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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Identifying emerging phenomenon in long temporal phenotyping experiments

Abstract Motivation The rapid improvement of phenotyping capability, accuracy and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. Results We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust and biologically significant. Availability and implementation The source code, manual and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗
DOE OSTI2019

ECHO: an application for detection and analysis of oscillators identifies metabolic regulation on genome-wide circadian output

Abstract Motivation Time courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems. Results We have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and in recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms. Availability and implementation ECHO’s full interface is available at https://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded at https://CRAN.R-project.org/package=echo.find. Supplementary information Supplementary data are available at Bioinformatics online.

De los Santos, Hannah↗
DOE OSTI2019

Multi-scale structural analysis of proteins by deep semantic segmentation

Abstract Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.

Eguchi, Raphael R.↗
DOE OSTI2020

Methanobactin from methanotrophs: genetics, structure, function and potential applications

ABSTRACT Aerobic methane-oxidizing bacteria of the Alphaproteobacteria have been found to express a novel ribosomally synthesized post-translationally modified polypeptide (RiPP) termed methanobactin (MB). The primary function of MB in these microbes appears to be for copper uptake, but MB has been shown to have multiple capabilities, including oxidase, superoxide dismutase and hydrogen peroxide reductase activities, the ability to detoxify mercury species, as well as acting as an antimicrobial agent. Herein, we describe the diversity of known MBs as well as the genetics underlying MB biosynthesis. We further propose based on bioinformatics analyses that some methanotrophs may produce novel forms of MB that have yet to be characterized. We also discuss recent findings documenting that MBs play an important role in controlling copper availability to the broader microbial community, and as a result can strongly affect the activity of microbes that require copper for important enzymatic transformations, e.g. conversion of nitrous oxide to dinitrogen. Finally, we describe procedures for the detection/purification of MB, as well as potential medical and industrial applications of this intriguing RiPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The origin of aerobic methanotrophy within the Proteobacteria

ABSTRACT Aerobic methanotrophs play critical roles in the global carbon cycle, but despite their environmental ubiquity, they are phylogenetically restricted. Via bioinformatic analyses, it is shown that methanotrophy likely arose from methylotrophy from the lateral gene transfer of either of the two known forms of methane monooxygenase (particulate and soluble methane monooxygenases). Moreover, it appears that both known forms of pyrroloquinoline quinone-dependent methanol dehydrogenase (MeDH) found in methanotrophs—the calcium-containing Mxa-MeDH and the rare earth element-containing Xox-MeDH—were likely encoded in the genomes before the acquisition of the methane monooxygenases (MMOs), but that some methanotrophs subsequently received an additional copy of Xox-MeDH-encoding genes via lateral gene transfer. Further, data are presented that indicate the evolution of methanotrophy from methylotrophy not only required lateral transfer of genes encoding for methane monooxygenases, but also likely the pre-existence of a means of collecting copper. Given the emerging interest in valorizing methane via biological platforms, it is recommended that future strategies for heterologous expression of methane monooxygenase for conversion of methane to methanol also include cloning of genes encoding mechanism(s) of copper uptake, especially for expression of particulate methane monooxygenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High-resolution crystal structure of the Borreliella burgdorferi PlzA protein in complex with c-di-GMP: new insights into the interaction of c-di-GMP with the novel xPilZ domain

ABSTRACT In the tick-borne pathogens, Borreliella burgdorferi and Borrelia hermsii, c-di-GMP is produced by a single diguanylate cyclase (Rrp1). In these pathogens, the Plz proteins (PlzA, B and C) are the only c-di-GMP receptors identified to date and PlzA is the sole c-di-GMP receptor found in all Borreliella isolates. Bioinformatic analyses suggest that PlzA has a unique PilZN3-PilZ architecture with the relatively uncommon xPilZ domain. Here, we present the crystal structure of PlzA in complex with c-di-GMP (1.6 Å resolution). This is the first structure of a xPilz domain in complex with c-di-GMP to be determined. PlzA has a two-domain structure, where each domain comprises topologically equivalent PilZ domains with minimal sequence identity but remarkable structural similarity. The c-di-GMP binding site is formed by the linker connecting the two domains. While the structure of apo PlzA could not be determined, previous fluorescence resonance energy transfer data suggest that apo and holo forms of the protein are structurally distinct. The information obtained from this study will facilitate ongoing efforts to identify the molecular mechanisms of PlzA-mediated regulation in ticks and mammals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Novel approaches in function-driven single-cell genomics

