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Zhong, S.

Publications and source records attributed to Zhong, S..

72 records · Page 4Linked to original sources
bioRxiv2020

A single-cell transcriptome atlas of the aging human and macaque retina

The human retina is a complex neural tissue that detects light and sends visual information to the brain. However, the molecular and cellular processes that underlie aging primate retina remain unclear. Here, we provide a comprehensive transcriptomic atlas based on 119,520 single cells of the foveal and peripheral retina of humans and macaques covering different ages. The molecular features of retinal cells differed between the two species, suggesting the distinct regional and species specializations of the human and macaque retinae. In addition, human retinal aging occurred in a region- and cell-type- specific manner. Aging of human retina exhibited a foveal to peripheral gradient. MYO9A- rods and a horizontal cell subtype were greatly reduced in aging retina, indicating their vulnerability to aging. Moreover, we generated a dataset showing the cell-type- and region- specific gene expression associated with 55 types of human retinal disease, which provides a foundation to understand the molecular and cellular mechanisms underlying human retinal diseases. Together, these datasets are valuable for understanding the molecular characteristics of primate retina, as well as the molecular regulation of aging progression and related diseases.

neuroscience↗
bioRxiv2020

Decoding the regulatory architecture of the maize leaf

The transcription regulatory network underlying essential and complex functionalities inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex regulatory netowrk. Here, we used ChIP-seq to determine the binding profiles of 104 TF expressed in the maize leaf. With this large dataset, we could reconstruct a transcription regulatory network that covers over 77% of the expressed genes, and reveal its scale-free topology and functional modularity like a real-world network. We found that TF binding occurs in clusters covering [~]2% of the genome, and shows enrichment for sequence variations associated with eQTLs and GWAS hits of complex agronomic traits. Machine-learning analyses were used to identify TF sequence preferences, and showed that co-binding is key for TF specificity. The trained models were used to predict and compare the regulatory networks in other species and showed that the core network is evolutionarily conserved. This study provided an extensive description of the architecture, organizing principle and evolution of the transcription regulatory network inside the plant leaf.

molecular biology↗
bioRxiv2019

Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development.

The development of single-cell RNA-Sequencing (scRNA-Seq) has allowed high resolution analysis of cell type diversity and transcriptional networks controlling cell fate specification. To identify the transcriptional networks governing human retinal development, we performed scRNA-Seq over retinal organoid and in vivo retinal development, across 20 timepoints. Using both pseudotemporal and cross-species analyses, we examined the conservation of gene expression across retinal progenitor maturation and specification of all seven major retinal cell types. Furthermore, we examined gene expression differences between developing macula and periphery and between two distinct populations of horizontal cells. We also identify both shared and species-specific patterns of gene expression during human and mouse retinal development. Finally, we identify an unexpected role for ATOH7 expression in regulation of photoreceptor specification during late retinogenesis. These results provide a roadmap to future studies of human retinal development, and may help guide the design of cell-based therapies for treating retinal dystrophies.

neuroscience↗
bioRxiv2019

Using Machine Learning and Gene Nonhomology Features to Predict Gene Ontology

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

bioinformatics↗
bioRxiv2019

Varying constraints impact the miR-6/5/4/286/3/309 microRNA cluster in Drosophila leg development

A striking feature of microRNAs is that they are often clustered in the genomes of animals. The functional and evolutionary consequences of this clustering remain obscure. Here, we investigated a microRNA cluster miR-6/5/4/286/3/309 that is conserved across drosophilid lineages. Small RNA sequencing revealed expression of this microRNA cluster in Drosophila melanogaster leg discs, and conditional overexpression of the whole cluster resulted in leg appendage shortening. Transgenic overexpression lines expressing different combinations of microRNA cluster members were also constructed. Expression of individual microRNAs from the cluster resulted in a normal wild-type phenotype, but either the expression of several ancient microRNAs together (miR-5/4/286/3/309) or more recently evolved clustered microRNAs (miR-6-1/2/3) can recapitulate the phenotypes generated by the whole-cluster overexpression. Screening of transgenic fly lines revealed down-regulation of leg patterning gene cassettes in generation of the leg-shortening phenotype. Furthermore, cell transfection with different combinations of microRNA cluster members revealed a suite of downstream genes targeted by all cluster members, as well as complements of targets that are unique for distinct microRNAs. Considering together the microRNA targets and the evolutionary ages of each microRNA in the cluster demonstrates the importance of microRNA clustering, where new members can reinforce and modify the selection forces on both the cluster regulation and the gene regulatory network of existing microRNAs.

