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Unruh BA, Weidemann DE, Miao L, Kojima S. Coordination of rhythmic RNA synthesis and degradation orchestrates 24- and 12-h RNA expression patterns in mouse fibroblasts. Proc Natl Acad Sci U S A 2024; 121:e2314690121. [PMID: 38315868 PMCID: PMC10873638 DOI: 10.1073/pnas.2314690121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024] Open
Abstract
Circadian RNA expression is essential to ultimately regulate a plethora of downstream rhythmic biochemical, physiological, and behavioral processes. Both transcriptional and posttranscriptional mechanisms are considered important to drive rhythmic RNA expression; however, the extent to which each regulatory process contributes to the rhythmic RNA expression remains controversial. To systematically address this, we monitored RNA dynamics using metabolic RNA labeling technology during a circadian cycle in mouse fibroblasts. We find that rhythmic RNA synthesis is the primary contributor of 24-h RNA rhythms, while rhythmic degradation is more important for 12-h RNA rhythms. These rhythms were predominantly regulated by Bmal1 and/or the core clock mechanism, and the interplay between rhythmic synthesis and degradation has a significant impact in shaping rhythmic RNA expression patterns. Interestingly, core clock RNAs are regulated by multiple rhythmic processes and have the highest amplitude of synthesis and degradation, presumably critical to sustain robust rhythmicity of cell-autonomous circadian rhythms. Our study yields invaluable insights into the temporal dynamics of both 24- and 12-h RNA rhythms in mouse fibroblasts.
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Affiliation(s)
- Benjamin A. Unruh
- Department of Biological Sciences, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA24061
| | - Douglas E. Weidemann
- Department of Biological Sciences, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA24061
| | - Lin Miao
- Department of Biological Sciences, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA24061
| | - Shihoko Kojima
- Department of Biological Sciences, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA24061
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Miao L, Weidemann DE, Ngo K, Unruh BA, Kojima S. A comparative study of algorithms detecting differential rhythmicity in transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.562079. [PMID: 37905086 PMCID: PMC10614781 DOI: 10.1101/2023.10.12.562079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Rhythmic transcripts play pivotal roles in driving the daily oscillations of various biological processes. Genetic or environmental disruptions can lead to alterations in the rhythmicity of transcripts, ultimately impacting downstream circadian outputs, including metabolic processes and even behavior. To statistically compare the differences in transcript rhythms between two or more conditions, several algorithms have been developed to analyze circadian transcriptomic data, each with distinct features. In this study, we compared the performance of seven algorithms that were specifically designed to detect differential rhythmicity. We found that even when applying the same statistical threshold, these algorithms yielded varying numbers of differentially rhythmic transcripts. Nevertheless, the set of transcripts commonly identified as differentially rhythmic exhibited substantial overlap among algorithms. Furthermore, the phase and amplitude differences calculated by these algorithms displayed significant correlations. In summary, our study highlights a high degree of similarity in the results produced by these algorithms. Furthermore, when selecting an algorithm for analysis, it is crucial to ensure the compatibility of input data with the specific requirements of the chosen algorithm and to assess whether the algorithm's output fits the needs of the user.
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Affiliation(s)
- Lin Miao
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Douglas E Weidemann
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Katherine Ngo
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Benjamin A Unruh
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Shihoko Kojima
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
- Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, 24061, USA
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