Deeper sequencing and improved bioinformatics in conjunction with single-cell and metagenomic approaches continue to illuminate undercharacterized environmental microbial communities. This has propelled the 'who is there, and what might they be doing' paradigm to the uncultivated and has already radically changed the topology of the tree of life and provided key insights into the microbial contribution to biogeochemistry. While characterization of 'who' based on marker genes can describe a large fraction of the community, answering 'what are they doing' remains the elusive pinnacle for microbiology. Function-driven single-cell genomics provides a solution by using a function-based screen to subsample complex microbial communities in a targeted manner for the isolation and genome sequencing of single cells. This enables single-cell sequencing to be focused on cells with specific phenotypic or metabolic characteristics of interest. Recovered genomes are conclusively implicated for both encoding and exhibiting the feature of interest, improving downstream annotation and revealing activity levels within that environment. This emerging approach has already improved our understanding of microbial community functioning and facilitated the experimental analysis of uncharacterized gene product space. Here we provide a comprehensive review of strategies that have been applied for function-driven single-cell genomics and the future directions we envision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Into the wild: new yeast genomes from natural environments and new tools for their analysis

Genomic studies of yeasts from the wild have increased significantly in the past few years. This revolution has been fueled by advances in high-throughput sequencing technologies and a better understanding of yeast ecology and phylogeography, especially for biotechnologically important species. The present review aims to first introduce new bioinformatic tools available for the generation and analysis of yeast genomes. Furthermore, we assess the accumulated genomic data of wild isolates of industrially relevant species, such as Saccharomyces spp., which provide unique opportunities to further investigate the domestication processes associated with the fermentation industry and opportunistic pathogenesis. The availability of genome sequences of other less conventional yeasts obtained from the wild has also increased substantially, including representatives of the phyla Ascomycota (e.g. Hanseniaspora) and Basidiomycota (e.g. Phaffia). Here, we review salient examples of both fundamental and applied research that demonstrate the importance of continuing to sequence and analyze genomes of wild yeasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Towards yeast taxogenomics: lessons from novel species descriptions based on complete genome sequences

In recent years, ‘multi-omic’ sciences have affected all aspects of fundamental and applied biological research. Yeast taxonomists, though somewhat timidly, have begun to incorporate complete genomic sequences into the description of novel taxa, taking advantage of these powerful data to calculate more reliable genetic distances, construct more robust phylogenies, correlate genotype with phenotype and even reveal cryptic sexual behaviors. Furthermore, the use of genomic data in formal yeast species descriptions is far from widespread. The present review examines published examples of genome-based species descriptions of yeasts, highlights relevant bioinformatic approaches, provides recommendations for new users and discusses some of the challenges facing the genome-based systematics of yeasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Chromosome-Level Genome Assembly of Mentha longifolia L. Reveals Gene Organization Underlying Disease Resistance and Essential Oil Traits

Mentha longifolia (L.) Huds., a wild, diploid mint species, has been developed as a model for mint genetic and genomic research to aid breeding efforts that target Verticillium wilt disease resistance and essential oil monoterpene composition. Here, we present a near-complete, chromosome-scale mint genome assembly for M. longifolia USDA accession CMEN 585. This new assembly is an update of a previously published genome draft, with dramatic improvements. A total of 42,107 protein-coding genes were annotated and placed on 12 chromosomal scaffolds. One hundred fifty-three genes contained conserved sequence domains consistent with nucleotide binding site-leucine-rich-repeat (NBS-LRR) plant disease resistance genes. Homologs of genes implicated in Verticillium wilt resistance in other plant species were also identified. Multiple paralogs of genes putatively involved in p-menthane monoterpenoid biosynthesis were identified and several cases of gene clustering documented. Heterologous expression of candidate genes, purification of recombinant target proteins, and subsequent enzyme assays allowed us to identify the genes underlying the pathway that leads to the most abundant monoterpenoid volatiles. Here, the bioinformatic and functional analyses presented here are laying the groundwork for using marker-assisted selection in improving disease resistance and essential oil traits in mints.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

OrthoPhyl—streamlining large-scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales

Abstract There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

The Medical Science DMZ

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbpsþ) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches

We describe the use of in silico approaches to improve the process of molecular assay development and reduce time and cost by utilizing available databases of whole genome pathogen sequences combined with modern bioinformatics and physical modeling tools. Well-characterized assays are needed for accurately detecting pathogens in environmental and patient samples and also for evaluation of the efficacy of a medical countermeasure that may be administered to patients. The polymerase chain reaction (PCR) remains the gold standard for pathogen detection due to the simplicity of its instrumentation, low cost of reagents, and outstanding limit of detection (LOD), sensitivity, and specificity. However, creation of such PCR assays often involves iterations of design, preliminary testing, and thorough validation with clinical isolates and testing in relevant matrices, which can be time consuming, costly, and result in suboptimal assays. Since formal validation (e.g., for Emergency Use Authorization [EUA] or Food and Drug Administration [FDA] licensure) of an infectious disease assay can be very expensive and can require extensive time of development, having a well-designed assay up front is a critical first step. Yet, many assays described in the literature utilized limited design capabilities and many initially promising assays fail the validation process, resulting in increased costs and timelines for successful product development. While the computational approaches outlined in this document by no means obviate the need for wet lab testing, they can reduce the amount of effort wasted on empirical optimization and iterative redesigns and also guide validation studies. The proposed computational approaches also result in higher performing assays with better sensitivity, specificity, and lower LOD and reduce the possibility of assay failure due to signature erosion. To provide clarity, an extensive glossary of defined terms is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cytochromes P450 involved in bacterial RiPP biosyntheses

