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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Contrasting modes of macro and microsynteny evolution in a eukaryotic subphylum

Examination of the changes in order and arrangement of homologous genes is key for understanding the mechanisms of genome evolution in eukaryotes. Previous comparisons between eukaryotic genomes have revealed considerable conservation across species that diverged hundreds of millions of years ago (e.g., vertebrates, bilaterian animals, and filamentous fungi). However, understanding how genome organization evolves within and between eukaryotic major lineages remains underexplored. We analyzed high-quality genomes of 120 representative budding yeast species (subphylum Saccharomycotina) spanning ~400 million years of eukaryotic evolution to examine how their genome organization evolved and to compare it with the evolution of animal and plant genome organization. We, in this study, found that the decay of both macrosynteny (the conservation of homologous chromosomes) and microsynteny (the conservation of local gene content and order) was strongly associated with evolutionary divergence across budding yeast major clades. However, although macrosynteny decayed very fast, within ~100 million years, the microsynteny of many genes—especially genes in metabolic clusters (e.g., in the GAL gene cluster)—was much more deeply conserved both within major clades and across the subphylum. We further found that when genomes with similar evolutionary divergence times were compared, budding yeasts had lower macrosynteny conservation than animals and filamentous fungi but higher conservation than angiosperms. In contrast, budding yeasts had levels of microsynteny conservation on par with mammals, whereas angiosperms exhibited very low conservation. Our results provide new insight into the tempo and mode of the evolution of gene and genome organization across an entire eukaryotic subphylum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Plant genotype and rhizobia strain combinations strongly influence the transcriptome under heavy metal stress conditions in Medicago truncatula

Heavy metals such as cadmium (Cd) and mercury (Hg) pose significant threats to plant health and food safety as they are absorbed from the environment. Legumes are generally considered sensitive to heavy metals but possess standing genetic variation for accumulation and tolerance to toxic ions. We conducted a transcriptomic analysis on hydroponically and soil grown Medicago truncatula plants to investigate gene expression responses to Cd and Hg exposure in roots, leaves, and nodules. By using plant genotypes with varying metal tolerance or accumulation levels, we observed distinct clustering of gene ontologies, indicating tissue-specific, genotype-specific, and metal-specific gene expression patterns. Considering the symbiotic relationship between legumes and nitrogen-fixing bacteria, we further examined plant phenotypes and transcriptomes of plant genotypes with contrasting Hg accumulation levels and inoculated them with high or low Hg-tolerant Sinorhizobium medicae strains that have presence-absence variation for a mercury reductase (Mer) operon. Host plants inoculated with the Hg-tolerant rhizobia strain possessing a Mer operon exhibited less reduction in nodule number and plant biomass. A smaller reduction in iron (Fe) distribution in nodules after Hg stress was measured using X-ray Fluorescence (XRF) imaging. Dual transcriptome (host plant and bacteria) analysis of nodules revealed a remarkable decrease in the number of differentially expressed genes (DEGs) and clustering of gene ontologies in plants inoculated with the Hg-tolerant rhizobia strain, including symbiosis related genes. This finding suggests that the Hg-tolerant rhizobia strain has the potential to mitigate Hg stress in host plants. Furthermore, we observed genotype by-genotype interactions between the high Hg accumulating plant genotype and the Hg-tolerant rhizobia strain. These findings provide insights into enhancing plant resilience in contaminated environments through optimizing legume-rhizobia interactions for heavy metal tolerance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Discovery of cyanophycin dipeptide hydrolase enzymes suggests widespread utility of the natural biopolymer cyanophycin

Cyanophycin is a bacterial polymer mainly used for nitrogen storage. It is composed of a peptide backbone of L-aspartate residues with L-arginines attached to their side chains through isopeptide bonds. Cyanophycin is degraded in two steps: Cyanophycinase cleaves the polymer into β-Asp-Arg dipeptides, which are hydrolyzed into free Asp and Arg by enzymes possessing isoaspartyl dipeptide hydrolase activity. Two unrelated enzymes with this activity, isoaspartyl dipeptidase (IadA) and isoaspartyl aminopeptidase (IaaA) have been shown to degrade β-Asp-Arg dipeptides, but bacteria which encode cyanophycin-metabolizing genes can lack iaaA and iadA genes. In this study, we investigate a previously uncharacterized enzyme whose gene can cluster with cyanophycin-metabolizing genes. This enzyme, which we name cyanophycin dipeptide hydrolase (CphZ), is specific for dipeptides derived from cyanophycin degradation. Accordingly, a co-complex structure of CphZ and β-Asp-Arg shows that CphZ, unlike IadA or IaaA, recognizes all portions of its β-Asp-Arg substrate. Bioinformatic analyses showed that CphZ is found in very many proteobacteria and is homologous to an uncharacterized protein encoded in the “arginine/ornithine transport” (aot) operon of many pseudomonas species, including Pseudomonas aeruginosa . In vitro assays show that AotO is indeed a CphZ, and in cellulo growth experiments show that this enzyme and the aot operon allow P. aeruginosa to take up and use β-Asp-Arg as a sole carbon and nitrogen source. Together the results establish the novel, highly specific enzyme subfamily of CphZs, suggesting that cyanophycin is potentially used by a much wider range of bacteria than previously appreciated.

