1. Computational and Systems Biology

Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation

  1. Leonardo VM de Assis  Is a corresponding author
  2. Lisbeth Harder
  3. José Thalles Lacerda
  4. Rex Parsons
  5. Meike Kaehler
  6. Ingolf Cascorbi
  7. Inga Nagel
  8. Oliver Rawashdeh
  9. Jens Mittag
  10. Henrik Oster  Is a corresponding author
  1. University of Lübeck, Germany
  2. Karolinska Institute, Sweden
  3. University of Sao Paulo, Brazil
  4. Queensland University of Technology, Australia
  5. University Hospital Schleswig-Holstein, Germany
  6. University of Queensland, Australia
https://doi.org/10.7554/eLife.79405
Share this article
Cite this article
  1. Leonardo VM de Assis
  2. Lisbeth Harder
  3. José Thalles Lacerda
  4. Rex Parsons
  5. Meike Kaehler
  6. Ingolf Cascorbi
  7. Inga Nagel
  8. Oliver Rawashdeh
  9. Jens Mittag
  10. Henrik Oster
(2022)
Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation
eLife 11:e79405.
https://doi.org/10.7554/eLife.79405
  2. Download BibTeX
  3. Download .RIS

Abstract

Diurnal (i.e., 24-hour) physiological rhythms depend on transcriptional programs controlled by a set of circadian clock genes/proteins. Systemic factors like humoral and neuronal signals, oscillations in body temperature, and food intake align physiological circadian rhythms with external time. Thyroid hormones (THs) are major regulators of circadian clock target processes such as energy metabolism, but little is known about how fluctuations in TH levels affect the circadian coordination of tissue physiology. In this study, a high triiodothyronine (T3) state was induced in mice by supplementing T3 in the drinking water, which affected body temperature, and oxygen consumption in a time-of-day dependent manner. 24-hour transcriptome profiling of liver tissue identified 37 robustly and time independently T3 associated transcripts as potential TH state markers in the liver. Such genes participated in xenobiotic transport, lipid and xenobiotic metabolism. We also identified 10 - 15% of the liver transcriptome as rhythmic in control and T3 groups, but only 4% of the liver transcriptome (1,033 genes) were rhythmic across both conditions - amongst these several core clock genes. In-depth rhythm analyses showed that most changes in transcript rhythms were related to mesor (50%), followed by amplitude (10%), and phase (10%). Gene set enrichment analysis revealed TH state dependent reorganization of metabolic processes such as lipid and glucose metabolism. At high T3 levels, we observed weakening or loss of rhythmicity for transcripts associated with glucose and fatty acid metabolism, suggesting increased hepatic energy turnover. In sum, we provide evidence that tonic changes in T3 levels restructure the diurnal liver metabolic transcriptome independent of local molecular circadian clocks.

Data availability

All experimental data was deposited in the Figshare depository (https://doi.org/10.6084/m9.figshare.20376444.v1). Microarray data was deposited in the Gene Expression Omnibus (GEO) database under access code GSE199998 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199998)

The following data sets were generated

Article and author information

Author details

  1. Leonardo VM de Assis

    Institute of Neurobiology, University of Lübeck, Lübeck, Germany
    For correspondence
    leonardo.deassis@uni-luebeck.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5209-0835

  2. Lisbeth Harder

    Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
    Competing interests
    The authors declare that no competing interests exist.

  3. José Thalles Lacerda

    Department of Physiology, University of Sao Paulo, Sao Paulo, Brazil
    Competing interests
    The authors declare that no competing interests exist.

  4. Rex Parsons

    Faculty of Health, Queensland University of Technology, Kelvin Grove, Australia
    Competing interests
    The authors declare that no competing interests exist.

  5. Meike Kaehler

    Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, Kiel, Germany
    Competing interests
    The authors declare that no competing interests exist.

  6. Ingolf Cascorbi

    Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, Kiel, Germany
    Competing interests
    The authors declare that no competing interests exist.

  7. Inga Nagel

    Institute of Experimental and Clinical Pharmacology, University Hospital Schleswig-Holstein, Kiel, Germany
    Competing interests
    The authors declare that no competing interests exist.

  8. Oliver Rawashdeh

    Faculty of Medicine, University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.

  9. Jens Mittag

    Institute for Endocrinology and Diabetes - Molecular Endocrinology, University of Lübeck, Lübeck, Germany
    Competing interests
    The authors declare that no competing interests exist.

  10. Henrik Oster

    Institute of Neurobiology, University of Lübeck, Lübeck, Germany
    For correspondence
    henrik.oster@uni-luebeck.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1414-7068

Funding

Deutsche Forschungsgemeinschaft (353-10/1; GRK-1957; CRC-296 LocoTact"")

  • Henrik Oster

Deutsche Forschungsgemeinschaft (CRC-296 LocoTact" (TP14).")

  • Jens Mittag

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, de Assis 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

  • 1,494
    views
  • 328
    downloads
  • 17
    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. Leonardo VM de Assis
  2. Lisbeth Harder
  3. José Thalles Lacerda
  4. Rex Parsons
  5. Meike Kaehler
  6. Ingolf Cascorbi
  7. Inga Nagel
  8. Oliver Rawashdeh
  9. Jens Mittag
  10. Henrik Oster
(2022)
Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation
eLife 11:e79405.
https://doi.org/10.7554/eLife.79405

Share this article

https://doi.org/10.7554/eLife.79405

Categories and tags

Research organism

Further reading

    1. Computational and Systems Biology

    Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

    Veronika Koren, Simone Blanco Malerba ... Stefano Panzeri
    Research Article

    The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

    1. Computational and Systems Biology

    Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

    Nobuhisa Umeki, Yoshiyuki Kabashima, Yasushi Sako
    Research Article

    The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Discovering root causal genes with high-throughput perturbations

    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.