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Model-driven experimental design workflow expands understanding of regulatory role of Nac in Escherichia coli.


ABSTRACT: The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in Escherichia coli. We comprehensively predict alternative nitrogen sources, including cytosine and cytidine, which trigger differential activation of Nac using a model-driven workflow. In accordance with the prediction, genome-wide measurements with ChIP-exo and RNA-seq were performed. Integrative data analysis reveals that the Nac and NtrC regulons consist of 97 and 43 genes under alternative nitrogen conditions, respectively. Functional analysis of Nac at the transcriptional level showed that Nac directly down-regulates amino acid biosynthesis and restores expression of tricarboxylic acid (TCA) cycle genes to alleviate nitrogen-limiting stress. We also demonstrate that both TFs coherently modulate α-ketoglutarate accumulation stress due to nitrogen limitation by co-activating amino acid and diamine degradation pathways. A systems-biology approach provided a detailed and quantitative understanding of both TF's roles and how nitrogen and carbon metabolic networks respond complementarily to nitrogen-limiting stress.

SUBMITTER: Park JY 

PROVIDER: S-EPMC9853098 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Model-driven experimental design workflow expands understanding of regulatory role of Nac in <i>Escherichia coli</i>.

Park Joon Young JY   Lee Sang-Mok SM   Ebrahim Ali A   Scott-Nevros Zoe K ZK   Kim Jaehyung J   Yang Laurence L   Sastry Anand A   Seo Sang Woo SW   Palsson Bernhard O BO   Kim Donghyuk D  

NAR genomics and bioinformatics 20230120 1


The establishment of experimental conditions for transcriptional regulator network (TRN) reconstruction in bacteria continues to be impeded by the limited knowledge of activating conditions for transcription factors (TFs). Here, we present a novel genome-scale model-driven workflow for designing experimental conditions, which optimally activate specific TFs. Our model-driven workflow was applied to elucidate transcriptional regulation under nitrogen limitation by Nac and NtrC, in <i>Escherichia  ...[more]

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