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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Miscanthus giganteus Biolistic Transformation Using the Visible RUBY Red Marker Gene to Monitor Transformation Efficiency

Miscanthus × giganteus ( M × g ) is a high-yielding perennial C4 bioenergy crop, but genetic improvement by breeding is constrained by triploid sterility and clonal propagation. Improving genetic transformation methods for M × g would provide opportunities for advantageous trait introgression. Use of an easy to phenotype reporter gene is a promising strategy to improve transformation processes and efficiency. This study presents an efficient novel method for biolistic transformation of inflorescence-derived callus in M × g and demonstrates its efficacy using RUBY, a betalain-based noninvasive reporter that is visible throughout the transformation process. RUBY expression ( Zea mays codon optimized) was visible from callus stage through plantlet development into maturity. RUBY expressing independently transformed plants were confirmed by hygromycin phosphotransferase ELISA and by genomic PCR demonstrating that the RUBY phenotype is sufficient for screening transformants. The Zea mays codon optimized hygromycin selection marker was driven by previously established promoters for Miscanthus, ZmUBI and 2×35S, while the RUBY gene expression was controlled by a known Zea mays C4 promoter, Brachypodium UBI10, newly employed in Miscanthus. The construct containing the 2×35S promoter for hygromycin had a 15.1% transformation efficiency while the ZmUBI promoter had a 20.5% transformation efficiency. This study provides a novel, highly efficient protocol for successful biolistic transformation of M × g for stable expression. This study also demonstrates that RUBY expression can be used as a convenient and powerful monitor of transformation in ongoing and future work to engineer M × g into an improved bioproduct feedstock.

RUBY↗
DOE OSTI2026

Data for Miscanthus giganteus Biolistic Transformation Using the Visible RUBY Red Marker Gene to Monitor Transformation Efficiency

Miscanthus × giganteus ( M × g ) is a high- yielding perennial C4 bioenergy crop, but genetic improvement by breeding is constrained by triploid sterility and clonal propagation. Improving genetic transformation methods for M × g would provide opportunities for advantageous trait introgression. Use of an easy to phenotype reporter gene is a promising strategy to improve transformation processes and efficiency. This study presents an efficient novel method for biolistic transformation of inflorescence- derived callus in M × g and demonstrates its efficacy using RUBY, a betalain-based noninvasive reporter that is visible throughout the transformation process. RUBY expression ( Zea mays codon optimized) was visible from callus stage through plantlet development into maturity. RUBY expressing independently transformed plants were confirmed by hygromycin phosphotransferase ELISA and by genomic PCR demonstrating that the RUBY phenotype is sufficient for screening transformants. The Zea mays codon optimized hygromycin selection marker was driven by previously established promoters for Miscanthus, ZmUBI and 2×35S, while the RUBY gene expression was controlled by a known Zea mays C4 promoter, Brachypodium UBI10, newly employed in Miscanthus. The construct containing the 2×35S promoter for hygromycin had a 15.1% transformation efficiency while the ZmUBI promoter had a 20.5% transformation efficiency. This study provides a novel, highly efficient protocol for successful biolistic transformation of M × g for stable expression. This study also demonstrates that RUBY expression can be used as a convenient and powerful monitor of transformation in ongoing and future work to engineer M × g into an improved bioproduct feedstock. **NOTE: in "TableS2_ProtocolComparison.csv", the data from row 665 to 971 should be removed.

Gene Editing↗
DOE OSTI2021

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut ( Arachis hypogaea L.)

Abstract The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here, we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect—increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanuts, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (P-value < 1.0E−16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Otyama, Paul I.↗
DOE OSTI2020

Population Genetics of Sugar Kelp Throughout the Northeastern United States Using Genome-Wide Markers

An assessment of genetic diversity of marine populations is critical not only for the understanding and preserving natural biodiversity but also for its commercial potential. As commercial demand rises for marine resources, it is critical to generate baseline information for monitoring wild populations. Furthermore, anthropogenic stressors on the coastal environment, such as warming sea temperatures and overharvesting of wild populations, are leading to the destruction of keystone marine species such as kelps. In this study, we conducted a fine-scale genetic analysis using genome-wide high-density markers on Northwest Atlantic sugar kelp. The population structure for a total of 149 samples from the Gulf of Maine (GOM) and Southern New England (SNE) was investigated using AMOVA, FST, admixture, and PCoA. Genome-wide association analyses were conducted for six morphological traits, and the extended Lewontin and Krakauer (FLK) test was used to detect selection signatures. Our results indicate that the GOM region is more heterogeneous than SNE. These two regions have large genetic difference (between-location FST ranged from 0.21 to 0.32) and were separated by Cape Cod, which is known to be the biogeographic barrier for other taxa. We detected one significant SNP (P = 2.03 × 10 -7 ) associated with stipe length, and 248 SNPs with higher-than-neutral differentiation. The findings of this study provide baseline knowledge on sugar kelp population genetics for future monitoring, managing and potentially restoring wild populations, as well as assisting in selective breeding to improve desirable traits for future commercialization opportunities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass

Genomic prediction has accelerated breeding processes and provided mechanistic insights into the genetic bases of complex traits. To further optimize genomic prediction, we assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. Surprisingly, short read-based genome assembly performs comparably to or even better than long read-based assembly. Due to higher gene coverage, exome capture and multi-allelic variants outperform genotyping-by-sequencing and bi-allelic variants, respectively. Tetraploid models show higher prediction accuracy than octoploid models for most traits, likely due to the greater genetic distances among tetraploids. Depending on the trait in question, different types of variants need to be integrated for optimal predictions. Furthermore, our study provides insights into the factors influencing genomic prediction outcomes, guiding best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Beyond Component Optimization: Systems Level Biodesign for Lanthanide Recovery

Global demand for lanthanides (Ln) is projected to rise sharply over the next decade, while geographically concentrated supply chains and the low concentrations and matrix complexity of secondary feedstocks limit the reach of conventional hydro- and pyrometallurgical separation. Engineered biological systems offer a selective, low-energy alternative, and component-level advances in Ln-binding proteins, AI-designed selective scaffolds, and cell-surface display platforms now rival synthetic chelators in affinity and selectivity. These components, however, remain functionally isolated. Currently, there are no engineered chassis coupling recognition, intracellular trafficking, accumulation, and controlled release into an end-to-end pipeline. Here, we outline how new biodesign strategies and chassis selection must move beyond bioleaching to encompass the full recovery pathway. Achieving this requires integrating AI/ML-guided design, genome-scale build tools, high-throughput phenotyping, and biophysical transport modeling within a Design–Build–Test–Learn cycle tuned to recognition, trafficking, accumulation, and release.

Biodesign↗
DOE OSTI2023

Detecting macroevolutionary genotype–phenotype associations using error-corrected rates of protein convergence

On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype–phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of non-synonymous to synonymous substitution rate ratios and developed the novel metric ω C , which measures the error-corrected convergence rate of protein evolution. While ω C distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally non-trivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype–phenotype associations, even in lineages that diverged for hundreds of millions of years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Novel Mutant Alleles Reveal a Role of the Extra-Large G Protein in Rice Grain Filling, Panicle Architecture, Plant Growth, and Disease Resistance

Plant growth and grain filling are the key agronomical traits for grain weight and yield of rice. The continuous improvement in rice yield is required for a future sustainable global economy and food security. The heterotrimeric G protein complex containing a canonical α subunit (RGA1) couples extracellular signals perceived by receptors to modulate cell function including plant development and grain weight. We hypothesized that, besides RGA1, three atypical, extra-large GTP-binding protein (XLG) subunits also regulate panicle architecture, plant growth, development, grain weight, and disease resistance. Here, we identified a role of XLGs in agronomic traits and stress tolerance by genetically ablating all three rice XLGs individually and in combination using the CRISPR/Cas9 genome editing in rice. For this study, eight (three single, two double, and three triple) null mutants were selected. Three XLG proteins combinatorically regulate seed filling, because loss confers a decrease in grain weight from 14% with loss of one XLG and loss of three to 32% decrease in grain weight. Null mutations in XLG2 and XLG4 increase grain size. The mutants showed significantly reduced panicle length and number per plant including lesser number of grains per panicle compared to the controls. Loss-of-function of all individual XLGs contributed to 9% more aerial biomass compared to wild type (WT). The double mutant showed improved salinity tolerance. Moreover, loss of the XLG gene family confers hypersensitivity to pathogens. Our findings suggest that the non-canonical XLGs play important roles in regulating rice plant growth, grain filling, panicle phenotype, stress tolerance, and disease resistance. Genetic manipulation of XLG s has the potential to improve agronomic properties in rice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Isolation, Characterization, and Pathogenicity of Two Pseudomonas syringae Pathovars from Populus trichocarpa Seeds

