Biology subjects

Wu, W.

Publications and source records attributed to Wu, W..

318 records · Page 18Linked to original sources

bioRxiv2018

Overexpressed microRNA-141-3p enhance proliferation via targeting PLAG1 in non-diabetic macrosomia

Several studies have shown microRNAs (miRNAs) could regulate the placental development, yet the role and mechanism of miRNAs in the development of non-diabetic macrosomia (NDFMS) remains unclear. The key miRNA that abnormal expressed in NDFMS placentas was screened out by miRNA microarray and verified using qRT-PCR in 91 subjects. The effects of the key miRNA were verified by proliferation assay and invasion assay in HTR-8/SVneo cell, and also in pregnant C57BL/6J mice. miR-141-3p was determined as the key miRNA with the most significant difference, which could promote the proliferation and invasion by regulating the expression of target gene PLAG1. Overexpression of PLAG1 could reverse the effect of cell proliferation and invasion ability caused by miR-141-3p overexpression. Significant difference in fetal birth weight was observed between the control group and treated group with miR-141-3p agomir in late pregnancy, but not in early pregnancy. This study revealed miR-141-3p could increase the proliferation of placenta to participate in the occurrence and development of NDFMS through regulating PLAG1 expression.

developmental biology↗

bioRxiv2018

Coupled Mixed Model for joint genetic analysis of complex disorders from independently collected data sets: application to Alzheimer’s disease and substance use disorder

In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratication, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimers disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases.\n\nThe software is available at https://github.com/HaohanWang/CMM

genetics↗

bioRxiv2018

Regulation of neural stem cell fate by the transcriptional repressor Capicua

Capicua (Cic) is a transcriptional repressor mutated in the brain cancer oligodendroglioma. Despite its cancer link, little is known of Cics function in the brain. Here, we investigated the relationship between Cic expression and cell type specification in the brain. Cic is strongly expressed in astrocytic and neuronal lineage cells but is more weakly expressed in stem cells and oligodendroglial lineage cells. Using a new conditional Cic knockout mouse, we show that forebrain-specific Cic deletion increases proliferation and self-renewal of neural stem cells. Furthermore, Cic loss biases neural stem cells toward glial lineage selection, expanding the pool of oligodendrocyte precursor cells (OPCs). These proliferation and lineage selection effects in the developing brain are dependent on de-repression of Ets transcription factors. In patient-derived oligodendroglioma cells, CIC re-expression or ETV5 blockade decreases lineage bias, proliferation, self-renewal and tumorigenicity. Our results identify Cic is an important regulator of cell fate in neurodevelopment and oligodendroglioma, and suggest that its loss contributes to oligodendroglioma by promoting proliferation and an OPC-like identity via Ets overactivity.

cancer biology↗

bioRxiv2018

Long noncoding RNA ANRIL supports proliferation of adult T-cell leukemia cells through cooperation with EZH2

Adult T-cell leukemia (ATL) is a highly aggressive T-cell malignancy induced by human T-cell leukemia virus type 1 (HTLV-1) infection. Long noncoding RNA (lncRNA) plays a critical role in the development and progression of multiple human cancers. However, the function of lncRNA on HTLV-1-induced oncogenesis has not been elucidated. In the present study, we show that the expression of the lncRNA ANRIL was elevated in HTLV-1 infected cell lines and clinical ATL samples. E2F1 induced ANRIL transcription by enhancing its promoter activity. Knocking down of ANRIL in ATL cells repressed cellular proliferation and increased apoptosis in vitro and in vivo. As a mechanism for these actions, we found that ANRIL targeted EZH2, and activated the NF-{kappa}B pathway in ATL cells. This activation was independent of the histone methyltransferase (HMT) activity of EZH2, but required the formation of an ANRIL/EZH2/p65 ternary complex. Chromatin immunoprecipitation assay revealed that ANRIL/EZH2 enhanced p65 DNA binding capability. In addition, we observed that ANRIL/EZH2 complex repressed p21/CDKN1A transcription through H3K27 trimethylation of the p21/CDKN1A promoter. Taken together, our results implicate that lncRNA ANRIL, by cooperating with EZH2, supports the proliferation of HTLV-1 infected cells, which is thought to be critical for oncogenesis.\n\nIMPORTANCEHuman T-cell leukemia virus type 1 (HTLV-1) is the pathogen that causes adult T-cell leukemia (ATL), which is a unique malignancy of CD4+ T cells. A role for long noncoding RNA (lncRNA) in HTLV-1-mediated cellular transformation has not been described. In this study, we demonstrated that lncRNA ANRIL was important for maintaining proliferation of ATL cells in vitro and in vivo. ANRIL was shown to activate NF-{kappa}B signaling through forming a ternary complex with EZH2 and p65. Further, epigenetic inactivation of p21/CDKN1A was involved in the oncogenic function of ANRIL. To the best of our knowledge, this is the first study to address the regulatory role of the lncRNA ANRIL in ATL and provides an important clue to prevent or treat HTLV-1 associated human diseases.