Science & Technology - Other Topics↗
DOE OSTI2022

A Novel Trichothecene Toxin Phenotype Associated with Horizontal Gene Transfer and a Change in Gene Function in Fusarium

Fusarium trichothecenes are among the mycotoxins of most concern to food and feed safety. Production of these mycotoxins and presence of the trichothecene biosynthetic gene (TRI) cluster have been confirmed in only two multispecies lineages of Fusarium: the Fusarium incarnatum-equiseti (Incarnatum) and F. sambucinum (Sambucinum) species complexes. Here, we identified and characterized a TRI cluster in a species that has not been formally described and is represented by Fusarium sp. NRRL 66739. This fungus is reported to be a member of a third Fusarium lineage: the F. buharicum species complex. Cultures of NRRL 66739 accumulated only two trichothecenes, 7-hydroxyisotrichodermin and 7-hydroxyisotrichodermol. Although these are not novel trichothecenes, the production profile of NRRL 66739 is novel, because in previous reports 7-hydroxyisotrichodermin and 7-hydroxyisotrichodermol were components of mixtures of 6–8 trichothecenes produced by several Fusarium species in Sambucinum. Heterologous expression analysis indicated that the TRI13 gene in NRRL 66739 confers trichothecene 7-hydroxylation. This contrasts the trichothecene 4-hydroxylation function of TRI13 in other Fusarium species. Phylogenetic analyses suggest that NRRL 66739 acquired the TRI cluster via horizontal gene transfer from a close relative of Incarnatum and Sambucinum. These findings provide insights into evolutionary processes that have shaped the distribution of trichothecene production among Fusarium species and the structural diversity of the toxins.

Food Science & Technology↗
NASA NTRSDate not supplied

Genetic Determinants of Microbial Survival in Space

Space flight agencies envision a future for humankind beyond Earth, including missions back to the Moon and to Mars in the coming decades. Sending humans into space inevitably includes their microbiomes as well, leading to trillions of bacteria being shed in their living areas. These bacteria shape the lives of their hosts as well as their environment; thus, it is crucial to understand the adaptations of these microbial spacefarers in spaceflight conditions. We aimed to elucidate the genetic determinants of microbial survival in space using a pan-genome analysis of 12 genera cultured from the International Space Station (ISS) from 2017 to 2018. Analysis was performed on each of the genera individually with terrestrial analogs to identify the core and accessory genomes of the spaceflight and terrestrial strains. We then compared the flight and terrestrial core and accessory genomes for each genera using a Bray-Curtis index and visualized the resulting dissimilarity using an Non-Metric Dimensional Scaling plot. The core proteins available in only the spaceflight organisms were then manually characterized for function and genomic location. In every core genome comparison in each genus, there was significant dissimilarity in the core of the spaceflight organisms when compared to the terrestrial organisms. This trend was present in some of the accessory genomes, but was not ubiquitous. Functional analysis of the core content of the ISS genomes showed the majority of genes unique to the core were clustered by location. These gene clusters suggested a set of genetic determinants confer survival in spacecraft-built environments, notably through the uptake of extracellular DNA such as bacteriophage and plasmids. The clear difference between spaceflight and terrestrial microorganisms shows that spaceflight conditions are selective, which has long term implications for their human hosts and environments.

