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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A trait syndrome ties cell morphology to glycolysis across the yeast subphylum

Co-variation of traits provides fundamental insights into constraints governing their evolution. An inverse correlation between glucose uptake rates (GURs) and cell surface area-to-volume (SA:V) ratios across 11 yeast species was recently reported. Here, we expand the analysis to 282 species to assess the generalizability of this correlation across Saccharomycotina yeasts and the contribution of shared evolutionary history to the co-variation of these traits. Using phylogenetic regression models, we found extracellular acidification rates (ECARs, used as a proxy for GURs) were weakly, but significantly, correlated with SA:V across Saccharomycotina. ECARs were also correlated with genome sizes and growth rates. Our findings support the reported correlation between GURs and SA:V ratios, but suggest other associated traits, including genome sizes. Specifically, yeasts that consume glucose faster tend to have lower SA:V, faster growth rates, and larger genomes, suggesting a trait syndrome governing several metabolic, genomic, and morphological traits across the yeast subphylum.

biological sciences↗
DOE OSTI2023

Plant–soil feedback under drought: does history shape the future?

Plant–soil feedback (PSF) is widely recognised as a driver of plant community composition, but understanding of its response to drought remains in its infancy. Here, we provide a conceptual framework for the role of drought in PSF, considering plant traits, drought severity, and historical precipitation over ecological and evolutionary timescales. Comparing experimental studies where plants and microbes do or do not share a drought history (through co-sourcing or conditioning), we hypothesise that plants and microbes with a shared drought history experience more positive PSF under subsequent drought. To reflect real-world responses to drought, future studies need to explicitly include plant–microbial co-occurrence and potential co-adaptation and consider the precipitation history experienced by both plants and microbes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with up 85 compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Altogether, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ContScout: sensitive detection and removal of contamination from annotated genomes

Contamination of genomes is an increasingly recognized problem affecting several downstream applications, from comparative evolutionary genomics to metagenomics. Here we introduce ContScout, a precise tool for eliminating foreign sequences from annotated genomes. It achieves high specificity and sensitivity on synthetic benchmark data even when the contaminant is a closely related species, outperforms competing tools, and can distinguish horizontal gene transfer from contamination. A screen of 844 eukaryotic genomes for contamination identified bacteria as the most common source, followed by fungi and plants. Furthermore, we show that contaminants in ancestral genome reconstructions lead to erroneous early origins of genes and inflate gene loss rates, leading to a false notion of complex ancestral genomes. Taken together, we offer here a tool for sensitive removal of foreign proteins, identify and remove contaminants from diverse eukaryotic genomes and evaluate their impact on phylogenomic analyses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ecological trade-offs drive phenotypic and genetic differentiation of Arabidopsis thaliana in Europe

Plant diversity is shaped by trade-offs between traits related to competitive ability, propagule dispersal, and stress resistance. However, we still lack a clear understanding of how these trade-offs influence species distribution and population dynamics. In Arabidopsis thaliana , recent genetic analyses revealed a group of cosmopolitan genotypes that successfully recolonized Europe from its center after the last glaciation, excluding older (relict) lineages from the distribution except for their north and south margins. Here, we tested the hypothesis that cosmopolitans expanded due to higher colonization ability, while relicts persisted at the margins due to higher tolerance to competition and/or stress. We compared the phenotypic and genetic differentiation between 71 European genotypes originating from the center, and the south and north margins. We showed that a trade-off between plant fecundity and seed mass shapes the differentiation of A. thaliana in Europe, suggesting that the success of the cosmopolitan groups could be explained by their high dispersal ability. However, at both north and south margins, we found evidence of selection for alleles conferring low dispersal but highly competitive and stress-resistance abilities. This study sheds light on the role of ecological trade-offs as evolutionary drivers of the distribution and dynamics of plant populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Global metagenomics reveals plastid diversity and unexplored algal lineages

Photosynthetic organelles in eukaryotes originated through primary endosymbiosis with a cyanobacterium, an event that profoundly shaped the evolutionary landscape of the eukaryotic tree of life. Primary plastids in Archaeplastida, especially in cultivable plants and algae, contribute most to known plastid diversity. Secondary and higher-order endosymbiosis, involving eukaryotic hosts and algal endosymbionts, further spread photosynthesis among protists within the CASH lineages (Cryptophyta, Alveolata, Stramenopila, and Haptophyta). Despite various hypotheses explaining secondary plastid evolution and distribution, empirical support remains limited. Here, we employ cultivation-independent global metagenomics to expand plastid diversity and investigate plastid origins. We capture 1,027 plastid sequences, including 300 novel sequences belonging to previously unsequenced plastids and representing yet-to-be described microeukaryotes. This includes a new lineage that offers insights into plastid evolution in haptophytes and cryptophytes. Our results confirm that Archaeplastida plastids originate from an early branching cyanobacterial lineage closely related to Gloeomargaritales and identify the closest extant relative of Paulinella plastids. Additionally, our findings suggest two independent origins of secondary red-algal plastids, contributing to plastid diversity in CASH lineages and challenging the prevailing model of single secondary plastid origin. Our study highlights the importance of metagenomic data in uncovering biological diversity and advancing understanding of plastid relationships across photosynthetic eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. In this perspective, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Perfect and imperfect views of ultraconserved sequences

Across the human genome, there are nearly 500 'ultraconserved' elements: regions of at least 200 contiguous nucleotides that are perfectly conserved in both the mouse and rat genomes. Remarkably, the majority of these sequences are non-coding, and many can function as enhancers that activate tissue-specific gene expression during embryonic development. From their first description more than 15 years ago, their extreme conservation has both fascinated and perplexed researchers in genomics and evolutionary biology. The intrigue around ultraconserved elements only grew with the observation that they are dispensable for viability. Here, in this paper, we review recent progress towards understanding the general importance and the specific functions of ultraconserved sequences in mammalian development and human disease and discuss possible explanations for their extreme conservation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Inverse design of pore wall chemistry and topology through active learning of surface group interactions

Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. Furthermore, this proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.

