Codon optimality influences homeostatic gene expression in zebrafish

Abstract

The ribosome plays a crucial role in translating mRNA into protein; however, the genetic code extends beyond merely specifying amino acids. Upon translation, codons, the three-nucleotide sequences interpreted by ribosomes, have regulatory properties affecting mRNA stability, a phenomenon known as codon optimality. Codon optimality has been previously observed in vertebrates during embryogenesis, where specific codons can influence the stability and degradation rates of mRNA transcripts. In our previous work, we demonstrated that codon optimality impacts mRNA stability in human cell lines. However, the extent to which codon content influences vertebrate gene expression in vivo remained unclear. In this study, we expand on our previous findings by demonstrating that codon optimality has a robust effect on homeostatic mRNA and protein levels in whole zebrafish during normal physiological conditions. Using reporters with nearly identical nucleotide sequences but different codon compositions, all expressed from the same genomic locus, we show that codon composition can significantly influence gene expression. This study provides new insights into the regulatory roles of codon usage in vertebrate gene expression and underscores the importance of considering codon optimality in genetic and translational research. These findings have broad implications for understanding the complexities of gene regulation and could inform the design of synthetic genes and therapeutic strategies targeting mRNA stability.

Keywords: codon optimality; gene regulation; homeostasis; mRNA; translation; zebrafish.

Fig. 1.

Codon optimality shapes homeostatic gene expression in zebrafish. a) Schematic illustration of the conceptual effect of codon optimality. An increased percentage of optimal codons are correlated with higher mRNA stability and increased translation efficiency. A greater portion of nonoptimal codons is associated with decreased mRNA stability and lower translation efficiency. b) Diagram depicting the reporter constructs inserted into the zebrafish genome. Each reporter is made up of the same nucleotide sequence apart from a single base insertion after the P2A in the optimal version. This mutation causes a frameshift that results in a change in optimality from being overall nonoptimal to being overall optimal, relative to the endogenous transcriptome. c) Histogram showing the relative optimality vs the number of transcripts for the zebrafish transcriptome. The optimality of the frameshift reporters is indicated, with the optimal scoring more optimal than 88% of endogenous gene and the nonoptimal just above the lowest quartile at 26%. The dotted gray line represents the mean optimality of the transcriptome (Diez et al. 2022). d) Schematic of the generation of transgenic zebrafish. Injection of attB-containing frameshift constructs with phiC31 integrase mRNA into embryos harboring a genomic attP landing site results in mosaic F0 progeny. Upon outcrossing to wild-type animals, F1 generation frameshift optimal, nonoptimal, and mCherry control are produced. Subsequently, the mCherry control line is crossed with each of the frameshift lines to create dual positive F2 offspring. e) Bar plot displaying the phenotypic frequency of GFP and mCherry expression in the frameshift optimal/mCherry, frameshift nonoptimal/mCherry, and mCherry control lines upon outcrossing to wild-type fish. The violet bars show the counts from individual clutches and the numbers describe total larvae counted. f) Schematic showing how homozygous mCherry animals were generated to use a copy number control. Relative mCherry expression of each line is shown in the bar plot. F2 generation frameshift optimal/mCherry fish were in-crossed, producing clutches containing 25% homozygous mCherry progeny. Genomic DNA from frameshift optimal, frameshift nonoptimal/mCherry, and homozygous mCherry fish was assayed via qPCR and normalized by housekeeping gene eef1a1|1. g) Bar plot showing the relative mRNA levels of GFP and mCherry in 8 dpf larvae. Frameshift optimal samples showed a GFP fold change of 4.6× compared with the frameshift nonoptimal samples, while there was no change in mCherry between frameshift optimal and frameshift nonoptimal fish. h) Box and whisker plot of fluorescence intensity measured in the trunk of 8 dpf larvae (the box indicates the IQR, the whiskers show the range of values that are within 1.5×IQR, and a horizontal line indicates the mean). i) Fluorescent images of 8 dpf larvae (right) and schematic indicating the region shown in the images and the visual adjustment used (left). The larger images are shown with brightness and contrast optimized for the frameshift optimal/mCherry line, while the smaller inset is the same image adjusted for the frameshift nonoptimal/mCherry. Dotted white lines outline the perimeter of images too dark to see. Scale bar = 150 mm. For all the pannels: n.s., not significant; *P < 0.05, **P < 0.01, ***P < 0.001 via Welch's 2-tailed t-test.