Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions

  1. Anne Plessis
  2. Christoph Hafemeister
  3. Olivia Wilkins
  4. Zennia Jean Gonzaga
  5. Rachel Sarah Meyer
  6. Inês Pires
  7. Christian Müller
  8. Endang M Septiningsih
  9. Richard Bonneau
  10. Michael Purugganan  Is a corresponding author
  1. Plymouth University, United Kingdom
  2. New York University, United States
  3. International Rice Research Institute, Philippines
  4. Simons Foundation, New York, United States
  5. Texas A&M University, United States

Abstract

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the correspondence of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

Article and author information

Author details

  1. Anne Plessis

    School of Biological Sciences, Plymouth University, Plymouth, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Christoph Hafemeister

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Olivia Wilkins

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Zennia Jean Gonzaga

    International Rice Research Institute, Metro Manila, Philippines
    Competing interests
    The authors declare that no competing interests exist.
  5. Rachel Sarah Meyer

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Inês Pires

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Christian Müller

    Simons Center for Data Analysis, Simons Foundation, New York, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Endang M Septiningsih

    Department of Soil and Crop Sciences, Texas A&M University, College Station, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Richard Bonneau

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Michael Purugganan

    Department of Biology, Center for Genomics and Systems Biology, New York University, New York, United States
    For correspondence
    mp132@nyu.edu
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2015, Plessis et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,759
    views
  • 823
    downloads
  • 41
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Anne Plessis
  2. Christoph Hafemeister
  3. Olivia Wilkins
  4. Zennia Jean Gonzaga
  5. Rachel Sarah Meyer
  6. Inês Pires
  7. Christian Müller
  8. Endang M Septiningsih
  9. Richard Bonneau
  10. Michael Purugganan
(2015)
Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions
eLife 4:e08411.
https://doi.org/10.7554/eLife.08411

Share this article

https://doi.org/10.7554/eLife.08411

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Sarah De Beuckeleer, Tim Van De Looverbosch ... Winnok H De Vos
    Research Article

    Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.

    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics
    Bin Zheng, Meimei Duan ... Peng Zheng
    Research Article

    Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.