MOSAIC: A Joint Modeling Methodology for Combined Circadian and Non-Circadian Analysis of Multi-Omics Data

AbstractMotivationCircadian rhythms are approximately 24 hour endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation.ResultsTo determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the MOSAIC (Multi-Omics Selection with Amplitude Independent Criteria) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation.AvailabilityMOSAIC’s full interface is available at https://github.com/delosh653/[email protected] informationSupplementary data are available.at Bioinformatics online.

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MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data

Bioinformatics ◽

10.1093/bioinformatics/btaa877 ◽

2020 ◽

Author(s):

Hannah De los Santos ◽

Kristin P Bennett ◽

Jennifer M Hurley

Keyword(s):

Transcriptional Regulation ◽

Circadian Rhythms ◽

Synthetic Data ◽

Joint Modeling ◽

R Package ◽

Supplementary Information ◽

Circadian Regulation ◽

Proteomic Data ◽

Endogenous Cycles ◽

Post Transcriptional Regulation

Abstract Motivation Circadian rhythms are approximately 24-h endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation. Results To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the Multi-Omics Selection with Amplitude Independent Criteria (MOSAIC) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation. Availability and implementation MOSAIC’s full interface is available at https://github.com/delosh653/MOSAIC. An R package for this functionality, mosaic.find, can be downloaded at https://CRAN.R-project.org/package=mosaic.find. Supplementary information Supplementary data are available at Bioinformatics online.

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Circadian rhythms and post-transcriptional regulation in higher plants

Frontiers in Plant Science ◽

10.3389/fpls.2015.00437 ◽

2015 ◽

Vol 6 ◽

Cited By ~ 34

Author(s):

AndrÃ©s Romanowski ◽

Marcelo J. Yanovsky

Keyword(s):

Transcriptional Regulation ◽

Circadian Rhythms ◽

Higher Plants ◽

Post Transcriptional Regulation

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Post-Transcriptional Circadian Regulation in Macrophages Organizes Temporally Distinct Immunometabolic States

10.1101/2020.02.28.970715 ◽

2020 ◽

Cited By ~ 3

Author(s):

Emily J Collins ◽

Mariana P Cervantes-Silva ◽

George A Timmons ◽

James R O’Siorain ◽

Annie M Curtis ◽

...

Keyword(s):

Transcriptional Regulation ◽

Transcriptional Activation ◽

Mammalian Cells ◽

Metabolic Regulation ◽

Time Course ◽

Mitochondrial Morphology ◽

Circadian Regulation ◽

Atp Production ◽

Post Transcriptional Regulation ◽

The Impact

SUMMARYOur core timekeeping mechanism, the circadian clock, regulates an astonishing amount of cellular physiology and behavior, playing a vital role in organismal fitness. While the mechanics of circadian control over cellular regulation can in part be explained by the transcriptional activation stemming from the positive arm of the clock’s transcription-translation negative feedback loop, research has shown that extensive circadian regulation occurs beyond transcriptional activation in fungal species and data suggest that this post-transcriptional regulation may also be preserved in mammals. To determine the extent to which circadian output is regulated post-transcriptionally in mammalian cells, we comprehensively profiled the transcriptome and proteome of murine bone marrow-derived macrophages in a high resolution, sample rich time course. We found that only 15% of the circadian proteome had corresponding oscillating mRNA and this regulation was cell intrinsic. Ontological analysis of oscillating proteins revealed robust temporal enrichment for protein degradation and translation, providing potential insights into the source of this extensive post-transcriptional regulation. We noted post-transcriptional temporal-gating across a number of connected metabolic pathways. This temporal metabolic regulation further corresponded with rhythms we observed in ATP production, mitochondrial morphology, and phagocytosis. With the strong interconnection between cellular metabolic states and macrophage phenotypes/responses, our work demonstrates that post-transcriptional circadian regulation in macrophages is broadly utilized as a tool to confer time-dependent immune function and responses. As macrophages coordinate many immunological and inflammatory functions, an understanding of this regulation provides a framework to determine the impact of circadian regulation on a wide array of disease pathologies.

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