Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression
- PMID: 37926676
- DOI: 10.1002/bies.202300130
Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression
Abstract
Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.
Keywords: gene regulation; intron seqFISH; mRNA co-expression; mRNA degradation; promoter toggling.
© 2023 The Authors. BioEssays published by Wiley Periodicals LLC.
References
REFERENCES
-
- Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., Lao, K., & Surani, M. A. (2009). mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods, 6(5), 377-382. https://doi.org/10.1038/nmeth.1315
-
- Mahata, B., Zhang, X., Kolodziejczyk, A. A., Proserpio, V., Haim-Vilmovsky, L., Taylor, A. E., Hebenstreit, D., Dingler, F. A., Moignard, V., Göttgens, B., Arlt, W., McKenzie, A. N., & Teichmann, S. A (2014). Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Reports, 7(4), 1130-1142. https://doi.org/10.1016/j.celrep.2014.04.011
-
- Lamere, A. T., & Li, J. (2019). Inference of gene co-expression networks from single-cell RNA-sequencing data. In Yuan, G.-C. (Ed.), Computational methods for single-cell data analysis (pp 141-153). Springer, New York.
-
- Farahbod, M., & Pavlidis, P. (2020). Untangling the effects of cellular composition on coexpression analysis. Genome Research, 30(6), 849-859. https://doi.org/10.1101/gr.256735.119
-
- McCall, M. N., Illei, P. B., & Halushka, M. K. (2016). Complex sources of variation in tissue expression data: Analysis of the GTEx lung transcriptome. American Journal of Human Genetics, 99(3), 624-635. https://doi.org/10.1016/j.ajhg.2016.07.007
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources