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Cell Syst. 2019 Aug 28;9(2):143-158.e13. doi: 10.1016/j.cels.2019.07.001. Epub 2019 Aug 21.

Determination of the Gene Regulatory Network of a Genome-Reduced Bacterium Highlights Alternative Regulation Independent of Transcription Factors.

Author information

1
Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Doctor Aiguader 88, Barcelona 08003, Spain. Electronic address: eva.yus@crg.eu.
2
Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Doctor Aiguader 88, Barcelona 08003, Spain. Electronic address: veronica.llorens@crg.eu.
3
Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Doctor Aiguader 88, Barcelona 08003, Spain.
4
Department for General Microbiology, Georg-August-University Göttingen, 37077 Göttingen, Germany.
5
Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Doctor Aiguader 88, Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, Barcelona 08010, Spain. Electronic address: luis.serrano@crg.eu.

Abstract

Here, we determined the relative importance of different transcriptional mechanisms in the genome-reduced bacterium Mycoplasma pneumoniae, by employing an array of experimental techniques under multiple genetic and environmental perturbations. Of the 143 genes tested (21% of the bacterium's annotated proteins), only 55% showed an altered phenotype, highlighting the robustness of biological systems. We identified nine transcription factors (TFs) and their targets, representing 43% of the genome, and 16 regulators that indirectly affect transcription. Only 20% of transcriptional regulation is mediated by canonical TFs when responding to perturbations. Using a Random Forest, we quantified the non-redundant contribution of different mechanisms such as supercoiling, metabolic control, RNA degradation, and chromosome topology to transcriptional changes. Model-predicted gene changes correlate well with experimental data in 95% of the tested perturbations, explaining up to 70% of the total variance when also considering noise. This analysis highlights the importance of considering non-TF-mediated regulation when engineering bacteria.

KEYWORDS:

Mycoplasma pneumoniae; gene regulatory network; systems biology; transcription; transcription factors; transcription regulation

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