Biology subjects

Shao, W.

Publications and source records attributed to Shao, W..

50 records · Page 3Linked to original sources

bioRxiv2020

Integration in or Near Oncogenes Plays Only a Minor Role in Determining the in Vivo Distribution of HIV Integration Sites Before or During Suppressive Antiretroviral Therapy

HIV persists during antiretroviral therapy (ART) as integrated proviruses in cells descended from a small fraction of the CD4+ T cells infected prior to the initiation of ART. To better understand what controls HIV persistence and the distribution of integration sites (IS), we compared about 16,000 and 54,000 IS from individuals pre-ART and on ART, respectively, with approximately 385,000 IS from PBMC infected in vitro. The distribution of IS in vivo is quite similar to the distribution in PBMC, modified by selection against proviruses in expressed genes, by selection for proviruses integrated into one of 6 specific genes, and by clonal expansion. The clones in which a provirus integrated in an oncogene contributed to the survival of the clone comprise only a small fraction of the clones that persist in HIV-infected individuals on ART. Mechanisms that do not involve the provirus, or its location in the host genome, are more important in determining which clones expand and persist. Author SummaryIn HIV-infected individuals, a small fraction of the infected cells persist and divide. This reservoir persists on ART and can rekindle the infection if ART is discontinued. Because the number of possible sites of HIV DNA integration is very large, each infected cell, and all of its descendants, can be identified by the site where the provirus is integrated (IS). To understand the selective forces that determine the fates of infected cells in vivo, we compared the distribution of HIV IS in freshly-infected cells to cells from HIV-infected donors sampled both before and during ART. We found that, as has been previously reported, integration favors highly-expressed genes. However, over time the fraction of cells with proviruses integrated in highly-expressed genes decreases, implying that they grow less well. There are exceptions to this broad negative selection. When a provirus is integrated in a specific region in one of six genes, the proviruses affect the expression of the target gene, promoting growth and/or survival of the cell. Although this effect is striking, it is only a minor component of the forces that promote the growth and survival of the population of infected cells during ART.

microbiology↗

bioRxiv2020

Early Emergence and Long-Term Persistence of HIV-Infected T Cell Clones in Children

Little is known about the emergence and persistence of HIV-infected T cell clones in perinatally-infected children. We analyzed peripheral blood mononuclear cells for clonal expansion in 11 children who initiated antiretroviral therapy (ART) between 1.8-17.4 months of age and with viremia suppressed for 6-9 years. We obtained 8,662 HIV-1 integration sites from pre-ART and 1,861 sites on ART. Expanded clones of infected cells were detected pre-ART in 10/11 children. In 8 children, infected cell clones detected pre-ART persisted for 6-9 years on ART. A comparison of integration sites in the samples obtained on ART with healthy donor PBMC infected ex-vivo showed selection for cells with proviruses integrated in BACH2 and STAT5B. Our analyses indicate that, despite marked differences in T cell composition and dynamics between children and adults, HIV-infected cell clones are established early in children, persist for up to 9 years on ART, and can be driven by proviral integration in proto-oncogenes.

microbiology↗

bioRxiv2020

Transcript assembly improves expression quantification of transposable elements in single cell RNA-seq data

Transposable elements (TEs) are an integral part of the host transcriptome. TE-containing noncoding RNAs (ncRNAs) exhibit considerable tissue specificity and play crucial roles during development, including stem cell maintenance and cell differentiation. Recent advances in single cell RNA-seq (scRNA-seq) revolutionized cell-type specific gene expression analysis. However, scRNA-seq quantification tools tailored for TEs are lacking, limiting our ability to dissect TE expression dynamics at single cell resolution. To address this issue, we established a TE expression quantification pipeline that is compatible with scRNA-seq data generated across multiple technology platforms. We constructed TE containing ncRNA references using bulk RNA-seq data and demonstrated that quantifying TE expression at the transcript level effectively reduces noise. As proof of principle, we applied this strategy to mouse embryonic stem cells and successfully captured the expression profile of endogenous retroviruses in single cells. We further expanded our analysis to scRNA-seq data from early stages of mouse embryogenesis. Our results illustrated the dynamic TE expression at pre-implantation stages and revealed 137 TE-containing ncRNA transcripts with substantial tissue specificity during gastrulation and early organogenesis.

