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Delilah, prospero, and D-Pax2 constitute a gene regulatory network essential for the development of functional proprioceptors.

ABSTRACT: Coordinated animal locomotion depends on the development of functional proprioceptors. While early cell-fate determination processes are well characterized, little is known about the terminal differentiation of cells within the proprioceptive lineage and the genetic networks that control them. In this work we describe a gene regulatory network consisting of three transcription factors-Prospero (Pros), D-Pax2, and Delilah (Dei)-that dictates two alternative differentiation programs within the proprioceptive lineage in Drosophila. We show that D-Pax2 and Pros control the differentiation of cap versus scolopale cells in the chordotonal organ lineage by, respectively, activating and repressing the transcription of dei. Normally, D-Pax2 activates the expression of dei in the cap cell but is unable to do so in the scolopale cell where Pros is co-expressed. We further show that D-Pax2 and Pros exert their effects on dei transcription via a 262 bp chordotonal-specific enhancer in which two D-Pax2- and three Pros-binding sites were identified experimentally. When this enhancer was removed from the fly genome, the cap- and ligament-specific expression of dei was lost, resulting in loss of chordotonal organ functionality and defective larval locomotion. Thus, coordinated larval locomotion depends on the activity of a dei enhancer that integrates both activating and repressive inputs for the generation of a functional proprioceptive organ.

SUBMITTER: Avetisyan A

PROVIDER: S-EPMC8716109 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. RESULTS: To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9. CONCLUSIONS: This work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.

Project description:Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

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Delilah, prospero, and D-Pax2 constitute a gene regulatory network essential for the development of functional proprioceptors.

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