Biology subjects

Jiang, R.

Publications and source records attributed to Jiang, R..

6 recordsLinked to original sources

bioRxiv2018

DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, though having successfully reported enhancers in typical cell lines, are still too costly and time consuming to perform systematic identification of enhancers specific to different cell lines under a variety of disease status. Existing computational methods, though capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the above understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. We demonstrate that our model not only consistently outperforms existing methods in the classification of enhancers against background sequences, but also accurately predicts enhancers across different cell lines. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers.

bioinformatics↗

bioRxiv2018

DeepTACT: predicting high-resolution chromatin contacts via bootstrapping deep learning

High-resolution interactions among regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanism. Hi-C technique allows the genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, current Hi-C experiments do not have high enough resolution to resolve contacts among regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts among regulatory elements. In tests based on promoter capture Hi-C data, DeepTACT is seen to offer improved resolution over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are active across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of high-resolution chromatin contact information in the study of human diseases is illustrated by the association of IFNA2 and IFNA1 to coronary artery disease via an integrative analysis of GWAS data and high-resolution contacts inferred by DeepTACT.

bioinformatics↗

bioRxiv2018

Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network

MotivationInteractions among such cis-regulatory elements as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them overlook long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating comprehensive data on high-resolution interactions between promoters and distal enhancers. On the other hand, plenty of studies have suggested that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately model gene expression.\n\nResultsWe propose DeepExpression, a densely connected convolutional neural network to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods not only in the classification of binary gene expression status but also in the regression of continuous gene expression levels, in both cross-validation experiments and cross-cell lines predictions. We show that sequential promoter information is more informative than experimental enhancer information while enhancer-promoter interactions are most beneficial from those within {+/-}100 kbp around the TSS of a gene. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures and known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.\n\nAvailabilityDeepExpression is freely available at https://github.com/wanwenzeng/DeepExpression.\n\nContactruijiang@tsinghua.edu.cn, ywang@amss.ac.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Pathogenic DDX3X mutations impair RNA metabolism and neurogenesis during fetal cortical development

De novo germline mutations in the RNA helicase DDX3X account for 1-3% of unexplained intellectual disability (ID) cases in females, and are associated with autism, brain malformations, and epilepsy. Yet, the developmental and molecular mechanisms by which DDX3X mutations impair brain function are unknown. Here we use human and mouse genetics, and cell biological and biochemical approaches to elucidate mechanisms by which pathogenic DDX3X variants disrupt brain development. We report the largest clinical cohort to date with DDX3X mutations (n=78), demonstrating a striking correlation between recurrent dominant missense mutations, polymicrogyria, and the most severe clinical outcomes. We show that Ddx3x controls cortical development by regulating neuronal generation and migration. Severe DDX3X missense mutations profoundly disrupt RNA helicase activity and induce ectopic RNA-protein granules and aberrant translation in neural progenitors and neurons. Together, our study demonstrates novel mechanisms underlying DDX3X syndrome, and highlights roles for RNA-protein aggregates in the pathogenesis of neurodevelopmental disease.

genetics↗

bioRxiv2018

Long-term all-optical interrogation of cortical neurons in awake-behaving non-human primates

Whereas optogenetic techniques have proven successful in their ability to manipulate neuronal populations in species ranging from insects to rodents--with high spatial and temporal fidelity--significant obstacles remain in their application to non-human primates (NHPs). Robust optogenetics-activated behavior and long-term noninvasive monitoring of target neurons, have been challenging in NHPs. Here we present a method for all-optical interrogation (AOI), integrating optical stimulation and simultaneous two-photon imaging of neuronal populations in the primary visual cortex (V1) of awake rhesus macaques. A red-shifted channel-rhodopsin transgene (C1V1) and genetically-encoded calcium indicators (GCaMP5 or GCaMP6s) were delivered by AAVs, and subsequently expressed in V1 neuronal populations for months with high stability. We achieved optogenetic stimulation using both single-photon (1P) activation of neuronal populations and two-photon (2P) activation of single-cells, while simultaneously recording 2P calcium imaging in awake monkeys. Optogenetic manipulations of V1 neuronal populations produced reliable artificial visual percepts. Together, our advances show the feasibility of precise and stable all-optical interrogation of cortical neurons in awake NHPs, which may facilitate broad applications in high-level cognition and pre-clinical testing studies.

neuroscience↗

bioRxiv2018

H3K4 methylation dependent and independent chromatin regulation by JHD2 and SET1 in budding yeast

Set1 and Jhd2 regulate the methylation state of histone H3 lysine-4 (H3K4me) through their opposing methyltransferase and demethylase activities in the budding yeast Saccharomyces cerevisiae. H3K4me associates with actively transcribed genes and, like both SET1 and JHD2 themselves, is known to regulate gene expression diversely. It remains unclear, however, if Set1 and Jhd2 act solely through H3K4me. Relevantly, Set1 methylates lysine residues in the kinetochore protein Dam1 while genetic studies of the S. pombe SET1 ortholog suggest the existence of non-H3K4 Set1 targets relevant to gene regulation. We interrogated genetic interactions of JHD2 and SET1 with essential genes involved in varied aspects of the transcription cycle. Our findings implicate JHD2 in genetic inhibition of the histone chaperone complexes Spt16-Pob3 (FACT) and Spt6-Spn1. This targeted screen also revealed that JHD2 inhibits the Nrd1-Nab3-Sen1 (NNS) transcription termination complex. We find that while Jhd2s impact on these transcription regulatory complexes likely acts via H3K4me, Set1 governs the roles of FACT and NNS through opposing H3K4-dependent and -independent functions. We also identify diametrically opposing consequences for mutation of H3K4 to alanine or arginine, illuminating that caution must be taken in interpreting histone mutation studies. Unlike FACT and NNS, detailed genetic studies suggest an H3K4me-centric mode of Spt6-Spn1 regulation by JHD2 and SET1. Chromatin immunoprecipitation and transcript quantification experiments show that Jhd2 opposes the positioning of a Spt6-deposited nucleosome near the transcription start site of SER3, a Spt6-Spn1 regulated gene, leading to hyper-induction of SER3. In addition to confirming and extending an emerging role for Jhd2 in the control of nucleosome occupancy near transcription start sites, our findings suggest some of the chromatin regulatory functions of Set1 are independent of H3K4 methylation.

genetics↗