MoBE↗
NASA NTRS2000

From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data

We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays. The essential steps are (I) clustering many genes by their expression time-course data into a minimal set of clusters of co-expressed genes, (2) theoretically modeling the various conditions under which the time-courses are measured using a continuous-time analog recurrent neural network for the cluster mean time-courses, (3) fitting such a regulatory model to the cluster mean time courses by simulated annealing with weight decay, and (4) analysing several such fits for commonalities in the circuit parameter sets including the connection matrices. This procedure can be used to assess the adequacy of existing and future gene expression time-course data sets for determining transcriptional regulatory relationships such as coregulation.

Mjolsness, Eric↗
DOE OSTI2023

Sphingobium lignivorans sp. nov., isolated from river sediment downstream of a paper mill

Here, a bacterial isolate, B1D3A T , was isolated from river sediment collected from the Hiwassee River near Calhoun, TN, by enrichment culturing with a model 5–5' lignin dimer, dehydrodivanillate, as its sole carbon source. B1D3A T was also shown to utilize several model lignin-derived monomers and dimers as sole carbon sources in a variety of minimal media. Cells were Gram-stain-negative, aerobic, motile, rod-shaped and formed yellow/cream-coloured colonies on rich agar. Optimal growth occurred at 30°C, pH 7–8, and in the absence of NaCl. The major fatty acids of B1D3A T were C 18:1 ω7c and C 17:1 ω6c. The predominant hydroxy fatty acids were C 14: 0 2-OH and C 15:0 2-OH. The polar lipid profile consisted of a mixture of phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, phosphatidyldimethylethanolamine and sphingoglycolipid. B1D3A T contained spermidine as the only major polyamine. The major isoprenoid quinone was Q-10 with minor amounts of Q-9 and Q-11. The genomic DNA G+C content of B1D3A T was 65.6mol%. Phylogenetic analyses based on 16S rRNA gene sequences and coding sequences of 49 core, universal genes defined by Clusters of Orthologous Groups gene families indicated that B1D3A T was a member of the genus Sphingobium. B1D3A T was most closely related to Sphingobium sp. SYK-6, with a 100% 16S rRNA gene sequence similarity. B1D3A T showed 78.1–89.9%average nucleotide identity and 19.5–22.2% digital DNA–DNA hybridization identity with other type strains from the genus Sphingobium. On the basis of phenotypic and genotypic properties and phylogenetic inference, strain B1D3A T should be classified as representing a novel species of the genus Sphingobium, for which the name Sphingobium lignivorans sp. nov. is proposed. The type strain is strain B1D3A T (ATCC TSD-279 T =DSM 111877 T ).

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Effects of Simulated Microgravity and LINC-Complex on Chromosome 8 of Human Mammary Cells

Previously, human breast epithelial cells were studied under simulated microgravity to determine the role of the LINC-complex in maintaining nuclear morphology and gene expression. Data from that study was used in this project to understand how the LINC-complex influences gene expression at chromosome 8 under simulated microgravity. Differentially expressed genes found in clusters typically indicate a molecular relationship amongst them. Visual and data analyses (GO, pathways) identified 51 genes within clusters on chromosome 8 across various simulated microgravity conditions that did not share any molecular relationship. Additional results also determined that cells with a disrupted LINC-complex experienced more differential gene expression than the control, and that extended periods of simulated microgravity exacerbated differential gene expression in chromosome 8 of LINC-disrupted cells. These results suggest that the stressful simulated microgravity environment coupled with disrupted biological processes, such as in the case of the LINC-complex, can increase differential gene expression in chromosomes. Future studies where additional analyses are performed to further analyze gene expression of clusters under simulated microgravity could potentially elucidate the molecular relationship amongst affected genes.

Simulated microgravity↗
NASA NTRS2002

Saccharomyces cerevisiae gene expression changes during rotating wall vessel suspension culture

This study utilizes Saccharomyces cerevisiae to study genetic responses to suspension culture. The suspension culture system used in this study is the high-aspect-ratio vessel, one type of the rotating wall vessel, that provides a high rate of gas exchange necessary for rapidly dividing cells. Cells were grown in the high-aspect-ratio vessel, and DNA microarray and metabolic analyses were used to determine the resulting changes in yeast gene expression. A significant number of genes were found to be up- or downregulated by at least twofold as a result of rotational growth. By using Gibbs promoter alignment, clusters of genes were examined for promoter elements mediating these genetic changes. Candidate binding motifs similar to the Rap1p binding site and the stress-responsive element were identified in the promoter regions of differentially regulated genes. This study shows that, as in higher order organisms, S. cerevisiae changes gene expression in response to rotational culture and also provides clues for investigations into the signaling pathways involved in gravitational response.

