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Gene expression changes upon drug withdrawal

ABSTRACT: Gene expression changes upon drug withdrawal

PROVIDER: PRJNA386477 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR5534710.fastq.gz	Fastqsanger.gz
	SRR5534720.fastq.gz	Fastqsanger.gz
	SRR5534730.fastq.gz	Fastqsanger.gz
	SRR5534711.fastq.gz	Fastqsanger.gz
	SRR5534721.fastq.gz	Fastqsanger.gz

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Pentylenetetrazol treatment and its withdrawal induced gene expression changes in female Drosophila head

Project description:We have recently developed a Drosophila behavioral- and transcriptomic- based (systems) model of chronic pentylenetetrazole (PTZ) induced locomotor plasticity. Pharmacological validation using antiepileptic drugs (AEDs) shows that the model is predictive of antiepileptic, antiepileptogenic, disease-modifying and neuroprotective activities. This model is however developed using male flies. The present submission relates to microarray gene expression profiling of fly heads after treatment of female Drosophila adults with PTZ for seven days subsequent withdrawal of PTZ for next seven days. Expression profiles have been generated at three time points during chronic PTZ treatment, namely 12 hrs, 2nd day and 7th day and at the end of withdrawal period, i.e., 14th day from the beginning of the treatment. Keywords: Drug response

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Predicting gene expression changes upon epigenomic drug treatment.

Project description:BackgroundTumors are characterized by global changes in epigenetic changes such as DNA methylation and histone modifications that are functionally linked to tumor progression. Accordingly, several drugs targeting the epigenome have been proposed for cancer therapy, notably, histone deacetylase inhibitors (HDACi) such as Vorinostatis and DNA methyltransferase inhibitors (DNMTi) such as Zebularine. However, a fundamental challenge with such approaches is the lack of genomic specificity, i.e., the transcriptional changes at different genomic loci can be highly variable thus making it difficult to predict the consequences on the global transcriptome and drug response. For instance, treatment with DNMTi may upregulate the expression of not only a tumor suppressor but also an oncogene leading to unintended adverse effect.MethodsGiven the pre-treatment transcriptome and epigenomic profile of a sample, we assessed the extent of predictability of locus-specific changes in gene expression upon treatment with HDACi using machine learning.ResultsWe found that in two cell lines (HCT116 treated with Largazole at 8 doses and RH4 treated with Entinostat at 1μM) where the appropriate data (pre-treatment transcriptome and epigenome as well as post-treatment transcriptome) is available, our model distinguished the post-treatment up versus downregulated genes with high accuracy (up to ROC of 0.89). Furthermore, a model trained on one cell line is applicable to another cell line suggesting generalizability of the model.ConclusionsHere we present a first assessment of the predictability of genome-wide transcriptomic changes upon treatment with HDACi. Lack of appropriate omics data from clinical trials of epigenetic drugs currently hampers the assessment of applicability of our approach in clinical setting.

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Integrated miRNA/mRNA-Seq of the Habenulo-Interpeduncular Withdrawal Circuit

Project description:Nicotine dependence is responsible for perpetuating the adverse health effects due to tobacco use, the leading cause of preventable death worldwide. Nicotine is an agonist for nicotinic acetylcholine receptors, which are enriched in the habenulo-interpeduncular withdrawal circuit. Drugs of abuse, including nicotine, induce stable neuroadaptations, requiring protein synthesis through regulation of transcription factors, epigenetic mechanisms, and non-coding RNAs. It also been shown that miRNAs in brain are regulated by nicotine and that miRNA dysregulation contributes to brain dysfunction, including drug addiction. While much is known about the neurocircuitry responsible for the behaviors associated with nicotine reward or withdrawal, the underlying mechanisms of how changes in behavior are induced are less clear. Using miRNA- and mRNA-Seq, we demonstrate that there are widespread changes in both miRNA and mRNA expression in the IPN and MHb during acute nicotine withdrawal. Conserved, differentially expressed miRNAs were predicted to target inversely regulated mRNAs. This dataset represents a valuable resource, identifying a multitude of miRNAs/genes, which upon further study may reveal new mechanisms underlying the neuroadaptations of nicotine dependence and the symptoms of nicotine withdrawal.

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Project description:Kindling induced by Pentylenetetrazol is an established rodent model of epileptogenesis. The molecular basis of the long-term plasticity involved is however not clear. In addition, rodent models of kindling plasticity are not useful for large-scale screening of compounds to identify antiepileptogenic drugs. Given this, we have developed a fly model of chronic PTZ- and withdrawal-induced behavioral plasticity. In our fly model, the chronic PTZ treatment is given for 7 days. This is then followed by 7 day long withdrawal. Expression profiling of fly heads at three time points in the 7 day long withdrawal period – 8th day, 10th day, and 14th day from the beginning of the treatment - showed a dynamic and widespread alteration of various functional categories of genes. Keywords: time series

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