evolutionary biology↗
bioRxiv2019

Dynamic changes in RNA-chromatin interactome promote endothelial dysfunction

Chromatins are pervasively attached by RNAs. Here, we asked whether global RNA-chromatin contacts are altered in a given cell type in a disease context, and whether these alterations impact gene expression and cell function. In endothelial cells (ECs) treated by high-glucose and TNF, we employed single-cell RNA-sequencing and in situ mapping of RNA-genome interaction (iMARGI) assay to delineate temporal changes in transcriptome and RNA-chromatin interactome. ECs displayed dramatic and heterogeneous changes in single cell transcriptome, accompanied by a dynamic and strong increase in inter-chromosomal RNA-DNA interactions, particularly among super enhancers (SEs). These SEs overlap with genes contributing to inflammatory response and endothelial mesenchymal transition (EndoMT), two key aspects of endothelial dysfunction. Perturbation of a high-glucose and TNF-activated interaction involving SEs overlapping LINC00607 and SERPINE1 attenuated the pro-inflammatory and pro-EndoMT gene induction and EC dysfunction. Our findings highlight RNA-chromatin contacts as a crucial regulatory feature in biological and disease processes, exemplified by endothelial dysfunction, a major mediator of numerous diseases.

genomics↗
bioRxiv2019

Prognostic Role of c-Met Overexpression in High Grade Glioma: a Meta-analysis

ObjectivesThis study aims to assess the relationship between the expression of c-Met and the prognosis of high grade glioma patients.\n\nMethodThe MET proto-oncogene encoded c-Met protein. The gene expression data of 325 patients were downloaded from CGGA. The Oncomine database analysis and the prognosis analysis were conducted. Besides, meta-analysis was also performed to confirm the conclusion.\n\nResultOncomine database was identified and analyzed and results showed that the MET copy number was obviously higher in glioblastoma than normal tissue consistently (p<0.001). The prognostic analysis of 325 high grade glioma samples showed that high c-Met expression patients had poor overall survival (OS) and progression free survival (PFS) than the low c-Met expression patients dramatically (HR, 2.223; 95% CI: 1.662 to 2.974; P<0.0001 and HR, 2.089; 95% CI: 1.578 to 2.770; P<0.0001). 6 studies involving 503 patients were included in the meta-analysis. The pooled results indicated that the high expression of c-Met was not significantly associated with OS (HR =1.01, 95% CI:0.93-1.09), but strongly connected with shorter PFS (HR =1.92, 95% CI:1.42-2.58, p<0.01).\n\nConclusionc-Met overexpression has correlation with poor prognosis of high grade glioma patients.

cancer biology↗
bioRxiv2019

Vascularized human cortical organoids model cortical development in vivo

Modelling the neuronal progenitor proliferation and organization processes that produce mature cortical neuron subtypes is essential for the study of human brain development and the search for potential cell therapies. To provide a vascularized and functional model of brain organoids, we demonstrated a new paradigm to generate vascularized organoids that consist of typical human cortical cell types and recapitulate the lamination of the neocortex with a vascular structure formation for over 200 days. In addition, the observation of the sEPSCs (spontaneous Excitatory Postsynaptic Potential) and sIPSCs (spontaneous Inhibitory Postsynaptic Potential) and the bidirectional electrical transmission indicated the presence of chemical and electrical synapses in the vOrganoids. More importantly, the single-cell RNA-seq analysis illustrated that the vOrganoids exhibited microenvironments to promote neurogenesis and neuronal maturation that resembled in vivo processes. The transplantation of the vOrganoids to the mouse S1 cortex showed human-mouse co-constructed functional blood vessels in the grafts that could promote the survival and integration of the transplanted cells to the host. This vOrganoid culture method could not only serve as a model to study human cortical development and to explore brain disease pathology but could also provide potential prospects for new cell therapies for neural system disorders and injury.

