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Abstract Habitat transitions have shaped the evolutionary trajectory of many clades. Sea catfishes (Ariidae) have repeatedly undergone ecological transitions, including colonizing freshwaters from marine environments, leading to an adaptive radiation in Australia and New Guinea alongside non-radiating freshwater lineages elsewhere. Here, we generate and analyze one long-read reference genome and 66 short-read whole genome assemblies, in conjunction with genomic data for 54 additional species. We investigate how three major ecological transitions have shaped genomic variation among ariids over their ~ 50 million-year evolutionary history. Our results show that relatively younger freshwater lineages exhibit a higher incidence of positive selection than their more ancient marine counterparts. They also display a larger disparity in body shapes, a trend that correlates with a heightened occurrence of positive selection on genes associated with body size and elongation. Although positive selection in the Australia and New Guinea radiation does not stand out compared to non-radiating lineages overall, selection across the prolactin gene family during the marine-to-freshwater transition suggests that strong osmoregulatory adaptations may have facilitated their colonization and radiation. Our findings underscore the significant role of selection in shaping the genome and organismal traits in response to habitat shifts across macroevolutionary scales. 

Abstract BackgroundProtein–protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein–protein interactions and produce high-quality multimeric structural models. ResultsApplication of our method to the Human and Yeast genomes yield protein–protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3. We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4. ConclusionsThe presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.