omic data in the past decades has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we proposed a mixed-linear-model-based method (called MOMENT) that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for the effects of unobserved confounders as well as the correlations between distal probes induced by the confounders. We demonstrated by simulations that MOMENT showed a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package (called OSCA) together with a number of other implementations for omic-data-based analysis including the estimation of variance in a trait captured by all measures of multiple omic profiles, omic-data-based quantitative trait locus (xQTL) analysis, and meta-analysis of xQTL data.

bioinformatics↗

bioRxiv2018

CRISPR-Cas9 Screens Reveal Genes Regulating a G0-like State in Human Neural Progenitors

Single cell RNA-seq has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From this data, we created a cell cycle classifier, which, in addition to traditional cell cycle phases, also identifies a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. This state, Neural G0, is enriched for expression of quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. For gliomas, Neural G0 cell populations and gene expression is significantly associated with less aggressive tumors and extended patient survival. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuro-epithelial derived cells and gliomas.

cell biology↗