neuroscience↗
bioRxiv2019

EpiAlignment: alignment with both DNA sequence and epigenomic data

Comparative epigenomics, by subjecting both epigenome and genome to interspecies comparison, has become a powerful approach to reveal regulatory features of the genome. Thus elucidated regulatory features surpassed the information derived from comparison of genomic sequences alone. Here, we present EpiAlignment, a web-based tool to align genomic regions with both DNA sequence and epigenomic data. EpiAlignment takes DNA sequence and epigenomic profiles derived by ChIP-seq, DNase-seq, or ATAC-seq from two species as input data, and outputs the best semi-global alignments. These alignments are based on EpiAlignment scores, computed by a dynamic programming algorithm that accounts for both sequence alignment and epigenome similarity. For timely response, the EpiAlignment web server automatically initiates up to 140 computing threads depending on the size of user input data. For users convenience, we have pre-compiled the comparable human and mouse epigenome datasets in matched cell types and tissues from the Roadmap Epigenomics and ENCODE consortia. Users can either upload their own data or select pre-compiled datasets as inputs for EpiAlignment analyses. Results are presented in graphical and tabular formats where the entries can be interactively expanded to visualize additional features of these aligned regions. EpiAlignment is available at https://epialign.ucsd.edu/.

bioinformatics↗
bioRxiv2018

Genome-wide co-localization of RNA-DNA interactions and fusion RNA pairs

Fusion transcripts are used as biomarkers in companion diagnoses. Although more than 15,000 fusion RNAs have been identified from diverse cancer types, few common feature has been reported. Here, we compared 16,410 fusion transcripts detected in cancer (from a published cohort of 9,966 tumor samples of 33 cancer types) with genome-wide RNA-DNA interactions mapped in two normal, non-cancerous cell types (using iMARGI, an enhanced version of the MARGI [Mapping RNA-Genome Interactions assay]). Among the top 10 most significant RNA-DNA interactions in normal cells, 5 co-localized with the gene pairs that formed fusion RNAs in cancer. Furthermore, throughout the genome, the frequency of a gene pair to exhibit RNA-DNA interactions is positively correlated with the probability of this gene pair to present documented fusion transcripts in cancer. To test whether RNA-DNA interactions in normal cells are predictive of fusion RNAs, we analyzed in a validation cohort of 96 lung cancer samples using RNA-seq. We found 34 out of 42 fusion transcripts in the validation cohort exhibited RNA-DNA interactions in normal cells. Finally, combining RNA-seq, single-molecule RNA FISH, and DNA FISH, we detected a cancer sample with Eml4-Alk fusion RNA without forming the Eml4-Alk fusion gene. Collectively, these data suggest a novel RNA-poise model, where spatial proximity of RNA and DNA could poise for creation of fusion transcripts.

systems biology↗
bioRxiv2018

Single-cell RNA-seq analysis maps the development of human fetal retina

Vision starts with image formation at the retina, which contains diverse neuronal cell types that extract, process, and relay visual information to higher order processing centers in the brain. Though there has been steady progress in defining retinal cell types, very little is known about retinal development in humans, which starts well before birth. In this study, we performed transcriptomic profiling of developing human fetal retina from gestational weeks 12 to 27 using single-cell RNA-seq (scRNA-seq) and used pseudotime analysis to reconstruct the developmental trajectories of retinogenesis. Our analysis reveals transcriptional programs driving differentiation down four different cell types and suggests that Muller glia (MG) can serve as embryonic progenitors in early retinal development. In addition, we also show that transcriptional differences separate retinal progenitor cells (RPCs) into distinct subtypes and use this information to reconstruct RPC developmental trajectories and cell fate. Our results support a hierarchical program of differentiation governing cell-type diversity in the developing human retina. In summary, our work details comprehensive molecular classification of retinal cells, reconstructs their relationships, and paves the way for future mechanistic studies on the impact of gene regulation upon human retinogenesis.

neuroscience↗
bioRxiv2018

New genetic variants associated with major adverse cardiovascular events in patients with acute coronary syndromes and treated with clopidogrel and aspirin

