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Siderophores are crucial for iron-scavenging in microorganisms. While many yeasts can uptake siderophores produced by other organisms, they are typically unable to synthesize siderophores themselves. In contrast, Wickerhamiella/Starmerella (W/S) clade yeasts gained the capacity to make the siderophore enterobactin following the remarkable horizontal acquisition of a bacterial operon enabling enterobactin synthesis. Yet, how these yeasts absorb the iron bound by enterobactin remains unresolved. Here, we demonstrate that Enb1 is the key enterobactin importer in the W/S-clade species Starmerella bombicola. Through phylogenomic analyses, we show that ENB1 is present in all W/S clade yeast species that retained the enterobactin biosynthetic genes. Conversely, it is absent in species that lost the ent genes, except for Starmerella stellata, making this species the only cheater in the W/S clade that can utilize enterobactin without producing it. Through phylogenetic analyses, we infer that ENB1 is a fungal gene that likely existed in the W/S clade prior to the acquisition of the ent genes and subsequently experienced multiple gene losses and duplications. Through phylogenetic topology tests, we show that ENB1 likely underwent horizontal gene transfer from an ancient W/S clade yeast to the order Saccharomycetales, which includes the model yeast Saccharomyces cerevisiae, followed by extensive secondary losses. Taken together, these results suggest that the fungal ENB1 and bacterial ent genes were cooperatively integrated into a functional unit within the W/S clade that enabled adaptation to iron-limited environments. This integrated fungal-bacterial circuit and its dynamic evolution determine the extant distribution of yeast enterobactin producers and cheaters.

 

The Saccharomycotina yeasts (“yeasts” hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km²resolution for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversity. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high-diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and range size (Rapoport’s rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

 

Abstract Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species. 

Polyploids have been theorized to occur more frequently in environments that are subjected to severe conditions or sudden disruptions. Here we test the expectation that polyploid taxa occur more frequently in extreme or disrupted environments than their diploid counterparts, whether due to increased adaptive potential, environmental resilience or cross‐ploidy competition.

South America.

All frog genera in the area with both polyploid and diploid member species (Ceratophrys, Chiasmocleis, Odontophrynus, PhyllomedusaandPleurodema).

In all, 13,556 occurrence records of 82 frog species were collected from the Global Biodiversity Information Facility. Species distribution models, range overlap estimates, statistical tests and principal component analyses were used to estimate and compare environments between diploid and polyploid species within and across genera using several categorical and quantitative variables taken from multiple publicly available sources.

Almost all polyploid occurrences are found within southeastern South America, largely to the exclusion of diploids. Polyploid species occur more closely with intergeneric polyploids than they do with congeneric diploids. Southeastern South America is more temperate, seasonal and less forested when compared to the tropical environments more commonly inhabited by diploids. The habitat ranges of polyploid species are subject to greater temperature fluctuations than diploid species. This region has also experienced major transformations in the modern era, owing to an agriculture boom over the last century. Polyploid occurrences are more likely to be found in areas with greater cropland usage, fertilizer application and pesticide application than diploids.

Across species, temperature seasonality was the only variable with strong statistical differences between diploids and polyploids. Greater annual fluctuations in temperature may lead to more established polyploid species due to reasons mentioned above; however, extreme temperature differences are also known to contribute to polyploid gamete formation, providing a possible non‐selective explanation. Polyploid occurrences are also more likely to be found in areas of high agricultural impact, providing support for the hypothesis that polyploids are more resilient to environmental disruptions than diploids.

 

Abstract Advances in sequencing technology have resulted in the expectation that genomic studies will become more representative of organismal diversity. To test this expectation, we explored species representation of nonhuman eukaryotes in the Sequence Read Archive. Though species richness has been increasing steadily, species evenness is decreasing over time. Moreover, the top 1% most studied organisms increasingly represent a larger proportion of total experiments, demonstrating growing bias in favor of a small minority of species. To better understand molecular processes and patterns, genomic studies should reverse current trends by adopting more comparative approaches.