TY - JOUR
T1 - RECTA
T2 - Regulon identification based on comparative genomics and transcriptomics analysis
AU - Chen, Xin
AU - Ma, Anjun
AU - McDermaid, Adam
AU - Zhang, Hanyuan
AU - Liu, Chao
AU - Cao, Huansheng
AU - Ma, Qin
N1 - Funding Information:
Acknowledgments: This work was supported by National Science Foundation/EPSCoR Award No. IIA-1355423, the State of South Dakota Research Innovation Center and the Agriculture Experiment Station of South Dakota State University (SDSU). Support for this project was also provided by Hatch Project: SD00H558-15/project accession No. 1008151 from the USDA National Institute of Food and Agriculture, Sanford Health–SDSU Collaborative Research Seed Grant Program, and SDSU Scholarly Excellence Award (337T06). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation (grant number ACI-1548562).
Funding Information:
This work was supported by National Science Foundation/EPSCoR Award No. IIA-1355423, the State of South Dakota Research Innovation Center and the Agriculture Experiment Station of South Dakota State University (SDSU). Support for this project was also provided by Hatch Project: SD00H558-15/project accession No. 1008151 from the USDA National Institute of Food and Agriculture, Sanford Health–SDSU Collaborative Research Seed Grant Program, and SDSU Scholarly Excellence Award (337T06). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation (grant number ACI-1548562).
Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/6
Y1 - 2018/6
N2 - Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.
AB - Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.
KW - Acid stress response
KW - Cis-regulatory motif finding
KW - Differentially expressed gene
KW - Gene co-expression
KW - Gene regulatory network
KW - Lactococcus lactis MG1363
KW - RECTA
KW - Regulon
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U2 - 10.3390/genes9060278
DO - 10.3390/genes9060278
M3 - Article
AN - SCOPUS:85047904900
SN - 2073-4425
VL - 9
JO - Genes
JF - Genes
IS - 6
M1 - 278
ER -