Abstract

BackgroundInference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation.ResultsHere, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level.ConclusionsDetailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2525-5) contains supplementary material, which is available to authorized users.

Highlights

  • Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs

  • Genes significantly regulated by RNA silencing of candidate IFN-α modulators The selection procedure led to the characterization of the significant regulations induced by the inactivation of each of the two candidate IFN-α modulators, STAT1 and IFIH1

  • STAT1, a transcription factor central to IFN-α pathway, was here confirmed as a strong positive IFN-α modulator, with 17/21 genes down-regulated in the early stimulation phase, whereas IFIH1 was seen to be mainly a positive modulator with 8/12 down-regulated genes, 3 in the early and 5 in the late stimulation phase

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Summary

Introduction

Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. One of the most discussed topics in the field of systems biology is the inference of gene regulatory networks (GRNs) from high-throughput expression data. As regards GRNs, genetic perturbations, in which the expression levels of one or more genes are altered by their silencing (knockout, knockdown) or up-regulation (overexpression), are the best suited to reconstruct gene regulatory relationships that account for directionality [4, 8]. Given the complexity and the high costs related to a whole-genome approach, it is a common practice to focus the attention on smaller regulatory sub-networks and on the basic building modules of which they are composed [10, 11]

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