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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

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