bioRxivBioinformaticsPreprint
Evolutionary Diversification of Nitric Oxide Signaling Components Across Metazoa: A Comparative Phylogenomic Analysis
Abstract
Nitric oxide (NO) is an evolutionarily ancient gaseous signaling molecule in animals, yet the evolutionary history of multiple components spanning NO synthesis, substrate regulation, sensing, and signal termination has not been examined in a single integrated phylogenetic framework across Metazoa. Here, we trace the phylogenetic and gene-tree/species-tree histories of ten core NO-pathway components across 89 eukaryotic proteomes spanning Amoebozoa, Excavata, Fungi, Archaeplastida, and Opisthokonta - including Porifera, Placozoa, Cnidaria, Ctenophora, and Bilateria - with a focus on the 69 opisthokont species that anchor the animal comparisons. The results reveal a strikingly modular evolutionary architecture. Nitric oxide synthase (NOS) is broadly conserved across bilaterian lineages, and reconciliation analyses show that NOS diversification was driven predominantly by speciation rather than lineage-specific duplication - supporting the relative conservation of NOS across the sampled bilaterian lineages . By contrast, the arginine-recycling enzymes ASS1 and ASL show ancestral duplications and inferred secondary losses in specific lineages, while arginase isoforms (ARG1/ARG2) and the cGMP-degrading enzyme PDE5A underwent extensive, independent duplications across metazoan groups. GUCY1A1- and GUCY1B1-like sequences were recovered across several metazoan lineages, but the two subunits exhibited partially divergent evolutionary trajectories. Together, these patterns support a model in which a comparatively conserved NO-producing component coexists with more dynamic diversification of associated pathway gene families, providing an evolutionary framework for investigating how NO-cGMP signaling may have been differentially deployed in nervous systems.
- organism: human
- organism: mouse
- organism: rat
- assay: proteomics
- research focus: phylogenetics / evolution
- approach: statistical model
- Citations
- 0
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