Pseudomonas syringae is a ubiquitous plant pathogen, infecting both woody and herbaceous plants and resulting in devastating agricultural crop losses. Characterized by a remarkable specificity for plant hosts, P. syringae pathovars utilize a number of virulence factors including the type III secretion system and effector proteins to elicit disease in a particular host species. Here, two Pseudomonas syringae strains were isolated from diseased Populustrichocarpa seeds. The pathovars were capable of inhibiting poplar seed germination and were selective for the Populus genus. Sequencing of the newly described organisms revealed similarity to phylogroup II pathogens and genomic regions associated with woody host-associated plant pathogens, as well as genes for specific virulence factors. The host response to infection, as revealed through metabolomics, is the induction of the stress response through the accumulation of higher-order salicylates. Combined with necrosis on leaf surfaces, the plant appears to quickly respond by isolating infected tissues and mounting an anti-inflammatory defense. This study improves our understanding of the initial host response to epiphytic pathogens in Populus and provides a new model system for studying the effects of a bacterial pathogen on a woody host plant in which both organisms are fully genetically sequenced.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
DOE OSTI2021

Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes

One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanotate) all use a protein-coding training model to predict protein-coding genes, with the latter three allowing for the training model to be created ab initio from the input genome. Different methods are available for creating the training model, and to increase the accuracy of such tools, we present here GOODORFS, a method for identifying protein-coding genes within a set of all possible open reading frames (ORFS). Our workflow begins with taking the amino acid frequencies of each ORF, calculating an entropy density profile (EDP), using KMeans to cluster the EDPs, and then selecting the cluster with the lowest variation as the coding ORFs. To test the efficacy of our method, we ran GOODORFS on 14,179 annotated phage genomes, and compared our results to the initial training-set creation step of four other similar methods (Glimmer, MED2, PHANOTATE, Prodigal). We found that GOODORFS was the most accurate (0.94) and had the best F1-score (0.85), while Glimmer had the highest precision (0.92) and PHANOTATE had the highest recall (0.96).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Chapter 6: An Improved Leaf Protoplast System for Highly Efficient Transient Expression in Switchgrass (Panicum virgatum L.)

As a robust perennial C4-type monocot plant and a native species to North America, switchgrass (Panicum virgatum) has been evaluated and designated as a strong candidate bioenergy crop by the U.S. DOE. Although genetic modifications of switchgrass have been used to successfully reduce the recalcitrance of switchgrass biomass for biofuel production, the generation of transgenic switchgrass is still a slow and laborious process. A transient protoplast system can provide an excellent platform to accelerate the selection of genes-of-interest for tailoring switchgrass biomass. However, partially due to the lack of the complete genomic information, the attempts to optimize the transient protoplast system for switchgrass remain scarce. In this chapter, we provide an improved protocol for switchgrass protoplast isolation, increased transformation efficiency using CsCl gradient ultracentrifugation-derived plasmid DNA and extended application of the transient switchgrass protoplast system to analyze protein expression using western blot.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2023

Screening the complex biological behavior of late lanthanides through genome-wide interactions

Abstract Despite their similar physicochemical properties, recent studies have demonstrated that lanthanides can display different biological behaviors. Hence, the lanthanide series can be divided into three parts, namely early, mid, and late lanthanides, based on their interactions with biological systems. In particular, the late lanthanides demonstrate distinct, but poorly understood biological activity. In the current study, we employed genome-wide functional screening to help understand biological effects of exposure to Yb(III) and Lu(III), which were selected as representatives of the late lanthanides. As a model organism, we used Saccharomyces cerevisiae, since it shares many biological functions with humans. Analysis of the functional screening results indicated toxicity of late lanthanides is consistent with disruption of vesicle-mediated transport, and further supported a role for calcium transport processes and mitophagy in mitigating toxicity. Unexpectedly, our analysis suggested that late lanthanides target proteins with SH3 domains, which may underlie the observed toxicity. This study provides fundamental insights into the unique biological chemistry of late lanthanides, which may help devise new avenues toward the development of decorporation strategies and bio-inspired separation processes.

