Inferring the Relation Between Transcriptional and Posttranscriptional Regulation from Expression Compendia

Overview

Journal BMC Microbiol

Publisher Biomed Central

Specialty Microbiology

Date 2014 Jan 29

PMID 24467879

Citations 8

Authors

Ivan Ishchukov

Yan Wu

Sandra Van Puyvelde

Jos Vanderleyden

Kathleen Marchal

Affiliations

Soon will be listed here.

Abstract

Background: Publicly available expression compendia that measure both mRNAs and sRNAs provide a promising resource to simultaneously infer the transcriptional and the posttranscriptional network. To maximally exploit the information contained in such compendia, we propose an analysis flow that combines publicly available expression compendia and sequence-based predictions to infer novel sRNA-target interactions and to reconstruct the relation between the sRNA and the transcriptional network.

Results: We relied on module inference to construct modules of coexpressed genes (sRNAs). TFs and sRNAs were assigned to these modules using the state-of-the-art inference techniques LeMoNe and Context Likelihood of Relatedness (CLR). Combining these expressions with sequence-based sRNA-target interactions allowed us to predict 30 novel sRNA-target interactions comprising 14 sRNAs. Our results highlight the role of the posttranscriptional network in finetuning the transcriptional regulation, e.g. by intra-operonic regulation.

Conclusion: In this work we show how strategies that combine expression information with sequence-based predictions can help unveiling the intricate interaction between the transcriptional and the posttranscriptional network in prokaryotic model systems.

Citing Articles

Using genome-wide expression compendia to study microorganisms.

Lee A, Reiter T, Doing G, Oh J, Hogan D, Greene C Comput Struct Biotechnol J. 2022; 20:4315-4324.

PMID: 36016717 PMC: 9396250. DOI: 10.1016/j.csbj.2022.08.012.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.

Arrieta-Ortiz M, Hafemeister C, Shuster B, Baliga N, Bonneau R, Eichenberger P mSystems. 2020; 5(3).

PMID: 32487739 PMC: 8534726. DOI: 10.1128/mSystems.00057-20.

Unified feature association networks through integration of transcriptomic and proteomic data.

McClure R, Wendler J, Adkins J, Swanstrom J, Baric R, Deatherage Kaiser B PLoS Comput Biol. 2019; 15(9):e1007241.

PMID: 31527878 PMC: 6748406. DOI: 10.1371/journal.pcbi.1007241.

Species-specific transcriptomic network inference of interspecies interactions.

McClure R, Overall C, Hill E, Song H, Charania M, Bernstein H ISME J. 2018; 12(8):2011-2023.

PMID: 29795448 PMC: 6052077. DOI: 10.1038/s41396-018-0145-6.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.

McClure R, Overall C, McDermott J, Hill E, Markillie L, McCue L Nucleic Acids Res. 2016; 44(18):8810-8825.

PMID: 27568004 PMC: 5062996. DOI: 10.1093/nar/gkw737.

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