An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network

Molecular Systems Biology

Mario L Arrieta-Ortiz³  Christoph Hafemeister³  Ashley Rose Bate³  Timothy Chu³  Alex Greenfield³  Bentley Shuster³  Samantha N Barry³  Matthew Gallitto³  Brian Liu³  Thadeous Kacmarczyk³  Francis Santoriello³  Jie Chen³  Christopher DA Rodrigues²  Tsutomu Sato¹  David Z Rudner²  Adam Driks⁴  Richard Bonneau³ 

[1] Department of Frontier Bioscience, Hosei University, Koganei, Tokyo, Japan;Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA;Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA;Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, USA

关键词: Bacillus subtilis;    network inference;    sporulation;    transcriptional networks;   

DOI  :  10.15252/msb.20156236

来源: Wiley

【 摘 要 】

Abstract

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Synopsis

A new computational framework integrating network component analysis and model selection is combined with transcriptomic datasets and generates an expanded and more accurate transcriptional regulatory network (TRN) for Bacillus subtilis.

• A global TRN is inferred for B. subtilis and contains 3,086 protein-coding genes, 215 transcription factors (TFs) and predicts 4,516 interactions (2,258 novel).
• Previously known interactions are recalled at high proportion (74%) and experimental support is provided for 1,289 TF–gene interactions (out of 1,841 tested) in transcriptional profiling data with KO strains, including 391 (out of 635) novel interactions.
• The inferred TRN provides novel functional insights even for well-studied pathways, such as spore formation.

【 授权许可】

CC BY   
© 2015 The Authors. Published under the terms of the CC BY 4.0 license

Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.