genomics↗

bioRxiv2020

MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification

ABSTRACTTo fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis for multiple types of omics data. We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification. MORONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MORONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from a wide range of biomedical classification applications using mRNA expression data, DNA methylation data, and miRNA expression data. Furthermore, MORONET is able to identify important biomarkers from different omics data types that are related with the investigated diseases.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Wide field-of-view volumetric imaging by a mesoscopic scanning oblique plane microscopy with switchable objective lens

Conventional light sheet fluorescence microscopy (LSFM), or selective plane illumination microscopy (SPIM), enables high resolution 3D imaging over a large volume by using two orthogonally aligned objective lenses to decouple excitation and emission. The recent development of oblique plane microscopy (OPM) simplifies LSFM design with only one single objective lens, by using off-axis excitation and remote focusing. However, most reports on OPM has a limited microscopic field of view (FOV), typically within 1×1 mm2. Our goal is to overcome the limitation with a new variant of OPM to achieve mesoscopic FOV. We implemented an optical design of mesoscopic scanning OPM to allow using low numerical aperture (NA) objective lens. The angle of the intermediate image before the remote focusing system was increased by a demagnification under Scheimpflug condition such that the light collecting efficiency in the remote focusing system was significantly improved. We characterized the 3D resolutions and FOV by imaging fluorescence microspheres, and demonstrated the volumetric imaging on intact whole zebrafish larvae, mouse cortex, and multiple Caenorhabditis elegans (C. elegans). We demonstrate a mesoscopic FOV up to ~6× 5×0.6 mm3 volumetric imaging, the largest reported FOV by OPM so far. The angle of the intermediate image plane is independent of the magnification. As a result, the system is highly versatile, allowing simple switching between different objective lenses with low (10x, NA 0.3) and median NA (20x, NA 0.5). Detailed microvasculature in zebrafish larvae, mouse cortex, and neurons in C. elegans are clearly visualized in 3D. The proposed mesoscopic scanning OPM allows using low NA objective such that centimeter-level FOV volumetric imaging can be achieved. With the extended FOV, simple sample mounting protocol, and the versatility of changeable FOVs/resolutions, our system will be ready for the varieties of applications requiring in vivo volumetric imaging over large length scales.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2020

Diagnostic Evidence GAuge of Single cells (DEGAS): A transfer learning framework to infer impressions of cellular and patient phenotypes between patients and single cells

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimers Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on newly generated myeloma single cell transcriptomics led to the identification of PHF19high myeloma cells associated with progression.

bioinformatics↗

bioRxiv2020

Visible light optical coherence tomography angiography (vis-OCTA) and local microvascular retinal oximetry in human retina

We report herein the first visible light optical coherence tomography angiography (vis-OCTA) for human retinal imaging. Compared to the existing vis-OCT systems, we devised a spectrometer with a narrower bandwidth to increase the spectral power density for OCTA imaging, while retaining the major spectral contrast in the blood. We achieved a 100 kHz A-line rate, the fastest acquisition speed reported so far for human retinal vis-OCT. We rigorously optimized the imaging protocol such that a single acquisition takes <6 seconds with a field of view (FOV) of 3x7.8 mm2. The angiography enables accurate localization of microvasculature down to the capillary level and thus enables oximetry at vessels < 100 m in diameter. We demonstrated microvascular hemoglobin oxygen saturation (sO2) at the feeding and draining vessels at the perifoveal region. The longitudinal repeatability was assessed by <5% coefficient of variation (CV). The unique capabilities of our vis-OCTA system may allow studies on the role of microvascular oxygen in various retinal pathologies.

bioengineering↗

bioRxiv2020

A deep learning approach for tissue spatial quantification and genomic correlations of histopathological images

Epithelial and stromal tissue are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. We propose an image analysis pipeline based on a Convolutional Neural Network (CNN) model to classify epithelial and stromal regions in whole-slide images. The CNN model was trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) project. Our model achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generated pixel-level epithelial/stromal tissue maps for 1,000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimated the epithelial and stromal ratios and performed correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology enrichment analyses of genes that were highly correlated with tissue ratios suggest the same tissue was associated with similar biological processes in different breast cancer subtypes, whereas each subtype had its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic data and biological processes for all types of solid tumors.