Non-NASA Center↗
DOE OSTI2021

Identifying Genomic Islands with Deep Neural Networks

Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Computer Vision↗
NASA NTRSDate not supplied

Biological Insights at the Interface of Multiple Arabidopsis Legacy Datasets

The NASA GeneLab database includes an open-access collection of datasets yielded by space biology experiments. Six Gene Lab Data Sets (GLDS’s) performed in Arabidopsis were selected for analysis (7/17/44/121/205/213), all of which included transcriptome data from spaceflight and ground control environments. Hardware, ecotype, environmental conditions, and other experimental conditions varied, allowing the observations of overarching gene expression impacts of microgravity on plant life without focusing on effects of specific experimental conditions. Using GeneLab pre-processed datasets as the basis for the study, RNA microarray data were analyzed to identify genes that showed altered expression in microgravity when compared to control samples for each individual GLDS. All differentially expressed genes were compared to locate differentially expressed genes common between spaceflight experiments. The most noteworthy result is that not one gene shared differential expression among the six GLDS’s. However, gene expression was not influenced randomly by the microgravity environment, as there were several gene ontology terms that were significantly enriched across all experiments. These included 20 significantly enriched biological processes, and although the genes which enriched each term varied, there were many cases of specific genes common to clusters of multiple GLDS’s. Gene expression such as NAC92 and ERF011 or membrane structural element FFP6 provide insight and direction toward understanding the plant response to spaceflight. Characterizing these common processes and the shared differentially expressed genes has demonstrated potential targets for further study to understand and modulate the biological response of plants in microgravity. Life on Earth has never been subjected to the absence of gravity as a selective pressure, so observing how life forms react to a microgravity environment could provide insight to our shared fundamental biological processes. It is also feasible that the genetic modification of specific genes linked to the microgravity response could improve health and yield of space crops.

Joseph Emhof↗
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

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potentialroles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Hu, Yingwei↗
DOE OSTI2020

Repeated horizontal gene transfer of GAL actose metabolism genes violates Dollo’s law of irreversible loss

Dollo’s law posits that evolutionary losses are irreversible, thereby narrowing the potential paths of evolutionary change. While phenotypic reversals to ancestral states have been observed, little is known about their underlying genetic causes. The genomes of budding yeasts have been shaped by extensive reductive evolution, such as reduced genome sizes and the losses of metabolic capabilities. However, the extent and mechanisms of trait reacquisition after gene loss in yeasts have not been thoroughly studied. Here, through phylogenomic analyses, we reconstructed the evolutionary history of the yeast galactose utilization pathway and observed widespread and repeated losses of the ability to utilize galactose, which occurred concurrently with the losses of GALactose (GAL) utilization genes. Unexpectedly, we detected multiple galactose-utilizing lineages that were deeply embedded within clades that underwent ancient losses of galactose utilization. We show that at least two, and possibly three, lineages reacquired the GAL pathway via yeast-to-yeast horizontal gene transfer. Our results show how trait reacquisition can occur tens of millions of years after an initial loss via horizontal gene transfer from distant relatives. Furthermore, these findings demonstrate that the losses of complex traits and even whole pathways are not always evolutionary dead-ends, highlighting how reversals to ancestral states can occur.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells. Copyright 2000 Academic Press.

Review↗
DOE OSTI2024

Expression, purification, and characterization of diacylated Lipo-YcjN from Escherichia coli

YcjN is a putative substrate binding protein expressed from a cluster of genes involved in carbohydrate import and metabolism in Escherichia coli. Here, we determine the crystal structure of YcjN to a resolution of 1.95 Å, revealing that its three-dimensional structure is similar to substrate binding proteins in subcluster D-I, which includes the well-characterized maltose binding protein. Furthermore, we found that recombinant overexpression of YcjN results in the formation of a lipidated form of YcjN that is posttranslationally diacylated at cysteine 21. Comparisons of size-exclusion chromatography profiles and dynamic light scattering measurements of lipidated and nonlipidated YcjN proteins suggest that lipidated YcjN aggregates in solution via its lipid moiety. Additionally, bioinformatic analysis indicates that YcjN-like proteins may exist in both Bacteria and Archaea, potentially in both lipidated and nonlipidated forms. Together, our results provide a better understanding of the aggregation properties of recombinantly expressed bacterial lipoproteins in solution and establish a foundation for future studies that aim to elucidate the role of these proteins in bacterial physiology.

Escherichia coli↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