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Evolutionary BiologybioRxiv
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One Molecule, Multiple Histories: Comparative Phylogenomics of Glutamate Metabolism, Transport and Receptor Families Across 89 Eukaryotic Proteomes

ANKIT THAKURiD, Mahesh KulhariaiD

Abstract

Glutamate links cellular metabolism with excitatory neurotransmission, yet the evolutionary histories of its metabolic enzymes, transporters, and receptor paralogues have not been compared within a common human-reference-anchored phylogenomic framework. Here, we traced human UniProt/Swiss-Prot-associated protein lineages across 89 eukaryotic proteomes using homology searches, sequence and domain filtering, maximum-likelihood phylogenies assessed with transfer bootstrap expectation (TBE) and ultrafast bootstrap (UFBoot), sequence-stability analyses, and GeneRax gene-tree/species-tree reconciliation under a duplication-loss model. Metabolic enzyme homologues showed the broadest distributions: GLUD, GOT, and GPT were recovered across multiple eukaryotic supergroups, whereas GLS-associated sequences were detected in metazoans and the ichthyosporean Capsaspora owczarzaki. SLC17 and SLC1 transporter homologues were recovered only from sampled metazoans. Receptor distributions varied among families: GRM and GRIN sequences were detected across sampled cnidarians and bilaterians, GRIA and GRIK across bilaterian groups, and GRID only in seven sampled vertebrate species. Reconciliation mapped numerous paralogue-associated duplications to early vertebrate, gnathostome, and osteichthyan ancestral nodes, alongside independent lineage-specific expansions. Although many local phylogenetic relationships were supported, deeper relationships in some transporter and receptor families remained uncertain, with TBE and UFBoot providing different support values. These findings characterize the human-associated glutamatergic repertoire as an evolutionary mosaic of gene families with distinct taxonomic distributions and duplication histories. Because homology searches were anchored on human reference proteins, the inferred distributions represent detectable lineages in the sampled dataset rather than definitive evidence of gene-family origins or absence.

PDFDOI

02 /Specification & Classification

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