bioinformatics↗

bioRxiv2019

HIV-DRLink: A tool for detecting linked HIV-1 drug resistance mutations in next generation sequencing data

The prevalence of HIV-1 drug resistance is increasing worldwide and monitoring its emergence is important for the successful management of populations receiving combination antiretroviral therapy (cART). Using Ultrasensitive Single-Genome Sequencing (uSGS), a next-generation method that avoids PCR bias and PCR recombination, a recent report showed that pre-existing dual-class drug resistance mutations linked on the same viral genomes were predictive of treatment failure while unlinked mutations were not. Because of the large numbers of sequences generated by uSGS and other next-generation sequencing methods, it is difficult to assess each sequence individually for linked resistance mutations. Several software/programs exist to report the frequencies of individual mutations in large datasets but they provide no information on their linkage. Here, we report the HIV-DRLink program, a research tool that provides mutation frequencies in the total dataset as well as their linkage to other mutations conferring resistance to the same or different drug classes. The HIV-DRLink program should only be used on datasets generated by methods that eliminate artifacts due to PCR recombination, for example, standard Single-Genome Sequencing (SGS) or Ultrasensitive Single-Genome Sequencing (uSGS). HIV-DRLink is exclusively a research tool and is not intended to inform clinical decisions.

bioinformatics↗

bioRxiv2019

Volumetric fluorescence imaging in a human eye model by oblique scanning laser ophthalmoscope (oSLO): a feasibility study

Fluorescence retinal imaging, such as fluorescein angiography, indocyanine green angiography, and autofluorescence imaging, are valuable tools in ophthalmology and vision science. However, these clinical imaging modalities provide en face view of the retina, with limited capability to discriminate retinal layers over a large field-of-view (FOV). We recently developed a novel retinal imaging method, oblique scanning laser ophthalmoscopy (oSLO), to provide volumetric retinal fluorescence imaging without any depth sectioning. OSLO breaks the coaxial alignment of the excitation and detection, to produce a cross-sectional view on retina using the natural ocular optics. In this paper, we demonstrated oSLO in a realistic human eye model and showed the feasibility for future in vivo human retinal imaging. A new optical design was implemented to significantly simplify our previous oSLO systems. We overcame the limitation by the small numerical aperture (NA) of the human eye, by integrating a pair of cylindrical lens in the remote focusing system. We experimentally showed that the current setup can achieve a FOV of [~]3x6x0.8 mm3, and the transverse and axial resolutions of 7 and 41 {micro}m, respectively. The capability of volumetric fluorescence imaging over a large FOV in the human retina could lead to new clinical imaging paradigms for retinal diseases.

bioengineering↗

bioRxiv2019

TSUNAMI: Translational Bioinformatics Tool Suite For Network Analysis And Mining

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It helps to discover latent gene/molecular interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus help to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream Gene Ontology (GO) enrichment analysis, as well as modules that may share common members. To address this need, we develop a GCN mining tool package TSUNAMI (Tools SUite for Network Analysis and MIning) which incorporates our state-of-the-art lmQCM algorithm to mine GCN modules in public and user-input data (microarray, RNA-seq, or any other numerical omics data), then performs downstream GO and enrichment analysis based on the modules identified. It has several features and advantages: (i) user friendly interface and the real-time co-expression network mining through web server; (ii) direct access and search of GEO and TCGA databases as well as user-input expression matrix (microarray, RNA-seq, etc.) for GCN module mining; (iii) multiple co-expression analysis tools to choose with highly flexible of parameter selection options; (iv) identified GCN modules are summarized to eigengenes, which are convenient for user to check their correlation with other clinical traits; (v) integrated downstream Enrichr enrichment analysis and links to other GO tools; (vi) visualization of gene loci by Circos plot in any step. The web service is freely accessible through URL: http://spore.ph.iu.edu:3838/zhihuan/TSUNAMI/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

bioinformatics↗

bioRxiv2019

BURMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for cell lineation and identifying bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch-Effect ReMoval Using Deep Autoencoders) -- a novel transfer-learning-based method for batch-effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

bioinformatics↗

bioRxiv2018

Differential gene expression, including Sjfs800, in Schistosoma japonicum females before, during, and after male-female pairing