Pallares, Roger M. (ORCID:0000000174238706)↗
DOE OSTI2024

Biomass yields, reproductive fertility, compositional analysis, and genetic diversity of newly developed triploid giant miscanthus hybrids

Abstract Miscanthus × giganteus (giant miscanthus), first found as a naturally occurring hybrid, has shown promise as a bioenergy/biomass crop throughout much of the temperate world. This allotriploid (2 n = 3 x = 57) hybrid resulted from a cross between tetraploid Miscanthus sacchariflorus (2 n = 4 x = 76) and diploid Miscanthus sinensis (2 n = 2 x = 38) and is particularly desirable due to its low fertility that minimizes reseeding and potential invasiveness. However, there is limited genetic diversity in commonly grown cultivars of triploid M. × giganteus and breeding and development efforts to improve and domesticate this crop have been minimal. Here, we report on newly developed M. × giganteus hybrids compared with the industry standard M. × giganteus '1993‐1780'. Dry biomass yields of new hybrids ranged from 19.5 to 32.4 Mg/ha/year for the fourth growing season, compared with 21.0 Mg/ha/year for M. × giganteus '1993‐1780'. Plant reproductive fertility remained low for all accessions with overall fertility [(seed set × seed germination)/100] ranging from 0.3% to 4.5% for new hybrids compared to 0.4% for M. × giganteus '1993‐1780'. Culm density and height varied among accessions and were positively correlated with increased biomass. Based on compositional analyses, theoretical ethanol yields ranged from 9, 740 to 16,278 L/ha/year for new hybrids compared to 10,406 L/ha/year for M. × giganteus '1993‐1780'. Relative feed value indices were low overall and ranged between 66.0 and 72.8 for new hybrids compared to M. × giganteus '1993‐1780' with 71.3. The genetic diversity of new hybrids, compared with existing cultivars, was characterized using whole genome sequences. Based on pair‐wise distances, cluster analysis clearly showed increased diversity of new hybrids compared with earlier selections. These results document new triploid hybrids of M. × giganteus with enhanced biomass and theoretical ethanol yields in combination with broader genetic diversity and lowreproductive fertility.

Touchell, Darren H.↗
DOE OSTI2024

Modularization of EDGE Workflows Using Nextflow: Improving the Efficiency and Maintainability of Bioinformatics Software

EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Constructs and methods for genome editing and genetic engineering of fungi and protists

Provided herein are constructs for genome editing or genetic engineering its fungi or protists, methods of using the constructs and media for use in selecting cells. The construct include a polynucleotide encoding a thymidine kinase operably connected to a promoter, suitably a constitutive promoter; a polynucleotide encoding an endonuclease operably connected to an inducible promoter; and a recognition site for the endonuclease. The constructs may also include selectable markers for use in selecting recombinations.

Hittinger, Christopher Todd↗
DOE OSTI2020

Marker-free carotenoid-enriched rice generated through targeted gene insertion using CRISPR-Cas9

Targeted insertion of transgenes at pre-determined plant genomic safe harbors provides a desirable alternative to insertions at random sites achieved through conventional methods. Most existing cases of targeted gene insertion in plants have either relied on the presence of a selectable marker gene in the insertion cassette or occurred at low frequency with relatively small DNA fragments (<1.8 kb). Here, we report the use of an optimized CRISPR-Cas9-based method to achieve the targeted insertion of a 5.2 kb carotenoid biosynthesis cassette at two genomic safe harbors in rice. We obtain marker-free rice plants with high carotenoid content in the seeds and no detectable penalty in morphology or yield. Whole-genome sequencing reveals the absence of off-target mutations by Cas9 in the engineered plants. These results demonstrate targeted gene insertion of marker-free DNA in rice using CRISPR-Cas9 genome editing, and offer a promising strategy for genetic improvement of rice and other crops.

59 BASIC BIOLOGICAL SCIENCES↗