Dataset Information

Pairing of segmentation clock genes drives robust pattern formation.

ABSTRACT: Gene expression is an inherently stochastic process^1,2; however, organismal development and homeostasis require cells to coordinate the spatiotemporal expression of large sets of genes. In metazoans, pairs of co-expressed genes often reside in the same chromosomal neighbourhood, with gene pairs representing 10 to 50% of all genes, depending on the species^3-6. Because shared upstream regulators can ensure correlated gene expression, the selective advantage of maintaining adjacent gene pairs remains unknown⁶. Here, using two linked zebrafish segmentation clock genes, her1 and her7, and combining single-cell transcript counting, genetic engineering, real-time imaging and computational modelling, we show that gene pairing boosts correlated transcription and provides phenotypic robustness for the formation of developmental patterns. Our results demonstrate that the prevention of gene pairing disrupts oscillations and segmentation, and the linkage of her1 and her7 is essential for the development of the body axis in zebrafish embryos. We predict that gene pairing may be similarly advantageous in other organisms, and our findings could lead to the engineering of precise synthetic clocks in embryos and organoids.

SUBMITTER: Zinani OQH

PROVIDER: S-EPMC7932681 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Dataset Information

Pairing of segmentation clock genes drives robust pattern formation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets