Pradipta Ray

Epigenomic Analysis of Multilineage Differentiation of Human Embryonic Stem Cells

Epigenetic mechanisms have been proposed to play crucial roles in mammalian development, but their precise functions are only partially understood. To investigate epigenetic regulation of embryonic development, we differentiated human embryonic stem cells into mesendoderm, neural progenitor cells, trophoblast-like cells, and mesenchymal stem cells and systematically characterized DNA methylation, chromatin modifications, and the transcriptome in each lineage. We found that promoters that are active in early developmental stages tend to be CG rich and mainly engage H3K27me3 upon silencing in nonexpressing lineages. By contrast, promoters for genes expressed preferentially at later stages are often CG poor and primarily employ DNA methylation upon repression. Interestingly, the early developmental regulatory genes are often located in large genomic domains that are generally devoid of DNA methylation in most lineages, which we termed DNA methylation valleys (DMVs). Our results suggest that distinct epigenetic mechanisms regulate early and late stages of ES cell differentiation.

Integrative analysis of haplotype-resolved epigenomes across human tissues.

Allelic differences between the two homologous chromosomes can affect the propensity of inheritance in humans; however, the extent of such differences in the human genome has yet to be fully explored. Here we delineate allelic chromatin modifications and transcriptomes among a broad set of human tissues, enabled by a chromosome-spanning haplotype reconstruction strategy. The resulting large collection of haplotype-resolved epigenomic maps reveals extensive allelic biases in both chromatin state and transcription, which show considerable variation across tissues and between individuals, and allow us to investigate cis-regulatory relationships between genes and their control sequences. Analyses of histone modification maps also uncover intriguing characteristics of cis-regulatory elements and tissue-restricted activities of repetitive elements. The rich data sets described here will enhance our understanding of the mechanisms by which cis-regulatory elements control gene expression programs.

BayCis: a Bayesian hierarchical HMM for cis-regulatory module decoding in metazoan genomes

The transcriptional regulatory sequences in metazoan genomes often consist of multiple cis-regulatory modules (CRMs). Each CRM contains locally enriched occurrences of binding sites (motifs) for a certain array of regulatory proteins, capable of integrating, amplifying or attenuating multiple regulatory signals via combinatorial interaction with these proteins. The architecture of CRM organizations is reminiscent of the grammatical rules underlying a natural language, and presents a particular challenge to computational motif and CRM identification in metazoan genomes. In this paper, we present BayCis, a Bayesian hierarchical HMM that attempts to capture the stochastic syntactic rules of CRM organization. Under the BayCis model, all candidate sites are evaluated based on a posterior probability measure that takes into consideration their similarity to known BSs, their contrasts against local genomic context, their first-order dependencies on upstream sequence elements, as well as priors reflecting general knowledge of CRM structure. We compare our approach to five existing methods for the discovery of CRMs, and demonstrate competitive or superior prediction results evaluated against experimentally based annotations on a comprehensive selection of Drosophila regulatory regions. The software, database and Supplementary Materials will be available at http://www.sailing.cs. cmu.edu/baycis.

CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing

Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotide level, and lack the ability to capture the evolutionary dynamics of functional turnover of aligned sequence entities. As a result, comparative genomic search of non-conserved motifs across evolutionarily related taxa remains a difficult challenge, especially in higher eukaryotes, where the cis-regulatory regions containing motifs can be long and divergent; existing methods rely heavily on specialized pattern-driven heuristic search or sampling algorithms, which can be difficult to generalize and hard to interpret based on phylogenetic principles. We propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a context-dependent probabilistic graphical model that allows aligned sites from different taxa in a multiple alignment to be modeled by either a background or an appropriate motif phylogeny conditioning on the functional specifications of each taxon. The functional specifications themselves are the output of a phylogeny which models the evolution not of individual nucleotides, but of the overall functionality (e.g., functional retention or loss) of the aligned sequence segments over lineages. Combining this method with a hidden Markov model that autocorrelates evolutionary rates on successive sites in the genome, CSMET offers a principled way to take into consideration lineage-specific evolution of TFBSs during motif detection, and a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. On both simulated and real Drosophila cis-regulatory modules, CSMET outperforms other state-of-the-art comparative genomic motif finders.

