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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis—nonadditive interactions between amino acid substitutions—and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences—a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

biocatalysis↗
DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Intraspecific Reaction Norm Variation Controls the Eco-Evolutionary Consequences of Environmental Change

As environmental change accelerates globally, understanding concurrent organismal, species, and community responses is increasingly vital. Here, we examine these collective responses by incorporating genotype-specific thermal reaction norms into an eco-evolutionary predator-prey model, allowing us to track simultaneous phenotypic, ecological, and evolutionary responses to environmental change within ecological communities. We show that the reaction norms expressed by genotypes within a population determine how a community switches between different eco-evolutionary outcomes with changes in temperature. We identify how different components of phenotypic variation in thermal reaction norms—environmental (E), additive environmental and genetic (E + G), and gene-by-environment interactions (G × E)—influence eco-evolutionary dynamics and outcomes as temperature changes. Furthermore, our findings underscore how complex eco-evolutionary responses to environmental change ultimately emerge from variation in reaction norms among genotypes, offering new mechanistic insights into environmental impacts on adaptation, the maintenance of phenotypic and genetic variation, and ecological stability, which is crucial for understanding and predicting eco-evolutionary effects of rapid environmental change in the future.

Eco-evolutionary↗
DOE OSTI2025

Ecological acclimation: A framework to integrate fast and slow responses to climate change

Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.

climate adaptation↗
DOE OSTI2026

Hubble Space Telescope Imaging of Three Isolated Faint Dwarf Galaxies beyond the Local Group: Pavo, Corvus A, and Kamino

We present new Hubble Space Telescope (HST) imaging of three recently discovered star-forming dwarf galaxies beyond the Local Group: Pavo, Corvus A, and Kamino. The discovery of Kamino is reported here for the first time. They rank among the most isolated faint dwarf galaxies known; hence they provide unique opportunities to study galaxy evolution at the smallest scales, free from the environmental effects of more massive galaxies. Our HST data reach ∼2–4 magnitudes below the tip of the red giant branch (TRGB) for each dwarf, allowing us to measure their distances, structural properties, and recent star formation histories (SFHs). All three galaxies contain a complex stellar population of young and old stars, and are typical of field galaxies in this mass regime (M V = −10.62 ± 0.08 and $D = 2.16_{-0.07}^{+0.08}$ Mpc for Pavo, M V = −10.91 ± 0.10 and D = 3.34 ± 0.11 Mpc for Corvus A, and M V = −12.02 ± 0.12 and $D = 6.50_{-0.11}^{+0.15}$ Mpc for Kamino). Our HST-derived SFHs reveal differences among the three dwarfs: Pavo and Kamino show relatively steady, continuous star formation, while Corvus A formed ∼60% of its stellar mass by 10 Gyr ago. These results align with theoretical predictions of diverse evolutionary pathways for isolated low-mass galaxies.

Mutlu-Pakdil, Burçin [Dartmouth College, Hanover, ↗
DOE OSTI2025

Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.

Pau, Stephanie [Department of Geography University↗
DOE OSTI2026

Telomere-to-telomere assemblies of chromosome 10 reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by Helitrons

The model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases ( KCS ), which has been difficult to confirm with short-read assemblies. Long-read sequencing enables the development of telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. Our analysis of 78 long-read haplotypes from chromosome 10 identified more than twice as many KCS genes as previously reported, and numerous intragenic non-synonymous substitutions. Random Forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Additionally, amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a Helitron transposon. A phylogenetic analysis suggests Helitrons are the evolutionary mechanism for generating KCS tandem arrays. Long-read generated telomere-to-telomere assemblies of P. trichocarpa chromosome 10 revealed large-effect loci critical to genetic studies that are unattainable from short-reads. This new resource produced novel insights into genome structure and function, and a novel mechanism for generating tandem gene duplication. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

09 BIOMASS FUELS↗
DOE OSTI2026

Cryogenic electron tomography by the numbers: Charting underexplored lineages in structural cell biology

Imaging cells and their interactions across the whole biosphere with molecular-scale resolution is key for understanding structure–function relations. Cryogenic electron tomography (cryo-ET) is a powerful method for obtaining this critical information. However, cryo-ET studies are challenging and often limited to a small number of cell types per study. Here, we collate cryo-ET data from hundreds of cells and tissues across the biosphere to i) identify emerging methodological trends, ii) pinpoint strategies to reduce imaging time and costs, iii) quantitatively compare methods for cell freezing and sectioning, and iv) census cryo-ET species coverage across all domains of life. Comparing the fraction of cellular material within a single lamella across all domains of life reveals an order of magnitude difference between eukaryotes (1%) compared to bacteria (9%) and archaea (14%). We calculate the fraction of cellular material which can be imaged using distinct sectioning methods on multicellular communities and tissues—identifying serial lift-out as a powerful approach for obtaining more complete cellular depictions. Finally, we show that the biodiversity of current cryo-ET studies is 2 to 3 orders of magnitude lower than in sequence libraries and 4 to 5 lower than the total predicted on Earth. Our analyses reveal major evolutionary lineages which remain critically understudied and highlight where future cryo-ET research would be most impactful.

HPF↗
DOE OSTI2024

Extending evolutionary forecasts across bacterial species

Improving evolutionary forecasting requires progressing from studying repeated evolution of a single genotype under identical conditions to formulating broad principles. These principles should enable predictions of how similar species will adapt to similar selective pressures. Evolve-and-resequence experiments with multiple species allow testing forecasts on different biological levels and elucidating the causes for failed predictions. Here, we show that forecasts for adaptation to static culture conditions can be extended to multiple species by testing previous predictions for Pseudomonas syringae and Pseudomonas savastanoi. In addition to sequence divergence, these species differ in their repertoire of biofilm regulatory genes and structural components. Consistent with predictions, both species repeatedly produced biofilm mutants with a wrinkly spreader phenotype. Predominantly, mutations occurred in the wsp operon, with less frequent promoter mutations near uncharacterized diguanylate cyclases. However, mutational patterns differed on the gene level, which was explained by a lack of conservation in relative fitness of mutants between more divergent species. The same mutation was the most frequent for both species suggesting that conserved mutation hotspots can increase parallel evolution. This study shows that evolutionary forecasts can be extended across species, but that differences in the genotype–phenotype–fitness map and mutational biases limit predictability on a detailed molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗
DOE OSTI2025

Functional protein mining with conformal guarantees

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multisubstrate specificity shaped the complex evolution of the aminotransferase family across the tree of life

Aminotransferases (ATs) are an ancient enzyme family that play central roles in core nitrogen metabolism, essential to all organisms. However, many of the AT enzyme functions remain poorly defined, limiting our fundamental understanding of the nitrogen metabolic networks that exist in different organisms. Here, we traced the deep evolutionary history of the AT family by analyzing AT enzymes from 90 species spanning the tree of life (ToL). We found that each organism has maintained a relatively small and constant number of ATs. Mapping the distribution of ATs across the ToL uncovered that many essential AT reactions are carried out by taxon-specific AT enzymes due to wide-spread nonorthologous gene displacements. This complex evolutionary history explains the difficulty of homology-based AT functional prediction. Biochemical characterization of diverse aromatic ATs further revealed their broad substrate specificity, unlike other core metabolic enzymes that evolved to catalyze specific reactions today. Interestingly, however, we found that these AT enzymes that diverged over billion years share common signatures of multisubstrate specificity by employing different nonconserved active site residues. These findings illustrate that AT family enzymes had leveraged their inherent substrate promiscuity to maintain a small yet distinct set of multifunctional AT enzymes in different taxa. This evolutionary history of versatile ATs likely contributed to the establishment of robust and diverse nitrogen metabolic networks that exist throughout the ToL. The study provides a critical foundation to systematically determine diverse AT functions and underlying nitrogen metabolic networks across the ToL.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Constraints on White Dwarf Hydrogen Layer Masses Using Gravitational Redshifts

The hydrogen envelope is the outermost layer of a DA white dwarf; it makes up the entirety of the stellar photosphere, and yet its typical extent is difficult to model theoretically and remains poorly observationally constrained. As a result, hydrogen envelope mass is a substantial source of systematic uncertainty in the physical properties of white dwarfs, including overall masses and cooling ages. In this work, we fit a Gaussian mixture model to gravitational redshifts from high-resolution spectroscopy, paired with radius measurements from Gaia BP/RP spectra, to measure the mass–radius relation for a sample of 468 white dwarfs. Our results are in excellent agreement with the predicted mass–radius relations of state-of-the-art evolutionary models, including those from the MESA Isochrones and Stellar Tracks (MIST) library. We find that mass–radius relations such as those from MIST that assume a thick and mass-dependent hydrogen envelope are preferred by the observed probability density function over models that assume a hydrogen envelope of constant mass. Proper treatment of the evolution of white dwarf progenitors is thus important for accurately modeling the mass–radius relation. Our results indicate that gravitational redshift measurements of large samples of white dwarfs in wide binaries are promising probes of the hydrogen envelope masses of DA white dwarfs.

Astronomy and AstroPhysics↗
DOE OSTI2026

The macroevolution of filamentation morphology across the Saccharomycotina yeast subphylum

Saccharomycotina is a subphylum of ascomycete fungi with diverse asexual growth morphologies. Filamentous growth can comprise linear and branched budding cells that do not undergo cell separation, termed pseudohyphae, or tubular filaments with septa that perforate allowing movement of organelles, termed true hyphae. We integrated phenotypic, genomic, metabolic, and environmental data on isolation sources from 1051 species to examine the variation and evolutionary history of filamentation across Saccharomycotina and determine whether these data could predict filamentation types. We found that 63.37% of strains can form filaments; 6.56% true hyphae, 42.40% pseudohyphae, and 14.39% both true hyphae and pseudohyphae. The distributions of species that can produce true hyphae or filament were more strongly correlated with the yeast phylogeny than the distribution of species with pseudohyphae. Ancestral state reconstruction suggested that true hyphal and pseudohyphal morphologies evolved several times, that most yeast ancestors likely produced pseudohyphae or lacked filaments, and that the Saccharomycotina last common ancestor likely produced pseudohyphae but not true hyphae. Machine learning models trained on genomic and metabolic features predicted filament morphologies with ∼70% accuracy. Connecting the evolution of morphologies to their genomic, physiological, and ecological characteristics will enrich our understanding of how the diversity of lifestyles evolved in Saccharomycotina.

Saccharomycotina↗
DOE OSTI2024

Eco-evolutionary strategies for relieving carbon limitation under salt stress differ across microbial clades

With the continuous expansion of saline soils under climate change, understanding the eco-evolutionary tradeoff between the microbial mitigation of carbon limitation and the maintenance of functional traits in saline soils represents a significant knowledge gap in predicting future soil health and ecological function. Through shotgun metagenomic sequencing of coastal soils along a salinity gradient, we show contrasting eco-evolutionary directions of soil bacteria and archaea that manifest in changes to genome size and the functional potential of the soil microbiome. In salt environments with high carbon requirements, bacteria exhibit reduced genome sizes associated with a depletion of metabolic genes, while archaea display larger genomes and enrichment of salt-resistance, metabolic, and carbon-acquisition genes. This suggests that bacteria conserve energy through genome streamlining when facing salt stress, while archaea invest in carbon-acquisition pathways to broaden their resource usage. These findings suggest divergent directions in eco-evolutionary adaptations to soil saline stress amongst microbial clades and serve as a foundation for understanding the response of soil microbiomes to escalating climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Divergent trait controls on soluble sugars and starch underlie global strategies of tree carbohydrate storage

Nonstructural carbohydrate (NSC) stores buffer tree metabolism, osmotic regulation, and defense, thereby mediating tolerance and survival under climate extremes. Yet, the functional and evolutionary determinants of interspecific variation in NSC remain elusive, limiting understanding and prediction of forest carbon allocation and mortality under global change. Here, we present a cross-species synthesis of NSC concentrations across multiple organs for 281 woody species from 102 mixed forest communities worldwide, where we quantified species-specific deviations from community means to disentangle intrinsic trait effects from environmental and methodological variation. We found phylogenetic signals in NSC deviations, with coniferous gymnosperms and evergreen species consistently maintaining lower stem soluble sugars and starch concentrations than co-occurring angiosperms and deciduous species, respectively. A global pattern emerged where greater stomatal sensitivity to leaf water potential was associated with declines in the relative concentrations of both sugars and starch. In contrast, xylem hydraulic safety traits showed weak and organ-dependent relationships with NSC concentrations. Sugars increased with photosynthetic capacity and declined with wood density, whereas starch showed the reverse pattern, which aligned with the distinct functional-metabolic roles of sugars and starch. By integrating trait-based ecology with a community-centered framework, our study provides global evidence that stomatal regulation, photosynthetic capacity, specific leaf area, and wood density jointly govern interspecific NSC variation, through contrasting effects on sugars and starch. These are among the most broadly measured traits globally, thus the emergent carbohydrate–trait relationships can have broad applications toward understanding and predicting forest growth and survival under climate change.

tropic system↗
DOE OSTI2024

Diverse signatures of convergent evolution in cactus-associated yeasts

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity.

59 BASIC BIOLOGICAL SCIENCES↗