IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-62196-w.html
   My bibliography  Save this article

Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock

Author

Listed:
  • Connor Reynolds

    (Norwich Research Park)

  • Joshua Colmer

    (Norwich Research Park
    Centrum)

  • Hannah Rees

    (Aberystwyth University)

  • Ehsan Khajouei

    (Norwich Research Park)

  • Rachel Rusholme-Pilcher

    (Norwich Research Park)

  • Hiroshi Kudoh

    (Kyoto University)

  • Antony N. Dodd

    (Norwich Research Park)

  • Anthony Hall

    (Norwich Research Park
    University of East Anglia)

Abstract

The circadian clock of plants contributes to their survival and fitness. However, understanding clock function at the transcriptome level and its response to the environment requires assaying across high resolution time-course experiments. Generating these datasets is labour-intensive, costly and, in most cases, performed under tightly controlled laboratory conditions. To overcome these barriers, we have developed ChronoGauge: an ensemble model that can reliably estimate the endogenous circadian time of Arabidopsis plants using the expression of a handful of time-indicating genes within a single time-pointed transcriptomic sample. ChronoGauge can predict a plant’s circadian time with high accuracy across unseen Arabidopsis bulk RNA-seq and microarray samples, and can be further applied to make non-random predictions across samples in non-model species, including field samples. Finally, we demonstrate how ChronoGauge can be applied to generate hypotheses regarding the response of the circadian transcriptome to specific genotypes or environmental conditions.

Suggested Citation

  • Connor Reynolds & Joshua Colmer & Hannah Rees & Ehsan Khajouei & Rachel Rusholme-Pilcher & Hiroshi Kudoh & Antony N. Dodd & Anthony Hall, 2025. "Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62196-w
    DOI: 10.1038/s41467-025-62196-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-62196-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-62196-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chia-Yi Cheng & Ying Li & Kranthi Varala & Jessica Bubert & Ji Huang & Grace J. Kim & Justin Halim & Jennifer Arp & Hung-Jui S. Shih & Grace Levinson & Seo Hyun Park & Ha Young Cho & Stephen P. Moose , 2021. "Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Motomu Endo & Hanako Shimizu & Maria A. Nohales & Takashi Araki & Steve A. Kay, 2014. "Tissue-specific clocks in Arabidopsis show asymmetric coupling," Nature, Nature, vol. 515(7527), pages 419-422, November.
    3. Junyan Duan & Michelle N. Ngo & Satya Swaroop Karri & Lam C. Tsoi & Johann E. Gudjonsson & Babak Shahbaba & John Lowengrub & Bogi Andersen, 2024. "tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Marisa Miller & Qingxin Song & Xiaoli Shi & Thomas E. Juenger & Z. Jeffrey Chen, 2015. "Natural variation in timing of stress-responsive gene expression predicts heterosis in intraspecific hybrids of Arabidopsis," Nature Communications, Nature, vol. 6(1), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuwei Qin & Zhijian Liu & Shiqi Gao & Carlos Martínez-Vasallo & Yanping Long & Xinlong Zhu & Bin Liu & Ya Gao & Xiaodong Xu & Maria A. Nohales & Qiguang Xie & Jixian Zhai, 2025. "48-Hour and 24-Hour Time-lapse Single-nucleus Transcriptomics Reveal Cell-type specific Circadian Rhythms in Arabidopsis," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    2. Mark Greenwood & Mirela Domijan & Peter D Gould & Anthony J W Hall & James C W Locke, 2019. "Coordinated circadian timing through the integration of local inputs in Arabidopsis thaliana," PLOS Biology, Public Library of Science, vol. 17(8), pages 1-31, August.
    3. Tessa R. MacNish & Monica F. Danilevicz & Philipp E. Bayer & Mitchell S. Bestry & David Edwards, 2025. "Application of machine learning and genomics for orphan crop improvement," Nature Communications, Nature, vol. 16(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62196-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.