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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Occurrence and molecular characterization of Enterocytozoon bieneusi in wild and domestic animal species in Portugal

Abstract The phylum Microsporidia encompasses a diverse group of obligate, intracellular, and spore-forming organisms able to infect a wide range of animal hosts. Among them, Enterocytozoon bieneusi is the most frequently reported species in humans and animals. Little is known about the presence and epidemiology of E. bieneusi in wildlife. We investigated E. bieneusi occurrence and genetic diversity in wild and domestic mammals, through molecular-detection methods, from different regions across Portugal. A total of 756 samples were collected from 288, 242, and 226 wild carnivores, wild ungulates, and domestic animals, respectively. Overall, eight specimens were E. bieneusi-positive (1.1%, 8/756) obtained from five wild (Iberian lynx, Iberian wolf, red fox, stone marten, and wild boar) and one domestic (sheep) host. Nucleotide sequence analysis identified four genotypes of E. bieneusi, Type IV, Wildboar3, BEB6, and PtEbIX. Three of those genotypes belong to Groups 1 (Type IV and Wildboar3) and 2 (BEB6), which are known to contain genotypes capable of infecting a variety of hosts, including humans, highlighting their public health importance. PtEbIX belongs to the dog-specific Group 11. This study represents the first, largest, and most comprehensive molecular-based epidemiology survey carried out in Portugal in wild and domestic animals to date and the first worldwide identification of E. bieneusi in wolf species. Our study showed that wild carnivores and ungulates may act as reservoirs of zoonotic genotypes of E. bieneusi, establishing their role in maintaining the sylvatic cycle of this parasite while representing a potential source of infection for humans and domestic animals.

Figueiredo, Ana M. (ORCID:0000000226236340)↗
DOE OSTI2023

MaNGA galaxy properties – II. A detailed comparison of observed and simulated spiral galaxy scaling relations

ABSTRACT We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample based on deep optical dark energy sky instrument legacy imaging survey (DESI) photometry processed with autoprof . Rotation curves (RCs), extracted by model-fitting H α velocity maps from the MaNGA Data Analysis Pipeline, extend out to 1.4 (1.9) Re for the primary (secondary) MaNGA samples, respectively. The RCs and ancillary MaNGA Pipe3D data products were used to construct various fundamental galaxy scaling relations that are also compared uniformly with similar relations from numerical investigation of a hundred astrophysical object (NIHAO) zoom-in simulations. Simulated NIHAO galaxies were found to broadly reproduce the observed MaNGA galaxy population for $\log (M_*/{\rm {\rm M}_{\odot })\, \gt\, 8.5}$. Some discrepancies remain, such as those pertaining to central stellar densities and the diversity of RCs due to strong feedback schemes. Also presented are spatially resolved scatters for the velocity-size-stellar mass (VRM*) structural relations using MaNGA and NIHAO samples. The scatter for these relations in the galaxian interiors is a consequence of the diversity of inner RC shapes, while scatter in the outskirts is dictated by the large range of stellar surface densities, which is itself driven by sporadic star formation. The detailed, spatially resolved scatter analysis highlights the complex interplay between local and global astrophysical processes and provides a strong constraint to numerical simulations.

Astronomy & Astrophysics↗
DOE OSTI2023

Fast and efficient identification of anomalous galaxy spectra with neural density estimation

ABSTRACT Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the probabilistic auto-encoder, to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae, and overlapping objects. Conditional modelling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available.

Böhm, Vanessa↗
DOE OSTI2023

A semi-analytic study of self-interacting dark-matter haloes with baryons

ABSTRACT We combine the isothermal Jeans model and the model of adiabatic halo contraction into a semi-analytic procedure for computing the density profile of self-interacting dark-matter (SIDM) haloes with the gravitational influence from the inhabitant galaxies. The model agrees well with cosmological SIDM simulations over the entire core-forming stage up to the onset of gravothermal core-collapse. Using this model, we show that the halo response to baryons is more diverse in SIDM than in CDM and depends sensitively on galaxy size, a desirable feature in the context of the structural diversity of bright dwarfs. The fast speed of the method facilitates analyses that would be challenging for numerical simulations – notably, we quantify the SIDM halo response as functions of the baryonic properties, on a fine mesh grid spanned by the baryon-to-total-mass ratio, Mb/Mvir, and galaxy compactness, r1/2/Rvir; we show with high statistical precision that for typical Milky-Way-like systems, the SIDM profiles are similar to their CDM counterparts; and we delineate the regime of core-collapse in the Mb/Mvir − r1/2/Rvir space, for a given cross section and concentration. Finally, we compare the isothermal Jeans model with the more sophisticated gravothermal fluid model, and show that the former yields faster core formation and agrees better with cosmological simulations. We attribute the difference to whether the target CDM halo is used as a boundary condition or as the initial condition for the gravothermal evolution, and thus comment on possible improvements of the fluid model. We have made our model publicly available at https://github.com/JiangFangzhou/SIDM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Extensive Genome-Wide Phylogenetic Discordance Is Due to Incomplete Lineage Sorting and Not Ongoing Introgression in a Rapidly Radiated Bryophyte Genus