ImportanceAlthough a few studies have reported the effects of several polymorphisms on major adverse cardiovascular events (MACE) in patients with acute coronary syndromes (ACS) and those undergoing percutaneous coronary intervention (PCI), these genotypes account for only a small fraction of the variation and evidence is insufficient. This study aims to identify new genetic variants associated with MACE by large-scale sequencing data.\n\nObjectiveTo identify the genetic variants that caused MACE.\n\nDesignAll patients in this study were allocated to dual antiplatelet therapy for up to 12 months and have the follow-up duration of 18 months.\n\nSettingA two-stage association study was performed.\n\nParticipantsWe evaluated the associations of genetic variants and MACE in 1961 patients with ACS undergoing PCI (2009-2012), including high-depth whole exome sequencing of 168 patients in the discovery cohort and high-depth targeted sequencing of 1793 patients in the replication cohort.\n\nMain Outcomes and MeasureThe primary clinical efficacy endpoint was the major adverse cardiovascular events (MACE) composite endpoint, including cardiovascular death, myocardial infarction (MI), stroke (CT or MR scan confirmed) and repeated revascularization (RR).\n\nResultsWe discovered and confirmed six new genotypes associated with MACE in patients with ACS. Of which, rs17064642 at MYOM2 increased the risk of MACE (hazard ratio [HR] 2.76; P = 2.95 x 10-9) and reached genome-wide significance. The other five suggestive variants were KRTAP10-4 (rs201441480), WDR24 (rs11640115), ECHS1 (rs140410716), AGAP3 (rs75750968) and NECAB1 (rs74569896). Notably, the expressions of MYOM2 and ECHS1 are down-regulated in both animal models and patients with phenotypes related to MACE. Importantly, we developed the first superior classifier for predicting MACE and achieved high predictive accuracy (0.809).\n\nConclusions and RelevanceWe identified six new genotypes associated with MACE and developed a superior classifier for predicting MACE. Our findings shed light on the pathogenesis of cardiovascular outcomes and may help clinician to make decision on the therapeutic intervention for ACS patients.\n\nTrial RegistrationThis study has been registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn, Registration number: ChiCTR-OCH-11001198).

genetics↗
bioRxiv2018

RNAs as proximity labeling media for identifying nuclear speckle positions relative to the genome

Nuclear speckles are interchromatin structures enriched in RNA splicing factors. Determining their relative positions with respect to the folded nuclear genome could provide critical information on co-and post-transcriptional regulation of gene expression. However, it remains challenging to identify which parts of the nuclear genome are in proximity to nuclear speckles, due to physical separation between nuclear speckle cores and chromatin. We hypothesized that noncoding RNAs including small nuclear RNAs, 7SK and Malat1, which accumulate at the periphery of nuclear speckles (nsaRNA, nuclear speckle associated RNA), may extend to sufficient proximity to the nuclear genome. Leveraging a transcriptome-genome interaction assay (MARGI), we identified nsaRNA-interacting genomic sequences, which exhibited clustering patterns (nsaPeaks) in the genome, suggesting existence of relatively stable interaction sites for nsaRNAs in nuclear genome. Posttranscriptional pre-mRNAs, which are known to be clustered to nuclear speckles, exhibited proximity to nsaPeaks but rarely to other genomic regions. Furthermore, CDK9 proteins that localize to the vicinity of nuclear speckles produced ChIP-seq peaks that overlapped with nsaPeaks. Our combined DNA FISH and immunofluorescence analysis in 182 single cells revealed a 3-fold increase in odds for nuclear speckles to localize near an nsaPeak than its neighboring genomic sequence. These data suggest a model that nsaRNAs locate in sufficient proximity to nuclear genome and leave identifiable genomic footprints, thus revealing the parts of genome proximal to nuclear speckles.

