Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes
Abstract Background Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptiona...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-022-04627-9 |
| _version_ | 1818446147466821632 |
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| author | Francis C. Motta Robert C. Moseley Bree Cummins Anastasia Deckard Steven B. Haase |
| author_facet | Francis C. Motta Robert C. Moseley Bree Cummins Anastasia Deckard Steven B. Haase |
| author_sort | Francis C. Motta |
| collection | DOAJ |
| description | Abstract Background Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Results This study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features. Conclusions There are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected—that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes. |
| first_indexed | 2024-12-14T19:43:06Z |
| format | Article |
| id | doaj.art-0f55cfa96ce9411ebaf32eda110b0971 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T19:43:06Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0f55cfa96ce9411ebaf32eda110b09712022-12-21T22:49:38ZengBMCBMC Bioinformatics1471-21052022-03-0123112010.1186/s12859-022-04627-9Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processesFrancis C. Motta0Robert C. Moseley1Bree Cummins2Anastasia Deckard3Steven B. Haase4Department of Mathematical Sciences, Florida Atlantic UniversityDepartment of Biology, Duke UniversityDepartment of Mathematical Sciences, Montana State UniversityGeometric Data AnalyticsDepartment of Biology, Duke UniversityAbstract Background Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Results This study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features. Conclusions There are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected—that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.https://doi.org/10.1186/s12859-022-04627-9Cell cycleCircadian rhythmsGene regulatory networksTranscription factorsNetwork inference |
| spellingShingle | Francis C. Motta Robert C. Moseley Bree Cummins Anastasia Deckard Steven B. Haase Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes BMC Bioinformatics Cell cycle Circadian rhythms Gene regulatory networks Transcription factors Network inference |
| title | Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| title_full | Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| title_fullStr | Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| title_full_unstemmed | Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| title_short | Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| title_sort | conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes |
| topic | Cell cycle Circadian rhythms Gene regulatory networks Transcription factors Network inference |
| url | https://doi.org/10.1186/s12859-022-04627-9 |
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