Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets.

Jeremiah Hadwen,Julio Plaza-Diaz,Sarah Schock,Alex Mackenzie,Faraz Farooq

doi:10.3390/ijms22126295

Abstract

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood–brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug–gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Highlights

Rare diseases including the muscular dystrophies system are significant contributors to human disability and illness
Samples of “activated” and “inhibited” networks elicited by the 50 drugs that modulated the greatest number of transcripts were identified by Ingenuity Pathway Analysis (IPA) using upstream regulator analysis of differential expression (DE) data
We have in the present study explored the use of IPA-based upstream regulator analysis (URA) to assign a reliability probability for single-gene instances found within system-wide RNA-seq datasets

Summary

Introduction

Rare diseases including the muscular dystrophies system are significant contributors to human disability and illness. Research involving transcriptome-wide DE data analysis may either focus on gene enrichment patterns or DE of single genes, this is so when studying rare diseases for it is the modulation of a specific gene that can hold the greatest therapeutic promise (e.g., utrophin for Duchenne muscular dystrophy). In the former analysis, which relies primarily on the gene annotation and enrichment analysis [18], a certain degree of statistical rigour can be achieved by virtue of the number of differentially expressed genes being studied. One such example is the analysis of drug-mediated transcriptome modulation to identify novel indications for established drugs, pioneered by Lamb et al who first used connectivity mapping of transcriptomic signatures to pair clinically approved drugs with disease signatures [19]

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