DISCOVER: a feature-based discriminative method for motif search in complex genomes.

Motivation: Identifying transcription factor binding sites (TFBSs) encoding complex regulatory signals in metazoan genomes remains a challenging problem in computational genomics. Due to degeneracy of nucleotide content among binding site instances or motifs, and intricate ‘grammatical organization’ of motifs within cis-regulatory modules (CRMs), extant pattern matching-based in silico motif search methods often suffer from impractically high false positive rates, especially in the context of analyzing large genomic datasets, and noisy position weight matrices which characterize binding sites. Here, we try to address this problem by using a framework to maximally utilize the information content of the genomic DNA in the region of query, taking cues from values of various biologically meaningful genetic and epigenetic factors in the query region such as clade-specific evolutionary parameters, presence/absence of nearby coding regions, etc. We present a new method for TFBS prediction in metazoan genomes that utilizes both the CRM architecture of sequences and a variety of features of individual motifs. Our proposed approach is based on a discriminative probabilistic model known as conditional random fields that explicitly optimizes the predictive probability of motif presence in large sequences, based on the joint effect of all such features. Results: This model overcomes weaknesses in earlier methods based on less effective statistical formalisms that are sensitive to spurious signals in the data. We evaluate our method on both simulated CRMs and real Drosophila sequences in comparison with a wide spectrum of existing models, and outperform the state of the art by 22% in F1 score. Availability and Implementation: The code is publicly available at http://www.sailing.cs.cmu.edu/discover.html. Contact: epxing@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Distinct and Predictive Histone Lysine Acetylation Patterns at Promoters, Enhancers, and Gene Bodies

In eukaryotic cells, histone lysines are frequently acetylated. However, unlike modifications such as methylations, histone acetylation modifications are often considered redundant. As such, the functional roles of distinct histone acetylations are largely unexplored. We previously developed an algorithm RFECS to discover the most informative modifications associated with the classification or prediction of mammalian enhancers. Here, we used this tool to identify the modifications most predictive of promoters, enhancers, and gene bodies. Unexpectedly, we found that histone acetylation alone performs well in distinguishing these unique genomic regions. Further, we found the association of characteristic acetylation patterns with genic regions and association of chromatin state with splicing. Taken together, our work underscores the diverse functional roles of histone acetylation in gene regulation and provides several testable hypotheses to dissect these roles.

Sigma 2 Receptor/Tmem97 Agonists Produce Long Lasting Antineuropathic Pain Effects in Mice

Neuropathic pain is an important medical problem with few effective treatments. The sigma 1 receptor (σ1R) is known to be a potential target for neuropathic pain therapeutics, and antagonists for this receptor are effective in preclinical models and are currently in phase II clinical trials. Conversely, relatively little is known about σ2R, which has recently been identified as transmembrane protein 97 (Tmem97). We generated a series of σ1R and σ2R/Tmem97 agonists and antagonists and tested them for efficacy in the mouse spared nerve injury (SNI) model. In agreement with previous reports, we find that σ1R ligands given intrathecally (IT) produce relief of SNI-induced mechanical hypersensitivity. We also find that the putative σ2R/Tmem97 agonists DKR-1005, DKR-1051, and UKH-1114 (Ki ∼ 46 nM) lead to relief of SNI-induced mechanical hypersensitivity, peaking at 48 h after dosing when given IT. This effect is blocked by the putative σ2R/Tmem97 antagonist SAS-0132. Systemic administration of UKH-1114 (10 mg/kg) relieves SNI-induced mechanical hypersensitivity for 48 h with a peak magnitude of effect equivalent to 100 mg/kg gabapentin and without producing any motor impairment. Finally, we find that the TMEM97 gene is expressed in mouse and human dorsal root ganglion (DRG) including populations of neurons that are involved in pain; however, the gene is also likely expressed in non-neuronal cells that may contribute to the observed behavioral effects. Our results show robust antineuropathic pain effects of σ1R and σ2R/Tmem97 ligands, demonstrate that σ2R/Tmem97 is a novel neuropathic pain target, and identify UKH-1114 as a lead molecule for further development.

