DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data

Our plain-English summary of DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data, by M.H. Schulz, W.E. Devanny, A. Gitter, et al (BMC Systems Biology, 2012).

The study

• Authors: M.H. Schulz, W.E. Devanny, A. Gitter, S. Zhong, J. Ernst, Z. Bar-Joseph
• Journal: BMC Systems Biology
• Published: 2012;6:104
• Cited by: 101 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Modeling dynamic regulatory networks is a major challenge since much of the protein-DNA interaction data available is static. The Dynamic Regulatory Events Miner (DREM) uses a Hidden Markov Model-based approach to integrate this static interaction data with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM has been used successfully in diverse areas of biological research. However, several issues were not addressed by the original version.

Results: DREM 2.0 is a comprehensive software for reconstructing dynamic regulatory networks that supports interactive graphical or batch mode. With version 2.0 a set of new features that are unique in comparison with other softwares are introduced. First, we provide static interaction data for additional species. Second, DREM 2.0 now accepts continuous binding values and we added a new method to utilize TF expression levels when searching for dynamic models. Third, we added support for discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. Finally, we improved the visualization to support the new features. Combined, these changes improve the ability of DREM 2.0 to accurately recover dynamic regulatory networks and make it much easier to use it for analyzing such networks in several species with varying degrees of interaction information.

Conclusions: DREM 2.0 provides a unique framework for constructing and visualizing dynamic regulatory networks. DREM 2.0 can be downloaded from: http://www.sb.cs.cmu.edu/drem.

In plain English

This article described DREM 2.0, a program combining gene activity measured over time with data on where transcription factors (control proteins) bind DNA, to model which factors may regulate which genes, and when. It was a software paper, not a study of people or asbestos-related disease. To demonstrate it, the authors reanalysed earlier data on human lung adenocarcinoma cells (A549) measured 1, 6, 24 and 48 hours and 7 days after asbestos exposure, alongside unexposed controls. The article did not name the asbestos fiber type. The program predicted involvement of transcription factors already linked to asbestos exposure, such as the FOS family, up-regulated at 6 hours. A motif search found a pattern closely resembling the HEB/TCF12 binding site. Using a model built without the binding data, a motif search at the 6-hour branch point matched, after grouping related factors by family, 10 of the 24 factors found there in the full run. The version using transcription factor expression levels found the most statistically significant gene-function terms. The authors concluded that they believed these improvements would make DREM 2.0 more widely used.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Software description paper (computational method), with a worked example reanalysing previously published gene expression data
• Who was studied: No people were studied. The worked example used publicly available gene expression data (GEO record GSE6013) from an earlier study of human lung adenocarcinoma cells (A549 cell line) treated with asbestos in the laboratory
• Exposure: Laboratory asbestos exposure of lung cells, with gene expression measured at 1, 6, 24 and 48 hours and 7 days after exposure, compared with an unexposed control time series. Fiber type and dose were not stated in this article.
• Main results:
  • In the asbestos-treated lung cell data, DREM 2.0 predicted enrichment of transcription factors described as known to be relevant in asbestos exposure, such as the FOS family, which were up-regulated at the 6 hour time point
  • For one split in the model that the interaction data could not explain, a motif search found a DNA pattern closely resembling the binding motif of HEB/TCF12 (similarity E-value 3.93e-12)
  • When a DREM model was built without transcription factor to gene interaction data, a DECOD motif search of promoter sequences for genes at the 6 hour split, with matches compared against TRANSFAC, recovered 10 of the 24 transcription factors identified for that split in the original run, after grouping factors from the same family
  • Of three approaches compared on the asbestos data (expression only; expression plus interaction data; expression plus interaction data plus transcription factor expression), the version using transcription factor expression produced the highest number of significant gene-function (GO) terms. Terms found only by this version included positive regulation of cell death.
• The authors' conclusion: The authors stated that they believed the improvements (supplied interaction data for more species, motif discovery, use of transcription factor expression levels and of time-varying binding data) would make DREM 2.0 a more widely used software package for reconstructing dynamic gene regulatory networks

Topics

Medical terms in this study, explained in our Lexicon: Discovery.

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