Year: 2026
Type: article
Abstract: How animals repeatedly adapted to life on land is a central question in evolutionary biology. While terrestrialisation occurred independently across animal phyla, it remains unclear whether shared genomic mechanisms underlie these transitions. Here, we combine large-scale comparative genomics, machine learning, and multi-omics data, including proteomics and transcriptomics from stress experiments relevant to terrestrial environmental challenges in 17 species, to investigate the genomic basis of animal terrestrial adaptation. Gene co-expression networks reveal that genes relevant to stress are largely lineage-specific, yet converge in function through the co-option of gene families pre-dating terrestrialisation events. Phylogenomic and machine learning analyses support a dominant role for early-evolving genes, enriched in stress-related functions, paired with a higher gene loss than gain at terrestrialisation nodes. Our findings support a model of lineage-specific genomic changes involving mostly conserved genes that converged at the functional level during the independent transitions to terrestrial life.
Source: Nature Communications
Authors: Gemma I. Martínez-Redondo, Klara Eleftheriadi, Judit Salces-Ortiz, Nuria Escudero, Fernando Ángel Fernández-Álvarez, Belén Carbonetto, Carlos Vargas-Chávez, Raquel García-Vernet, Javier Palma-Guerrero, Libe Rentería, Iñaki Rojo, Cristina Chiva, Eduard Sabidó, Aureliano Bombarely, Rosa Fernández
Institutions: Institut de Biologia Evolutiva, Universitat de Barcelona, Instituto Español de Oceanografía, Institut de Ciències del Mar, Centro Oceanográfico de Gijón, Forschungsinstitut für Biologischen Landbau, Universitat Pompeu Fabra, Barcelona Institute of Science and Technology, Centre for Genomic Regulation, Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7169664153