cancer biology↗

bioRxiv2018

Single-cell transcriptome of antler stem cells from antlerogenic periosteum

Antler regeneration, a stem cell-based epimorphic process, has potential as a valuable model for regenerative medicine. A pool of antler stem cells (ASCs) for antler development is located in the antlerogenic periosteum (AP). However, whether this ASC pool is homogenous or heterogeneous has not been fully evaluated. In this study, we produced a comprehensive transcriptome dataset at the single-cell level for the ASCs based on the 10x Genomics platform (scRNA-seq). A total of 4,565 ASCs were sequenced and classified into a large cell cluster, indicating that the ASCs resident in the AP are likely to be a homogeneous population. The scRNA-seq data revealed that tumor-related genes were highly expressed in these homogeneous ASCs: i.e. TIMP1, TMSB10, LGALS1, FTH1, VIM, LOC110126017 and S100A4. Results of screening for stem cell markers suggest that the ASCs may be considered as a special type of stem cell between embryonic (CD9) and adult (CD29, CD90, NPM1 and VIM) stem cells. Our results provide the first comprehensive transcriptome analysis at the single-cell level for the ASCs, and identified only one major cell type resident in the AP and some key stem cell genes, which may hold the key to why antlers, the unique mammalian organ, can fully regenerate once lost.

cell biology↗

bioRxiv2017

Ennet: exert enhaner-only somatic mutations to discover potential cancer-driving biological networks

Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in the SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including a large SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and neural development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work suggests that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.

bioinformatics↗

bioRxiv2017

Influence of APOA5 locus on the treatment efficacy of three statins: evidence from a randomized pilot study in Chinese subjects

Pharmacogenetics or pharmacogenomics approaches are important for addressing the individual variabilities of drug efficacy especially in the era of precision medicine. One particular interesting gene to investigate is APOA5 which has been repeatedly linked with the inter-individual variations of serum triglycerides. Here, we explored APOA5-statin interactions in 195 Chinese subjects randomized to rosuvastatin (5-10 mg/day), atorvastatin (10-20 mg/day), or simvastatin (40 mg/day) for 12 weeks by performing a targeted genotyping analysis of the APOA5 promoter SNP rs662799 (-1131T>C). There were no significant differences between the treatment arms for any of the statin-induced changes in clinical biomarkers. Reductions in LDL cholesterol were influenced by the APOA5 genotype in all three treatment groups. By contrast, changes in HDL cholesterol and triglycerides were only affected by the APOA5 genotype in the atorvastatin and simvastatin groups and not in the rosuvastatin group. Our results support earlier findings indicating that rosuvastatin is a better treatment option and that future studies should consider stratifying subjects not only by genetic background but also by statin type.\n\nAbbreviations

clinical trials↗

bioRxiv2017

Parental allele-specific genome architecture and transcription during the cell cycle

A normal human somatic cell inherits two haploid genomes. Individual chromosomes of each pair have distinct parental origins and parental alleles are known to unequally contribute to cellular function. We integrated chromosome conformation (form) and gene transcription (function) analyses to dissect the dynamics of the maternal and paternal genomes in lymphoblastoid cells during the cell cycle. We found a distinct set of homologous alleles with very different activity often located close to boundaries of euchromatin and heterochromatin domains. We also identified a set of allele-biased topologically associating domains (TADs) that were small sized and had higher gene density. Thousands of genes show allelically biased expression (ABE) with false discovery rate < 0.05, and 98% of them have no allelic switching during G1, S, and G2/M phases. A subset of ABE genes are preferentially localized near TAD boundaries, enriched with chromatin organization transcription factor binding sites, and contained higher number of sequence variants in CCCTC-binding factor sites. Our results extend previous findings of sequence variation as a basis for unequal functional parental genomes. Investigation of haplotype-resolved form-function dynamics may further our understanding of phenotypic traits, genetic diseases, vulnerability to complex disorders, and the development of cancers.

genomics↗

bioRxiv2017

Novel transcriptional activity and extensive allelic imbalance in the human MHC region