The relative importance of introgression for diversification has long been a highly disputed topic in speciation research and remains an open question despite the great attention it has received over the past decade. Gene flow leaves traces in the genome similar to those created by incomplete lineage sorting (ILS), and identification and quantification of gene flow in the presence of ILS is challenging and requires knowledge about the true phylogenetic relationship among the species. We use whole nuclear, plastid, and organellar genomes from 12 species in the rapidly radiated, ecologically diverse, actively hybridizing genus of peatmoss (Sphagnum) to reconstruct the species phylogeny and quantify introgression using a suite of phylogenomic methods. We found extensive phylogenetic discordance among nuclear and organellar phylogenies, as well as across the nuclear genome and the nodes in the species tree, best explained by extensive ILS following the rapid radiation of the genus rather than by postspeciation introgression. Our analyses support the idea of ancient introgression among the ancestral lineages followed by ILS, whereas recent gene flow among the species is highly restricted despite widespread interspecific hybridization known in the group. Our results contribute to phylogenomic understanding of how speciation proceeds in rapidly radiated, actively hybridizing species groups, and demonstrate that employing a combination of diverse phylogenomic methods can facilitate untangling complex phylogenetic patterns created by ILS and introgression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Fermentation Practices Select for Thermostable Endolysins in Phages

Endolysins are produced by (bacterio)phages and play a crucial role in degrading the bacterial cell wall and the subsequent release of new phage progeny. These lytic enzymes exhibit a remarkable diversity, often occurring in a multimodular form that combines different catalytic and cell wall-binding domains, even in phages infecting the same species. Yet, our current understanding lacks insight into how environmental factors and ecological niches may have influenced the evolution of these enzymes. In this study, we focused on phages infecting Streptococcus thermophilus, as this bacterial species has a well-defined and narrow ecological niche, namely, dairy fermentation. Among the endolysins found in phages targeting this species, we observed limited diversity, with a singular structural type dominating in most of identified S. thermophilus phages. Within this prevailing endolysin type, we discovered a novel and highly conserved calcium-binding motif. This motif proved to be crucial for the stability and activity of the enzyme at elevated temperatures. Ultimately, we demonstrated its positive selection within the host's environmental conditions, particularly under the temperature profiles encountered in the production of yogurt, mozzarella, and hard cheeses that rely on S. thermophilus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Specialization Restricts the Evolutionary Paths Available to Yeast Sugar Transporters

Functional innovation at the protein level is a key source of evolutionary novelties. The constraints on functional innovations are likely to be highly specific in different proteins, which are shaped by their unique histories and the extent of global epistasis that arises from their structures and biochemistries. These contextual nuances in the sequence–function relationship have implications both for a basic understanding of the evolutionary process and for engineering proteins with desirable properties. Here, we have investigated the molecular basis of novel function in a model member of an ancient, conserved, and biotechnologically relevant protein family. These Major Facilitator Superfamily sugar porters are a functionally diverse group of proteins that are thought to be highly plastic and evolvable. By dissecting a recent evolutionary innovation in an α-glucoside transporter from the yeast Saccharomyces eubayanus, we show that the ability to transport a novel substrate requires high-order interactions between many protein regions and numerous specific residues proximal to the transport channel. To reconcile the functional diversity of this family with the constrained evolution of this model protein, we generated new, state-of-the-art genome annotations for 332 Saccharomycotina yeast species spanning ~400 My of evolution. By integrating phylogenetic and phenotypic analyses across these species, we show that the model yeast α-glucoside transporters likely evolved from a multifunctional ancestor and became subfunctionalized. The accumulation of additive and epistatic substitutions likely entrenched this subfunction, which made the simultaneous acquisition of multiple interacting substitutions the only reasonably accessible path to novelty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Quantitative elemental imaging in eukaryotic algae

Abstract All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved by how these elements are utilized, for what purpose, and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status, or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable their operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supplies or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration, and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging, allowing subcellular resolution, with X-ray fluorescence microscopy (XFM, XRF) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Uncovering translation roadblocks during the development of a synthetic tRNA

