Dataset Information

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

ABSTRACT: Alternative splicing is important for the development and function of the nervous system, but little is known about the differences in alternative splicing between distinct types of neurons. Furthermore, the factors that control cell-type-specific splicing and the physiological roles of these alternative isoforms are unclear. By monitoring alternative splicing at single-cell resolution in Caenorhabditis elegans, we demonstrate that splicing patterns in different neurons are often distinct and highly regulated. We identify two conserved RNA-binding proteins, UNC-75/CELF and EXC-7/Hu/ELAV, which regulate overlapping networks of splicing events in GABAergic and cholinergic neurons. We use the UNC-75 exon network to discover regulators of synaptic transmission and to identify unique roles for isoforms of UNC-64/Syntaxin, a protein required for synaptic vesicle fusion. Our results indicate that combinatorial regulation of alternative splicing in distinct neurons provides a mechanism to specialize metazoan nervous systems.

SUBMITTER: Norris AD

PROVIDER: S-EPMC4096705 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

Norris Adam D AD Gao Shangbang S Norris Megan L ML Ray Debashish D Ramani Arun K AK Fraser Andrew G AG Morris Quaid Q Hughes Timothy R TR Zhen Mei M Calarco John A JA

Molecular cell 20140605 6

Alternative splicing is important for the development and function of the nervous system, but little is known about the differences in alternative splicing between distinct types of neurons. Furthermore, the factors that control cell-type-specific splicing and the physiological roles of these alternative isoforms are unclear. By monitoring alternative splicing at single-cell resolution in Caenorhabditis elegans, we demonstrate that splicing patterns in different neurons are often distinct and hi ...[more]

PMID: 24910101

Similar Datasets

Project description:Sympatric speciation can arise as a result of disruptive selection with assortative mating as a pleiotropic by-product. Studies on host choice, employing artificial neural networks as models for the host recognition system in exploiters, illustrate how disruptive selection on host choice coupled with assortative mating can arise as a consequence of selection for specialization. Our studies demonstrate that a generalist exploiter population can evolve into a guild of specialists with an 'ideal free' frequency distribution across hosts. The ideal free distribution arises from variability in host suitability and density-dependent exploiter fitness on different host species. Specialists are less subject to inter-phenotypic competition than generalists and to harmful mutations that are common in generalists exploiting multiple hosts. When host signals used as cues by exploiters coevolve with exploiter recognition systems, our studies show that evolutionary changes may be continuous and cyclic. Selection changes back and forth between specialization and generalization in the exploiters, and weak and strong mimicry in the hosts, where non-defended hosts use the host investing in defence as a model. Thus, host signals and exploiter responses are engaged in a red-queen mimicry process that is ultimately cyclic rather then directional. In one phase, evolving signals of exploitable hosts mimic those of hosts less suitable for exploitation (i.e. the model). Signals in the model hosts also evolve through selection to escape the mimic and its exploiters. Response saturation constraints in the model hosts lead to the mimic hosts finally perfecting its mimicry, after which specialization in the exploiter guild is lost. This loss of exploiter specialization provides an opportunity for the model hosts to escape their mimics. Therefore, this cycle then repeats. We suggest that a species can readily evolve sympatrically when disruptive selection for specialization on hosts is the first step. In a sexual reproduction setting, partial reproductive isolation may first evolve by mate choice being confined to individuals on the same host. Secondly, this disruptive selection will favour assortative mate choice on genotype, thereby leading to increased reproductive isolation.

Project description:Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently. In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific. Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.

Project description:Human embryonic stem cells (hESCs) and neural progenitor (NP) cells are excellent models for recapitulating early neuronal development in vitro, and are key to establishing strategies for the treatment of degenerative disorders. While much effort had been undertaken to analyze transcriptional and epigenetic differences during the transition of hESC to NP, very little work has been performed to understand post-transcriptional changes during neuronal differentiation. Alternative RNA splicing (AS), a major form of post-transcriptional gene regulation, is important in mammalian development and neuronal function. Human ESC, hESC-derived NP, and human central nervous system stem cells were compared using Affymetrix exon arrays. We introduced an outlier detection approach, REAP (Regression-based Exon Array Protocol), to identify 1,737 internal exons that are predicted to undergo AS in NP compared to hESC. Experimental validation of REAP-predicted AS events indicated a threshold-dependent sensitivity ranging from 56% to 69%, at a specificity of 77% to 96%. REAP predictions significantly overlapped sets of alternative events identified using expressed sequence tags and evolutionarily conserved AS events. Our results also reveal that focusing on differentially expressed genes between hESC and NP will overlook 14% of potential AS genes. In addition, we found that REAP predictions are enriched in genes encoding serine/threonine kinase and helicase activities. An example is a REAP-predicted alternative exon in the SLK (serine/threonine kinase 2) gene that is differentially included in hESC, but skipped in NP as well as in other differentiated tissues. Lastly, comparative sequence analysis revealed conserved intronic cis-regulatory elements such as the FOX1/2 binding site GCAUG as being proximal to candidate AS exons, suggesting that FOX1/2 may participate in the regulation of AS in NP and hESC. In summary, a new methodology for exon array analysis was introduced, leading to new insights into the complexity of AS in human embryonic stem cells and their transition to neural stem cells.

Dataset Information

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

Publications

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets