Dataset Information

RNA-Seq data on prefrontal cortex in valproic acid model of autism and control rats.

ABSTRACT: The transcriptome sequencing data of valproic acid (VPA) model of autism and control rats are presented. VPA model of autism was induced by a single intraperitoneal injection of 600?mg/kg sodium valproic acid to female rats at day 12.5 post-conception, and the control rats were injected with saline. Male offspring of VPA- or saline-injected dams from different litters were sacrificed on PND 35 (n = three rats/three litters/group). Prefrontal cortex was dissected from both hemispheres, and RNA was isolated. Libraries were prepared and RNA Sequencing (RNA-Seq) was performed following Illumina's recommendations. Samples are described in the SRA portal (SRP115258) and FASTQ files have been deposited in Sequence Read Archive (accession numbers: SRR5950172 to SRR5950177). The interpretation of these data is presented in the following research article: "Transcriptional and splicing dysregulation in prefrontal cortex of valproic acid induced rat models of autism" (Zhang et al., 2018) [1].

SUBMITTER: Qian Z

PROVIDER: S-EPMC5996313 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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RNA-Seq data on prefrontal cortex in valproic acid model of autism and control rats.

Qian Zhaoqiang Z Zhang Ruoxin R Zhou Jinlong J Sun Siqi S Di Yuanyuan Y Ren Wei W Tian Yingfang Y

Data in brief 20180321

The transcriptome sequencing data of valproic acid (VPA) model of autism and control rats are presented. VPA model of autism was induced by a single intraperitoneal injection of 600 mg/kg sodium valproic acid to female rats at day 12.5 post-conception, and the control rats were injected with saline. Male offspring of VPA- or saline-injected dams from different litters were sacrificed on PND 35 (<i>n</i> = three rats/three litters/group). Prefrontal cortex was dissected from both hemispheres, and ...[more]

PMID: 29900238

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Project description:Synaptic alterations concomitant with neuroinflammation have been described in patients and experimental models of autism spectrum disorder (ASD). However, the role of microglia and astroglia in relation to synaptic changes is poorly understood. Male Wistar rats prenatally exposed to valproic acid (VPA, 450 mg/kg, i.p.) or saline (control) at embryonic day 10.5 were used to study synapses, microglia, and astroglia in the prefrontal cortex (PFC) at postnatal days 3 and 35 (PND3 and PND35). Primary cultures of cortical neurons, microglia, and astroglia isolated from control and VPA animals were used to study each cell type individually, neuron-microglia and microglia-astroglia crosstalk. In the PFC of VPA rats, synaptic changes characterized by an increase in the number of excitatory synapses were evidenced at PND3 and persisted until PND35. At PND3, microglia and astroglia from VPA animals were morphologically similar to those of age-matched controls, whereas at PND35, reactive microgliosis and astrogliosis were observed in the PFC of VPA animals. Cortical neurons isolated from VPA rats mimicked in vitro the synaptic pattern seen in vivo. Cortical microglia and astroglia isolated from VPA animals exhibited reactive morphology, increased pro-inflammatory cytokines, and a compromised miRNA processing machinery. Microglia from VPA animals also showed resistance to a phagocytic challenge. In the presence of neurons from VPA animals, microglia isolated from VPA rats revealed a non-reactive morphology and promoted neurite outgrowth, while microglia from control animals displayed a reactive profile and promoted dendritic retraction. In microglia-astroglia co-cultures, microglia from VPA animals displayed a reactive profile and exacerbated astrocyte reactivity. Our study indicates that cortical microglia from VPA animals are insensitive or adapted to neuronal cues expressed by neurons from VPA animals. Further, long-term in vivo microgliosis could be the result of altered microglia-astroglia crosstalk in VPA animals. Thus, our study highlights cortical microglia-astroglia communication as a new mechanism implicated in neuroinflammation in ASD; consequently, we propose that this crosstalk is a potential target for interventions in this disorder.

Project description:BackgroundRNA-Seq has become one of the most widely used applications based on next-generation sequencing technology. However, raw RNA-Seq data may have quality issues, which can significantly distort analytical results and lead to erroneous conclusions. Therefore, the raw data must be subjected to vigorous quality control (QC) procedures before downstream analysis. Currently, an accurate and complete QC of RNA-Seq data requires of a suite of different QC tools used consecutively, which is inefficient in terms of usability, running time, file usage, and interpretability of the results.ResultsWe developed a comprehensive, fast and easy-to-use QC pipeline for RNA-Seq data, RNA-QC-Chain, which involves three steps: (1) sequencing-quality assessment and trimming; (2) internal (ribosomal RNAs) and external (reads from foreign species) contamination filtering; (3) alignment statistics reporting (such as read number, alignment coverage, sequencing depth and pair-end read mapping information). This package was developed based on our previously reported tool for general QC of next-generation sequencing (NGS) data called QC-Chain, with extensions specifically designed for RNA-Seq data. It has several features that are not available yet in other QC tools for RNA-Seq data, such as RNA sequence trimming, automatic rRNA detection and automatic contaminating species identification. The three QC steps can run either sequentially or independently, enabling RNA-QC-Chain as a comprehensive package with high flexibility and usability. Moreover, parallel computing and optimizations are embedded in most of the QC procedures, providing a superior efficiency. The performance of RNA-QC-Chain has been evaluated with different types of datasets, including an in-house sequencing data, a semi-simulated data, and two real datasets downloaded from public database. Comparisons of RNA-QC-Chain with other QC tools have manifested its superiorities in both function versatility and processing speed.ConclusionsWe present here a tool, RNA-QC-Chain, which can be used to comprehensively resolve the quality control processes of RNA-Seq data effectively and efficiently.

Dataset Information

RNA-Seq data on prefrontal cortex in valproic acid model of autism and control rats.

Publications

RNA-Seq data on prefrontal cortex in valproic acid model of autism and control rats.

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