Browsing by Author "Yao, Bing"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    5-hydroxymethylcytosine is dynamically regulated during forebrain organoid development and aberrantly altered in Alzheimer’s disease
    (Cell Press, 2021-04-27) Kuehner, Janise N.; Chen, Junyu; Bruggeman, Emily C.; Wang, Feng; Li, Yangping; Xu, Chongchong; McEachin, Zachary T.; Li, Ziyi; Chen, Li; Hales, Chadwick M.; Wen, Zhexing; Yang, Jingjing; Yao, Bing; Medicine, School of Medicine
    5-hydroxymethylcytosine (5hmC) undergoes dynamic changes during mammalian brain development, and its dysregulation is associated with Alzheimer's disease (AD). The dynamics of 5hmC during early human brain development and how they contribute to AD pathologies remain largely unexplored. We generate 5hmC and transcriptome profiles encompassing several developmental time points of healthy forebrain organoids and organoids derived from several familial AD patients. Stage-specific differentially hydroxymethylated regions demonstrate an acquisition or depletion of 5hmC modifications across developmental stages. Additionally, genes concomitantly increasing or decreasing in 5hmC and gene expression are enriched in neurobiological or early developmental processes, respectively. Importantly, our AD organoids corroborate cellular and molecular phenotypes previously observed in human AD brains. 5hmC is significantly altered in developmentally programmed 5hmC intragenic regions in defined fetal histone marks and enhancers in AD organoids. These data suggest a highly coordinated molecular system that may be dysregulated in these early developing AD organoids.
  • Thumbnail Image
    Item
    Accurate identification of circRNA landscape and complexity reveals their pivotal roles in human oligodendroglia differentiation
    (BMC, 2022-02-07) Li, Yangping; Wang, Feng; Teng, Peng; Ku, Li; Chen, Li; Feng, Yue; Yao, Bing; Biostatistics and Health Data Science, School of Medicine
    Background: Circular RNAs (circRNAs), a novel class of poorly conserved non-coding RNAs that regulate gene expression, are highly enriched in the human brain. Despite increasing discoveries of circRNA function in human neurons, the circRNA landscape and function in developing human oligodendroglia, the myelinating cells that govern neuronal conductance, remains unexplored. Meanwhile, improved experimental and computational tools for the accurate identification of circRNAs are needed. Results: We adopt a published experimental approach for circRNA enrichment and develop CARP (CircRNA identification using A-tailing RNase R approach and Pseudo-reference alignment), a comprehensive 21-module computational framework for accurate circRNA identification and quantification. Using CARP, we identify developmentally programmed human oligodendroglia circRNA landscapes in the HOG oligodendroglioma cell line, distinct from neuronal circRNA landscapes. Numerous circRNAs display oligodendroglia-specific regulation upon differentiation, among which a subclass is regulated independently from their parental mRNAs. We find that circRNA flanking introns often contain cis-regulatory elements for RNA editing and are predicted to bind differentiation-regulated splicing factors. In addition, we discover novel oligodendroglia-specific circRNAs that are predicted to sponge microRNAs, which co-operatively promote oligodendroglia development. Furthermore, we identify circRNA clusters derived from differentiation-regulated alternative circularization events within the same gene, each containing a common circular exon, achieving additive sponging effects that promote human oligodendroglia differentiation. Conclusions: Our results reveal dynamic regulation of human oligodendroglia circRNA landscapes during early differentiation and suggest critical roles of the circRNA-miRNA-mRNA axis in advancing human oligodendroglia development.
  • Thumbnail Image
    Item
    circMeta: a unified computational framework for genomic feature annotation and differential expression analysis of circular RNAs
    (Oxford University Press, 2020-01-15) Chen, Li; Wang, Feng; Bruggeman, Emily C.; Li, Chao; Yao, Bing; Medicine, School of Medicine
    Motivation: Circular RNAs (circRNAs), a class of non-coding RNAs generated from non-canonical back-splicing events, have emerged to play key roles in many biological processes. Though numerous tools have been developed to detect circRNAs from rRNA-depleted RNA-seq data based on back-splicing junction-spanning reads, computational tools to identify critical genomic features regulating circRNA biogenesis are still lacking. In addition, rigorous statistical methods to perform differential expression (DE) analysis of circRNAs remain under-developed. Results: We present circMeta, a unified computational framework for circRNA analyses. circMeta has three primary functional modules: (i) a pipeline for comprehensive genomic feature annotation related to circRNA biogenesis, including length of introns flanking circularized exons, repetitive elements such as Alu elements and SINEs, competition score for forming circulation and RNA editing in back-splicing flanking introns; (ii) a two-stage DE approach of circRNAs based on circular junction reads to quantitatively compare circRNA levels and (iii) a Bayesian hierarchical model for DE analysis of circRNAs based on the ratio of circular reads to linear reads in back-splicing sites to study spatial and temporal regulation of circRNA production. Both proposed DE methods without and with considering host genes outperform existing methods by obtaining better control of false discovery rate and comparable statistical power. Moreover, the identified DE circRNAs by the proposed two-stage DE approach display potential biological functions in Gene Ontology and circRNA-miRNA-mRNA networks that are not able to be detected using existing mRNA DE methods. Furthermore, top DE circRNAs have been further validated by RT-qPCR using divergent primers spanning back-splicing junctions.
  • Thumbnail Image
    Item
    Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood
    (Elsevier, 2022-10-23) Chen, Li; Saykin, Andrew J.; Yao, Bing; Zhao, Fengdi; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomarkers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the historical DNA methylation data and the performance is further improved by including all temporal DNA methylation data. Availability:: https://github.com/lichen-lab/MTAE.
  • Thumbnail Image
    Item
    Tet2 loss leads to hypermutagenicity in haematopoietic stem/progenitor cells
    (SpringerNature, 2017-04-25) Pan, Feng; Wingo, Thomas S.; Zhao, Zhigang; Gao, Rui; Makishima, Hideki; Qu, Guangbo; lin, Li; Yu, Miao; Ortega, Janice R.; Wang, Jiapeng; Nazha, Aziz; Chen, Li; Yao, Bing; Liu, Can; Chen, Shi; Weeks, Ophelia; Ni, Hongyu; Phillips, Brittany Lynn; Huang, Suming; Wang, Jianlong; He, Chuan; Li, Guo-Min; Radivoyevitch, Tomas; Aifantis, Iannis; Maciejewski, Jaroslaw P.; Yang, Feng-Chun; Jin, Peng; Xu, Mingjiang; Department of Pediatrics, School of Medicine
    TET2 is a dioxygenase that catalyses multiple steps of 5-methylcytosine oxidation. Although TET2 mutations frequently occur in various types of haematological malignancies, the mechanism by which they increase risk for these cancers remains poorly understood. Here we show that Tet2-/- mice develop spontaneous myeloid, T- and B-cell malignancies after long latencies. Exome sequencing of Tet2-/- tumours reveals accumulation of numerous mutations, including Apc, Nf1, Flt3, Cbl, Notch1 and Mll2, which are recurrently deleted/mutated in human haematological malignancies. Single-cell-targeted sequencing of wild-type and premalignant Tet2-/- Lin-c-Kit+ cells shows higher mutation frequencies in Tet2-/- cells. We further show that the increased mutational burden is particularly high at genomic sites that gained 5-hydroxymethylcytosine, where TET2 normally binds. Furthermore, TET2-mutated myeloid malignancy patients have significantly more mutational events than patients with wild-type TET2. Thus, Tet2 loss leads to hypermutagenicity in haematopoietic stem/progenitor cells, suggesting a novel TET2 loss-mediated mechanism of haematological malignancy pathogenesis.
  • Thumbnail Image
    Item
    WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
    (Oxford University Press, 2021) Wang, Ye; Jiang, Yuchao; Yao, Bing; Huang, Kun; Liu, Yunlong; Wang, Yue; Qin, Xiao; Saykin, Andrew J.; Chen, Li; Biostatistics and Health Data Science, School of Medicine
    Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first 'context-free' mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict regulatory variants of unknown and diverse context. The second 'context-dependent' mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.