Ribosomes are remarkable in their malleability to accept diverse aminoacyl-tRNA substrates from both the same organism and other organisms or domains of life. This is a critical feature of the ribosome that allows the use of orthogonal translation systems for genetic code expansion. Optimization of these orthogonal translation systems generally involves focusing on the compatibility of the tRNA, aminoacyl-tRNA synthetase, and a non-canonical amino acid with each other. As we expand the diversity of tRNAs used to include non-canonical structures, the question arises as to the tRNA suitability on the ribosome. Specifically, we investigated the ribosomal translation of allo-tRNA UTu1 , a uniquely shaped (9/3) tRNA exploited for site-specific selenocysteine insertion, using single-molecule fluorescence. With this technique we identified ribosomal disassembly occurring from translocation of allo-tRNA UTu1 from the A to the P site. Using cryo-EM to capture the tRNA on the ribosome, we pinpointed a distinct tertiary interaction preventing fluid translocation. Through a single nucleotide mutation, we disrupted this tertiary interaction and relieved the translation roadblock. With the continued diversification of genetic code expansion, our work highlights a targeted approach to optimize translation by distinct tRNAs as they move through the ribosome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Abstract Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes

Abstract CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genomes OnLine Database (GOLD) v.10: new features and updates

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Antitoxin control of optimal transcriptional repression in the atypical HigB–HigA toxin–antitoxin system from Proteus vulgaris

Abstract Bacterial toxin–antitoxin (TA) pairs transcriptionally autoregulate their expression via a repression/derepression mechanism in response to changing environmental conditions. The structural diversity of TA systems influences the mechanisms of transcriptional regulation. Here, we define the molecular mechanism for the plasmid-encoded HigB–HigA TA pair originally identified in a post-operative infection with antibiotic-resistant Proteus vulgaris. We determine DNA binding and promoter activity by the HigB–HigA complex supported by structural biology and molecular dynamics simulations of an elusive DNA operator–TA repressor complex. To define the optimal oligomeric TA repressor–DNA operator complex required for derepression, we engineered a dedicated trimeric HigB–HigA2 complex that represses transcription more than 26-fold as compared to the tetrameric HigB2–HigA2. These results expand the known diversity of how the HigB–HigA TA family is autoregulated.

Biochemistry & Molecular Biology↗
DOE OSTI2025

CABO-16S—a Combined Archaea, Bacteria, Organelle 16S rRNA database framework for amplicon analysis of prokaryotes and eukaryotes in environmental samples

Abstract Identification of both prokaryotic and eukaryotic microorganisms in environmental samples is currently challenged by the need for additional sequencing to obtain separate 16S and 18S ribosomal RNA (rRNA) amplicons or the constraints imposed by “universal” primers. Organellar 16S rRNA sequences are amplified and sequenced along with prokaryote 16S rRNA and provide an alternative method to identify eukaryotic microorganisms. CABO-16S combines bacterial and archaeal sequences from the SILVA database with 16S rRNA sequences of plastids and other organelles from the PR2 database to enable identification of all 16S rRNA sequences. Comparison of CABO-16S with SILVA 138.2 results in equivalent taxonomic classification of mock communities and increased classification of diverse environmental samples. In particular, identification of phototrophic eukaryotes in shallow seagrass environments, marine waters, and lake waters was increased. The CABO-16S framework allows users to add custom sequences for further classification of underrepresented clades and can be easily updated with future releases of reference databases. Addition of sequences obtained from Sanger sequencing of methane seep sediments and curated sequences of the polyphyletic SEEP-SRB1 clade resulted in differentiation of syntrophic and non-syntrophic SEEP-SRB1 in hydrothermal vent sediments. CABO-16S highlights the benefit of combining and amending existing training sets when studying microorganisms in diverse environments.

Eitel, Eryn M. (ORCID:0009000723919297)↗
DOE OSTI2021

Metabolic Source Isotopic Pair Labeling and Genome-Wide Association Are Complementary Tools for the Identification of Metabolite-Gene Associations in Plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. Here, we identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

09 BIOMASS FUELS↗
DOE OSTI2021

Return of the Lemnaceae: duckweed as a model plant system in the genomics and postgenomics era

Abstract The aquatic Lemnaceae family, commonly called duckweed, comprises some of the smallest and fastest growing angiosperms known on Earth. Their tiny size, rapid growth by clonal propagation, and facile uptake of labeled compounds from the media were attractive features that made them a well-known model for plant biology from 1950 to 1990. Interest in duckweed has steadily regained momentum over the past decade, driven in part by the growing need to identify alternative plants from traditional agricultural crops that can help tackle urgent societal challenges, such as climate change and rapid population expansion. Propelled by rapid advances in genomic technologies, recent studies with duckweed again highlight the potential of these small plants to enable discoveries in diverse fields from ecology to chronobiology. Building on established community resources, duckweed is reemerging as a platform to study plant processes at the systems level and to translate knowledge gained for field deployment to address some of society’s pressing needs. This review details the anatomy, development, physiology, and molecular characteristics of the Lemnaceae to introduce them to the broader plant research community. We highlight recent research enabled by Lemnaceae to demonstrate how these plants can be used for quantitative studies of complex processes and for revealing potentially novel strategies in plant defense and genome maintenance.

Biochemistry & Molecular Biology↗
DOE OSTI2021

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology

Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.

Plant Sciences↗