Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation
- University of Lübeck, Germany
- Karolinska Institute, Sweden
- University of Sao Paulo, Brazil
- Queensland University of Technology, Australia
- University Hospital Schleswig-Holstein, Germany
- University of Queensland, Australia
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)
-
Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementationGene Expression Omnibus, GSE199998.
Article and author information
Author details
Leonardo VM de Assis
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
-
- 1,490
- 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)
Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation
eLife 11:e79405.
https://doi.org/10.7554/eLife.79405
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
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.
-
- Computational and Systems Biology
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.