Abstract Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a large class of secondary metabolites that have garnered scientific attention due to their complex scaffolds with potential roles in medicine, agriculture, and chemical ecology. RiPPs derive from the cleavage of ribosomally synthesized proteins and additional modifications, catalyzed by various enzymes to alter the peptide backbone or side chains. Of these enzymes, cytochromes P450 (P450s) are a superfamily of heme-thiolate proteins involved in many metabolic pathways, including RiPP biosyntheses. In this review, we focus our discussion on P450 involved in RiPP pathways and the unique chemical transformations they mediate. Previous studies have revealed a wealth of P450s distributed across all domains of life. While the number of characterized P450s involved in RiPP biosyntheses is relatively small, they catalyze various enzymatic reactions such as C–C or C–N bond formation. Formation of some RiPPs is catalyzed by more than one P450, enabling structural diversity. With the continuous improvement of the bioinformatic tools for RiPP prediction and advancement in synthetic biology techniques, it is expected that further cytochrome P450-mediated RiPP biosynthetic pathways will be discovered. Summary The presence of genes encoding P450s in gene clusters for ribosomally synthesized and post-translationally modified peptides expand structural and functional diversity of these secondary metabolites, and here, we review the current state of this knowledge.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Computer-aided, resistance gene-guided genome mining for proteasome and HMG-CoA reductase inhibitors

Secondary metabolites (SMs) are biologically active small molecules, many of which are medically valuable. Fungal genomes contain vast numbers of SM biosynthetic gene clusters (BGCs) with unknown products, suggesting that huge numbers of valuable SMs remain to be discovered. It is challenging, however, to identify SM BGCs, among the millions present in fungi, that produce useful compounds. One solution is resistance gene-guided genome mining, which takes advantage of the fact that some BGCs contain a gene encoding a resistant version of the protein targeted by the compound produced by the BGC. The bioinformatic signature of such BGCs is that they contain an allele of an essential gene with no SM biosynthetic function, and there is a second allele elsewhere in the genome. We have developed a computer-assisted approach to resistance gene-guided genome mining that allows users to query large databases for BGCs that putatively make compounds that have targets of therapeutic interest. Working with the MycoCosm genome database, we have applied this approach to look for SM BGCs that target the proteasome β6 subunit, the target of the proteasome inhibitor fellutamide B, or HMG-CoA reductase, the target of cholesterol reducing therapeutics such as lovastatin. Our approach proved effective, finding known fellutamide and lovastatin BGCs as well as fellutamide- and lovastatin-related BGCs with variations in the SM genes that suggest they may produce structural variants of fellutamides and lovastatin. Gratifyingly, we also found BGCs that are not closely related to lovastatin BGCs but putatively produce novel HMG-CoA reductase inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Polymerase chain reaction for the in vitro detection of the pESI plasmid associated with the globally circulating Salmonella Infantis outbreak strain

Abstract A globally circulating strain of Salmonella enterica serotype Infantis containing the pESI plasmid has increased in prevalence in poultry meat samples and cases of human infections. In this study, a polymerase chain reaction (PCR) protocol was designed to detect the pESI plasmid and confirm the Infantis serotype of Salmonella isolates. Primers were tested bioinformatically to predict specificity, sensitivity, and precision. A total of 54 isolates of Salmonella serotypes Infantis, Senftenberg, and Alachua were tested, with and without the pESI plasmid carriage. Isolates of 31 additional serotypes were also screened to confirm specificity to Infantis. Specificity, sensitivity, and precision of each primer were >0.95. All isolates tested produced the expected band sizes. This PCR protocol provides a rapid and clear result for the detection of the pESI plasmid and serotype Infantis and will allow for the in vitro detection for epidemiological studies where whole-genome sequencing is not available.

McMillan, Elizabeth A. (ORCID:000000027503514X)↗
DOE OSTI2020

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Machine learning predicts new anti-CRISPR proteins

The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

59 BASIC BIOLOGICAL SCIENCES↗