scholarly journals Cell-type-specific profiling of loaded miRNAs from Caenorhabditis elegans reveals spatial and temporal flexibility in Argonaute loading

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Christopher A. Brosnan ◽  
Alexander J. Palmer ◽  
Steven Zuryn
Keyword(s):  
Caenorhabditis Elegans ◽  
Regulatory Networks ◽  
Regulatory Mechanisms ◽  
Cell Type ◽  
A Genome ◽  
Temporal Flexibility ◽  
Small Regulatory Rnas ◽  
Cell Type Specific ◽  
Single Cell Type ◽  
Regulated Gene Expression

AbstractMulticellularity has coincided with the evolution of microRNAs (miRNAs), small regulatory RNAs that are integrated into cellular differentiation and homeostatic gene-regulatory networks. However, the regulatory mechanisms underpinning miRNA activity have remained largely obscured because of the precise, and thus difficult to access, cellular contexts under which they operate. To resolve these, we have generated a genome-wide map of active miRNAs in Caenorhabditis elegans by revealing cell-type-specific patterns of miRNAs loaded into Argonaute (AGO) silencing complexes. Epitope-labelled AGO proteins were selectively expressed and immunoprecipitated from three distinct tissue types and associated miRNAs sequenced. In addition to providing information on biological function, we define adaptable miRNA:AGO interactions with single-cell-type and AGO-specific resolution. We demonstrate spatial and temporal dynamicism, flexibility of miRNA loading, and suggest miRNA regulatory mechanisms via AGO selectivity in different tissues and during ageing. Additionally, we resolve widespread changes in AGO-regulated gene expression by analysing translatomes specifically in neurons.

scholarly journals Genetic influences on cell type specific gene expression and splicing during neurogenesis elucidate regulatory mechanisms of brain traits

2020 ◽  
Author(s):  
Nil Aygün ◽  
Angela L. Elwell ◽  
Dan Liang ◽  
Michael J. Lafferty ◽  
Kerry E. Cheek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Elements ◽  
Regulatory Mechanisms ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Expression ◽  
Risk Variants ◽  
Allele Specific ◽  
Cell Type Specific ◽  
Regulatory Effects

SummaryInterpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing is mainly performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements of cells present during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs and allele specific expression in primary human neural progenitors (n=85) and their sorted neuronal progeny (n=74). Using colocalization and TWAS, we uncover cell-type specific regulatory mechanisms underlying risk for these traits.

scholarly journals Characterization of novel primary miRNA transcription units in human cells using Bru-seq nascent RNA sequencing

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Karan Bedi ◽  
Michelle T Paulsen ◽  
Thomas E Wilson ◽  
Mats Ljungman
Keyword(s):  
Rna Polymerase Ii ◽  
Rna Sequencing ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Mature Mirnas ◽  
Mirna Regulation ◽  
Cell Type ◽  
Data Resource ◽  
Cell Type Specific ◽  
Nascent Rna

Abstract MicroRNAs (miRNAs) are key contributors to gene regulatory networks. Because miRNAs are processed from RNA polymerase II transcripts, insight into miRNA regulation requires a comprehensive understanding of the regulation of primary miRNA transcripts. We used Bru-seq nascent RNA sequencing and hidden Markov model segmentation to map primary miRNA transcription units (TUs) across 32 human cell lines, allowing us to describe TUs encompassing 1443 miRNAs from miRBase and 438 from MirGeneDB. We identified TUs for 61 miRNAs with an unknown CAGE TSS signal for MirGeneDB miRNAs. Many primary transcripts containing miRNA sequences failed to generate mature miRNAs, suggesting that miRNA biosynthesis is under both transcriptional and post-transcriptional control. In addition to constitutive and cell-type specific TU expression regulated by differential promoter usage, miRNA synthesis can be regulated by transcription past polyadenylation sites (transcriptional read through) and promoter divergent transcription (PROMPTs). We identified 197 miRNA TUs with novel promoters, 97 with transcriptional read-throughs and 3 miRNA TUs that resemble PROMPTs in at least one cell line. The miRNA TU annotation data resource described here reveals a greater complexity in miRNA regulation than previously known and provides a framework for identifying cell-type specific differences in miRNA transcription in cancer and cell transition states.

Phylogenetic conservation of the cell-type-specific Lan3-2 glycoepitope in Caenorhabditis elegans

2010 ◽  
Vol 220 (3-4) ◽  
pp. 77-87
Author(s):  
Harper C. VanSteenhouse ◽  
Zachary A. Horton ◽  
Robert O’Hagan ◽  
Mei-Hui Tai ◽  
Birgit Zipser
Keyword(s):  
Caenorhabditis Elegans ◽  
Cell Type ◽  
Phylogenetic Conservation ◽  
Cell Type Specific

scholarly journals SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

2020 ◽  
Author(s):  
Yupeng Wang ◽  
Rosario Jaime-Lara ◽  
Abhrarup Roy ◽  
Ying Sun ◽  
Xinyue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cell Types ◽  
Regulatory Elements ◽  
Learning Models ◽  
Cell Type ◽  
Coding Sequences ◽  
Sequence Features ◽  
A Genome ◽  
Cell Type Specific

Abstract ObjectiveComputational identification of cell type-specific regulatory elements on a genome-wide scale is very challenging.ResultsWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

scholarly journals Dynamic regulatory module networks for inference of cell type specific transcriptional networks

2020 ◽  
Author(s):  
Alireza Fotuhi Siahpirani ◽  
Deborah Chasman ◽  
Morten Seirup ◽  
Sara Knaack ◽  
Rupa Sridharan ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Dynamics ◽  
Cell Types ◽  
Small Sample ◽  
Cell Type ◽  
Regulatory Module ◽  
A Cell ◽  
Cell Type Specific ◽  
Time Point

AbstractChanges in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled transcriptomes and epigenomes at different stages of a developmental process. However, integrating these data across multiple cell types to infer cell type specific regulatory networks is a major challenge because of the small sample size for each time point. We present a novel approach, Dynamic Regulatory Module Networks (DRMNs), to model regulatory network dynamics on a cell lineage. DRMNs represent a cell type specific network by a set of expression modules and associated regulatory programs, and probabilistically model the transitions between cell types. DRMNs learn a cell type’s regulatory network from input expression and epigenomic profiles using multi-task learning to exploit cell type relatedness. We applied DRMNs to study regulatory network dynamics in two different developmental dynamic processes including cellular reprogramming and liver dedifferentiation. For both systems, DRMN predicted relevant regulators driving the major patterns of expression in each time point as well as regulators for transitioning gene sets that change their expression over time.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

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