Schistosomiasis is a prevalent but neglected tropical disease caused by parasitic trematodes of the genus Schistosoma, with the primary disease-causing species being S. haematobium, S. mansoni, and S. japonicum. Male-female pairing of schistosomes is necessary for sexual maturity and the production of a large number of eggs, which are primarily responsible for schistosomiasis dissemination and pathology. Here, we used microarray hybridization, bioinformatics, quantitative PCR, in situ hybridization, and gene silencing assays to identify genes that play critical roles in S. japonicum reproduction biology, particularly in vitellarium development, a process that affects male-female pairing, sexual maturation, and subsequent egg production. Microarray hybridization analyses generated a comprehensive set of genes differentially transcribed before and after male-female pairing. Although the transcript profiles of females were similar 16 and 18 days after host infection, marked gene expression changes were observed at 24 days. The 30 most abundantly transcribed genes on day 24 included those associated with vitellarium development. Among these, genes for female-specific 800 (fs800), eggshell precursor protein, and superoxide dismutase (cu-zn-SOD) were substantially upregulated. Our in situ hybridization results in female S. japonicum indicated that cu-zn-SOD mRNA was highest in the ovary and vitellarium, eggshell precursor protein mRNA was expressed in the ovary, ootype, and vitellarium, and Sjfs800 mRNA was observed only in the vitellarium, localized in mature vitelline cells. Knocking down the Sjfs800 gene in female S. japonicum by approximately 60% reduced the number of mature vitelline cells, decreased rates of pairing and oviposition, and decreased the number of eggs produced in each male-female pairing by about 50%. These results indicate that Sjfs800 is essential for vitellarium development and egg production in S. japonicum and suggest that Sjfs800 regulation may provide a novel approach for the prevention or treatment of schistosomiasis.\n\nAuthor SummarySchistosomiasis is a common but largely unstudied tropical disease caused by parasitic trematodes of the genus Schistosoma. The eggs of schistosomes are responsible for schistosomiasis transmission and pathology, and the production of these eggs is dependent on the pairing of females and males. In this study, we determined which genes in Schistosoma japonicum females were differentially expressed before and after pairing with males, identifying the 30 most abundantly expressed of these genes. Among these 30 genes, we further characterized those in female S. japonicum that were upregulated after pairing and that were related to reproduction and vitellarium development, a process that affects male-female pairing, sexual maturation, and subsequent egg production. We identified three such genes, S. japonicum female-specific 800 (Sjfs800), eggshell precursor protein, and superoxide dismutase, and confirmed that the mRNAs for these genes were primarily localized in reproductive structures. By using gene silencing techniques to reduce the amount of Sjfs800 mRNA in females by about 60%, we determined that Sjfs800 plays a key role in development of the vitellarium and egg production. This finding suggests that regulation of Sjfs800 may provide a novel approach to reduce egg counts and thus aid in the prevention or treatment of schistosomiasis.

genomics↗

bioRxiv2018

U1 snRNP regulates chromatin retention of noncoding RNAs

Thousands of noncoding transcripts exist in mammalian genomes, and they preferentially localize to chromatin. Here, to identify cis-regulatory elements that control RNA-chromatin association, we developed a high-throughput method named RNA element for subcellular localization by sequencing (REL-seq). Coupling REL-seq with random mutagenesis (mutREL-seq), we discovered a key 7-nt U1 recognition motif in chromatin-enriched RNA elements. Reporter assays indicated a direct role for U1 snRNP recognition in regulating RNA-chromatin localization. Globally, U1 motifs and U1 binding are strongly enriched in long noncoding RNA (lncRNA) transcripts. Inhibition of U1 snRNA, and of U2 to a lesser degree, led to global reduction in chromatin association of hundreds of lncRNAs. For promoter- and enhancer-associated noncoding RNAs, U1 binds to their genomic neighborhoods, and their chromatin association depends on both U1 and U2 snRNAs. These findings reveal that U1 snRNP, perhaps together with the splicing machinery, acts widely to promote the chromatin association of noncoding transcripts.

molecular biology↗