Comparative transcriptome profiling of the human and mouse dorsal root ganglia: an RNA-seq–based resource for pain and sensory neuroscience research

Abstract Molecular neurobiological insight into human nervous tissues is needed to generate next-generation therapeutics for neurological disorders such as chronic pain. We obtained human dorsal root ganglia (hDRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the hDRG transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene coexpression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Relevant gene families and pathways were also analyzed, including transcription factors, G-protein-coupled receptors, and ion channels. Our analyses reveal an hDRG-enriched protein-coding gene set (∼140), some of which have not been described in the context of DRG or pain signaling. Most of these show conserved enrichment in mDRG and were mined for known drug–gene product interactions. Conserved enrichment of the vast majority of transcription factors suggests that the mDRG is a faithful model system for studying hDRG, because of evolutionarily conserved regulatory programs. Comparison of hDRG and tibial nerve transcriptomes suggests trafficking of neuronal mRNA to axons in adult hDRG, and are consistent with studies of axonal transport in rodent sensory neurons. We present our work as an online, searchable repository (https://www.utdallas.edu/bbs/painneurosciencelab/sensoryomics/drgtxome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics and a blueprint for cross-species transcriptomic analyses.

Group II mGluRs suppress hyperexcitability in mouse and human nociceptors

Abstract We introduce a strategy for preclinical research wherein promising targets for analgesia are tested in rodent and subsequently validated in human sensory neurons. We evaluate group II metabotropic glutamate receptors, the activation of which is efficacious in rodent models of pain. Immunohistochemical analysis showed positive immunoreactivity for mGlu2 in rodent dorsal root ganglia (DRG), peripheral fibers in skin, and central labeling in the spinal dorsal horn. We also found mGlu2-positive immunoreactivity in human neonatal and adult DRG. RNA-seq analysis of mouse and human DRG revealed a comparative expression profile between species for group II mGluRs and for opioid receptors. In rodent sensory neurons under basal conditions, activation of group II mGluRs with a selective group II agonist produced no changes to membrane excitability. However, membrane hyperexcitability in sensory neurons exposed to the inflammatory mediator prostaglandin E2 (PGE2) was prevented by (2R,4R)-4-aminopyrrolidine-2,4-dicarboxylate (APDC). In human sensory neurons from donors without a history of chronic pain, we show that PGE2 produced hyperexcitability that was similarly blocked by group II mGluR activation. These results reveal a mechanism for peripheral analgesia likely shared by mice and humans and demonstrate a translational research strategy to improve preclinical validation of novel analgesics using cultured human sensory neurons.

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes

Motivation: 5‐Methylcytosine and 5‐Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High‐throughput assays to detect these modifications are expensive, labor‐intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole‐genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high‐throughput hypotheses testing on such predicted methylation or hydroxymethylation maps. Results: We test our approach on H1 human embryonic stem cells and H1‐derived neural progenitor cells. Our predictive model is comparable in accuracy to other state‐of‐the‐art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole‐genome accuracy, paving the way for large‐scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam‐search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of results potentially leading to a better understanding of epigenetic gene regulation. Availability and implementation: http://www.pradiptaray.com/direction, under CC‐by‐SA license. Contacts: pradiptaray@gmail.com or mchen@utdallas.edu or michael.zhang@utdallas.edu Supplementary information: Supplementary data are available at Bioinformatics online.