The major histocompatibility complex (MHC) region encodes human leukocyte antigen (HLA) genes and is the most complex region in the human genome. The extensive polymorphic nature of the HLA hinders accurate localization and functional assessment of disease risk loci within this region. Using targeted capture sequencing and constructing individualized genomes for transcriptome alignment, we identified 908 novel transcripts within the human MHC region. These include 593 novel isoforms of known genes, 137 antisense strand RNAs, 119 novel long intergenic noncoding RNAs, and 5 transcripts of 3 novel putative protein coding human endogenous retrovirus genes. We revealed extensive allele-dependent expression imbalance involving 88% of all heterozygous transcribed single nucleotide polymorphisms throughout the MHC transcriptome. Further, we showed that the transcriptome within the MHC region can be defined by 14 distinct co-expression clusters, with evidence of co-regulation by unique transcription factors in at least 9 of these clusters. Our data suggest a very complex regulatory map of the human MHC, and can help uncover functional consequences of disease risk loci in this region.

genomics↗

bioRxiv2017

SRSF shape analysis for sequencing data reveal newdifferentiating patterns

MotivationSequencing-based methods to examine fundamental features of the genome, such as gene expression and chromatin structure, rely on inferences from the abundance and distribution of reads derived from Illumina sequencing. Drawing sound inferences from such experiments relies on appropriate mathematical methods to model the distribution of reads along the genome, which has been challenging due to the scale and nature of these data.\n\nResultsWe propose a new framework (SRSFseq) based on Square Root Slope Functions shape analysis to analyse Illumina sequencing data. In the new approach the basic unit of information is the density of mapped reads over region of interest located on the known reference genome. The densities are interpreted as shapes and a new shape analysis model is proposed. An equivalent of a Fisher test is used to quantify the significance of shape differences in read distribution patterns between groups of density functions in different experimental conditions. We evaluated the performance of this new framework to analyze RNA-seq data at the exon level, which enabled the detection of variation in read distributions and abundances between experimental conditions not detected by other methods. Thus, the method is a suitable supplement to the state of the are count based techniques. The variety of density representations and flexibility of mathematical design allow the model to be easily adapted to other data types or problems in which the distribution of reads is to be tested. The functional interpretation and SRSF phase-amplitude separation technique gives an efficient noise reduction procedure improving the sensitivity and specificity of the method.

bioinformatics↗

bioRxiv2017

Evolution and clinical impact of genetic epistasis within EGFR-mutant lung cancers

Introductory paragraph Introductory paragraph Main text Methods Author Contributions Author Information References The current understanding of tumorigenesis is largely centered on a monogenic driver oncogene model. This paradigm is incompatible with the prevailing clinical experience in most solid malignancies: monotherapy with a drug directed against an individual oncogenic driver typically results in incomplete clinical responses and eventual tumor progression1-7. By profiling the somatic genetic alterations present in over 2,000 cases of lung cancer, the leading cause of cancer mortality worldwide8,9, we show that combinations of functional genetic alterations, i.e. genetic collectives dominate the landscape of ...

cancer biology↗

bioRxiv2017

MinorityReport, software for generalized analysis of causal genetic variants

BackgroundThe widespread availability of next generation genome sequencing technologies has enabled a wide range of variant detection applications, especially in cancer and inborn genetic disorders. For model systems and microorganisms, the same technology may be used to discover the causative mutations for any phenotype, including those generated in response to chemical perturbation. In the case of pathogenic organisms, these approaches have allowed the determination of drug targets by means of resistance selection followed by genome sequencing.\n\nResultsHere, we present open source software written in python, MinorityReport, to facilitate the comparison of any two sets of genome alignments for the purpose of rapidly identifying the spectrum of nonsynonymous changes, insertions or deletions, and copy number variations in a presumed mutant relative to its parent. Specifically, MinorityReport relates mapped sequence reads in SAM format output from any alignment tool for both the mutant and parent genome, relative to a reference genome, and produces the set of variants that distinguishes the mutant from the parent, all presented in an intuitive, straightforward report format. MinorityReport features tunable parameters for evaluating evidence and a scoring system that prioritizes reported variants based on relative proportions of read counts supporting the variant in the mutant versus parent data sets. We demonstrate the utility of MinorityReport using publicly available data sets that we previously published to find the determinants of resistance for novel anti-malarial drugs.\n\nConclusionsMinorityReport is readily available (github: xxxxxxx) to identify the genetic mechanisms of drug resistance in plasmodium, genotype-phenotype relationships in human diads, or genomic variations between any two related organisms.

bioinformatics↗