36 MATERIALS SCIENCE↗
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 OSTI2025

Testing for the Genomic Footprint of Conflict Between Life Stages in an Angiosperm and Moss Species

Abstract The maintenance of genetic variation by balancing selection is of considerable interest to evolutionary biologists. An important but understudied potential driver of balancing selection is antagonistic pleiotropy between diploid and haploid stages of the plant life cycle. Despite sharing a common genome, sporophytes (2n) and gametophytes (n) may undergo differential or even opposing selection. Theoretical work suggests antagonistic pleiotropy between life stages can generate balancing selection and maintain genetic variation. Despite the potential for far-reaching consequences of gametophytic selection, empirical tests of its pleiotropic effects (neutral, synergistic, or antagonistic) on sporophytes are generally lacking. Here, we examined the population genomic signals of selection across life stages in the angiosperm Rumex hastatulus and the moss Ceratodon purpureus. We compared gene expression between life stages and sexes, combined with neutral diversity statistics and the analysis of the distribution of fitness effects. In contrast to what would be predicted under balancing selection due to antagonistic pleiotropy, we found that unbiased genes between life stages were under stronger purifying selection, likely explained by a predominance of synergistic pleiotropy between life stages and strong purifying selection on broadly expressed genes. In addition, we found that 30% of candidate genes under balancing selection in R. hastatulus were located within inversion polymorphisms. Our findings provide novel insights into the genome-wide characteristics and consequences of plant gametophytic selection.

Evolutionary Biology↗
DOE OSTI2026

MAL33 drives natural variation in maltose metabolism in Saccharomyces eubayanus

Maltose is one of the most abundant sugars in brewer’s wort, and its efficient utilization is critical for successful fermentation. However, maltose consumption varies naturally among Saccharomyces eubayanus strains isolated from different host trees, such as Quercus and Nothofagus. To identify the genetic determinants underlying these phenotypic differences, we performed bulk segregant analysis (BSA) and quantitative trait loci (QTL) mapping using an F 2 offspring derived from QC18 (Quercus-associated) and CL467.1 (Nothofagus-associated) strains. QTL mapping identified two significant genomic regions on subtelomeric loci of chromosomes V-R and XVI-L, each containing complete MAL loci composed of MAL32 (encoding maltase), MAL31 (transporter), and MAL33 (transcriptional activator) genes. Comparative polymorphism analyses identified mutations in MAL32 and MAL33 of QC18, including frameshift mutations resulting in premature stop codons. Functional validation demonstrated that the heterologous expression of MAL33 ChrV from CL467.1 fully restored maltose utilization in QC18, indicating the functional presence of MAL33 cis-regulatory sequences and MAL32 and MAL31 genes in QC18. While structural protein predictions identified truncation and impaired functionality in the maltose-responsive activation domain of Mal33p from QC18, overexpression of QC18’s own MAL33 ChrV allele also improved maltose metabolism, suggesting dosage-dependent transcriptional limitations rather than complete functional loss. These results indicate that allelic variations in the maltose-responsive activation domain of Mal33p result in differences in maltose consumption between strains. Here, we hypothesized that reduced maltose metabolism in QC18 is an adaptive response to the distinct sugar composition in Quercus robur bark, contrasting with the starch-rich environment of Nothofagus pumilio. These findings highlight subtelomeric MAL gene diversity as a reservoir of genetic variation, representing a key evolutionary mechanism that influences maltose adaptation among natural Saccharomyces isolates.

evolutionary plasticity↗
DOE OSTI2026

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Nextflow↗
DOE OSTI2025

Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies

A major goal of research in evolution and genetics is linking genotype to phenotype. This work could be direct, such as determining the genetic basis of a phenotype by leveraging genetic variation or divergence in a developmental, physiological, or behavioral trait. The work could also involve studying the evolutionary phenomena (e.g., reproductive isolation, adaptation, sexual dimorphism, behavior) that reveal an indirect link between genotype and a trait of interest. When the phenotype diverges across evolutionarily distinct lineages, this genotype-to-phenotype problem can be addressed using phylogenetic genotype-to-phenotype (PhyloG2P) mapping, which uses genetic signatures and convergent phenotypes on a phylogeny to infer the genetic bases of traits. The PhyloG2P approach has proven powerful in revealing key genetic changes associated with diverse traits, including the mammalian transition to marine environments and transitions between major mechanisms of photosynthesis. However, there are several intermediate traits layered in between genotype and the phenotype of interest, including but not limited to transcriptional profiles, chromatin states, protein abundances, structures, modifications, metabolites, and physiological parameters. Each intermediate trait is interesting and informative in its own right, but synthesis across data types has great promise for providing a deep, integrated, and predictive understanding of how genotypes drive phenotypic differences and convergence. We argue that an expanded PhyloG2P framework (the PhyloG2P matrix) that explicitly considers intermediate traits, and imputes those that are prohibitive to obtain, will allow a better mechanistic understanding of any trait of interest. Furthermore, this approach provides a proxy for functional validation and mechanistic understanding in organisms where laboratory manipulation is impractical.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l

Gene tree discordance in large genomic data sets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The data set included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Benefits and Limits of Phasing Alleles for Network Inference of Allopolyploid Complexes

Abstract Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

Evolutionary Biology↗
DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