bioRxivNeurosciencePreprint
Terminal selector and subtype selector function across 200 million years of nematode evolution
Yasmin H. RamadaniD, Curtis M. LoeriD, Hyunsoo YimiD, Luke T GeigeriD, Daniel M. MerrittiD, Itai Antoine TokeriD, Joke Evenblij, Hanh WitteiD, Steven J. CookiD, Ralf J. SommeriD, Oliver HobertiD
Abstract
The evolution of brains is subject to investigation in many different animal groups, each offering unique advantages to advance our understanding of the cellular, molecular and regulatory substrates of evolutionary change. Here, we use two nematode species, C. elegans and P. pacificus, separated by more than 200 million years of evolution to explore how neuronal cell types and the regulatory programs instructing the identity of these cell types have evolved over time. Using gene expression pattern analysis, we compare the differentiation programs of over half of all nematode neuron classes. To explore how the gene regulatory architecture of neuronal differentiation programs evolves, we apply our deep understanding of neuronal differentiation programs, controlled by terminal selectors and subtype selectors in C. elegans. Through mutant analysis of orthologous P. pacificus regulatory factors, we elucidate patterns of conservation and novelties over such substantial evolutionary distance. We discovered striking similarities in terminal selector expression and activities throughout the nervous system but also observed that terminal selectors can acquire novel sites of expression and distinct regulatory capabilities, manifested by changes in effector gene expression and, hence, neuronal phenotypes. Our mutant analysis argues for a buffering of terminal selector function and for an evolutionary lability of differences in closely related neuronal subtypes. Taken together, our analysis reveals molecular substrates of evolutionary change in nervous systems.
- organism: C. elegans
- organism: Drosophila
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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