How enhancers regulate wavelike gene expression patterns
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
- Goethe Universität Frankfurt am Main, Germany
- Graz University of Technology, Austria
- The University of Texas Rio Grande Valley, United States
Abstract
A key problem in development is to understand how genes turn on or off at the right place and right time during embryogenesis. Such decisions are made by non-coding sequences called 'enhancers'. Much of our models of how enhancers work rely on the assumption that genes are activated de novo as stable domains across embryonic tissues. Such view has been strengthened by the intensive landmark studies of the early patterning of the anterior-posterior (AP) axis of the Drosophila embryo, where indeed gene expression domains seem to arise more or less stably. However, careful analysis of gene expression patterns in other model systems (including the AP patterning in vertebrates and short-germ insects like the beetle Tribolium castaneum) painted a different, very dynamic view of gene regulation, where genes are oftentimes expressed in a wavelike fashion. How such gene expression waves are mediated at the enhancer level is so far unclear. Here we establish the AP patterning of the short-germ beetle Tribolium as a model system to study dynamic and temporal pattern formation at the enhancer level. To that end, we established an enhancer prediction system in Tribolium based on time- and tissue-specific ATAC-seq and an enhancer live reporter system based on MS2 tagging. Using this experimental framework, we discovered several Tribolium enhancers, and assessed the spatiotemporal activities of some of them in live embryos. We found our data consistent with a model in which the timing of gene expression during embryonic pattern formation is mediated by a balancing act between enhancers that induce rapid changes in gene expression patterns (that we call 'dynamic enhancers') and enhancers that stabilizes gene expression patterns (that we call 'static enhancers'). However, more data is needed for a strong support for this or any other alternative models.
Data availability
Raw sequence files and scaled coverage tracks (in bigWig format) have been deposited in the Gene Expression Omnibus database under accession number GSE235410. Scaled coverage tracks were also uploaded to the iBeetleBase Genome Browser (https://ibeetle-base.uni-goettingen.de/genomebrowser/) (107). Matlab codes for the Speed Regulation and Enhancer Switching models can be found in (Kuhlmann L, El-Sherif E. Speed regulation and gradual enhancer switching models as flexible and evolvable patterning mechanisms. BioRxiv. 2018 Feb 7), and can be modified based on information and parameter values indicated in Materials and Methods (Computational Modeling) to generate the simulations presented in this study. Generated transgenic Tribolium lines are available upon request.
-
ATAC-seq analysis of chromatin accessibility in Tribolium castaneum germband stage embryosNCBI Gene Expression Omnibus, GSE235410.
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (EL 870/2-1)
- Ezzat El-Sherif
Studienstiftung des Deutschen Volkes
- Christine Mau
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Mau 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
-
- 1,631
- views
-
- 296
- downloads
-
- 6
- 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)
How enhancers regulate wavelike gene expression patterns
eLife 12:e84969.
https://doi.org/10.7554/eLife.84969
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.
