Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C. glutamicum gene expression profile which includes 263 samples from 29 independent projects. We obtained 87 robust independent regulatory modules (iModulons). These iModulons explain 76.7% of the variance in the expression profile and constitute the quantitative transcriptional regulatory network of C. glutamicum. By analyzing the constituent genes in iModulons, we identified potential targets for 20 transcription factors. We also captured the changes in iModulon activities under different growth rates and dissolved oxygen concentrations, demonstrating the ability of iModulons to comprehensively interpret transcriptional responses to environmental changes. In summary, this study provides a genome-scale quantitative transcriptional regulatory network for C. glutamicum and informs future research on complex changes in the transcriptome.
Keywords: Corynebacterium glutamicum; IModulons discovery; Independence component analysis; Machine learning; Transcriptional regulatory network.
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