bioinformatics↗
bioRxiv2018

Development of a joint evolutionary model for the genome and the epigenome

BackgroundInterspecies epigenome comparisons yielded functional information that cannot be revealed by genome comparison alone, begging for theoretical advances that enable principled analysis approaches. Whereas probabilistic genome evolution models provided theoretical foundation to comparative genomics studies, it remains challenging to extend DNA evolution models to epigenomes.\n\nResultsWe present an effort to develop ab initio evolution models for epigenomes, by explicitly expressing the joint probability of multispecies DNA sequences and histone modifications on homologous genomic regions. This joint probability is modeled as a mixture of four components representing four evolutionary hypotheses, namely dependence and independence of interspecies epigenomic variations to sequence mutations and to sequence insertions and deletions (indels). For model fitting, we implemented a maximum likelihood method by coupling downhill simplex algorithm with dynamic programming. Based on likelihood comparisons, the model can be used to infer whether interspecies epigenomic variations depend on mutation or indels in local genomic sequences. We applied this model to analyze DNase hypersensitive regions and spermatid H3K4me3 ChIP-seq data from human and rhesus macaque. Approximately 5.5% of homologous regions in the genomes exhibited H3K4me3 modification in either species, among which approximately 67% homologous regions exhibited sequence-dependent interspecies H3K4me3 variations. Mutations accounted for less sequence-dependent H3K4me3 variations than indels. Among transposon-mediated indels, ERV1 insertions and L1 insertions were most strongly associated with H3K4me3 gains and losses, respectively.\n\nConclusionThis work initiates a class of probabilistic evolution models that jointly model the genomes and the epigenomes, thus helps to bring evolutionary principles to comparative epigenomic studies.

bioinformatics↗
bioRxiv2018

Rainbow-seq: combining cell lineage tracking with single-cell RNA sequencing in preimplantation embryos

Single-cell RNA-seq experiments cannot record cell division history and therefore cannot directly connect intercellular differences at a later developmental stage to their progenitor cells. We developed Rainbow-seq to combine cell division lineage tracing with single-cell RNA-seq. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA-seq experiment to simultaneously read single-cell transcriptomes and decode the lineage marker genes. We traced the lineages deriving from each blastomere in two-cell mouse embryos and observed inequivalent contributions to the embryonic and abembryonic poles in 72% of the blastocysts evaluated. Rainbow-seq on four- and eight-cell embryos with lineage tracing triggered at two-cell stage exhibited remarkable transcriptome-wide differences between the two cell lineages at both stages, including genes involved in negative regulation of transcription and signaling. These data provide critical insights on cell fate choices in cleavage embryos. Rainbow-seq bridged a critical gap between cellular division history and single-cell RNA-seq assays.

developmental biology↗
bioRxiv2018

GITAR: An open source tool for analysis and visualization of Hi-C data

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present GITAR (Genome Interaction Tools and Resources), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, visualization and topologically associated domains (TADs) analysis. GITAR is composed of two main modules: 1) HiCtool, a Python library to process and visualize Hi-C data, including TADs analysis and 2) Processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline which goes from the raw Hi-C data to the computation, visualization and optimized storage of intra-chromosomal contact matrices and topological domain coordinates. A large collection of standardized processed data allows to compare different datasets in a consistent way and it saves time of work to obtain data for visualization or additional analyses. GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data and it is freely available for the public at http://genomegitar.org as an open source software.

bioinformatics↗
bioRxiv2017

Building a genome browser with GIVE

Growing popularity and diversity of genomic data demands portable and versatile genome browsers. Here, we present an open source programming library, called GIVE that facilitates creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, \"linear\" quantitative data (wiggle), and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into users personal website or saved and shared with collaborators. The simplicity of use was enabled by encapsulation of novel data communication and visualization technologies, including new data structures, a memory management method, and a double layer display method. GIVE is available at: http://www.givengine.org/.

bioinformatics↗
bioRxiv2017

The 4D Nucleome Project

Summary Summary Background Overview of the goals... Structure of the 4D... Research Plans References The spatial organization of the genome and its dynamics contribute to gene expression and cellular function in normal development as well as in disease. Although we are increasingly well equipped to determine a genomes sequence and linear chromatin composition, studying the three-dimensional organization of the genome with high spatial and temporal resolution remains challenging. The 4D Nucleome Network aims to develop and apply approaches to map the structure and dynamics of the human and mouse genomes in space and time with the long term goal of gaining deeper mechanistic understanding of how the nucleus is organized. The project will develop and benchmark experimental and computational approaches for meas ...

genomics↗