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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enhancing Terminal Deoxynucleotidyl Transferase Activity on Substrates with 3′ Terminal Structures for Enzymatic De Novo DNA Synthesis

Enzymatic oligonucleotide synthesis methods based on the template-independent polymerase terminal deoxynucleotidyl transferase (TdT) promise to enable the de novo synthesis of long oligonucleotides under mild, aqueous conditions. Intermediates with a 3′ terminal structure (hairpins) will inevitably arise during synthesis, but TdT has poor activity on these structured substrates, limiting its usefulness for oligonucleotide synthesis. Here, we described two parallel efforts to improve the activity of TdT on hairpins: (1) optimization of the concentrations of the divalent cation cofactors and (2) engineering TdT for enhanced thermostability, enabling reactions at elevated temperatures. By combining both of these improvements, we obtained a ~10-fold increase in the elongation rate of a guanine-cytosine hairpin.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Efficient DNA sequence compression with neural networks

Abstract Background The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution-tolerant context models. Findings We benchmark GeCo3 as a reference-free DNA compressor in 5 datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, 2 compilations of archaeal and virus genomes, 4 whole genomes, and 2 collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of $2.4\%$, $7.1\%$, $6.1\%$, $5.8\%$, and $6.0\%$, respectively. To test its performance as a reference-based DNA compressor, we benchmark GeCo3 in 4 datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in $12.4\%$, $11.7\%$, $10.8\%$, and $10.1\%$ over the state of the art. The cost of this compression improvement is some additional computational time (1.7–3 times slower than GeCo2). The RAM use is constant, and the tool scales efficiently, independently of the sequence size. Overall, these values outperform the state of the art. Conclusions GeCo3 is a genomic sequence compressor with a neural network mixing approach that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3.

Silva, Milton↗
DOE OSTI2021

Unified Nanotechnology Format: One Way to Store Them All

The domains of DNA and RNA nanotechnology are steadily gaining in popularity while proving their value with various successful results, including biosensing robots and drug delivery cages. Nowadays, the nanotechnology design pipeline usually relies on computer-based design (CAD) approaches to design and simulate the desired structure before the wet lab assembly. To aid with these tasks, various software tools exist and are often used in conjunction. However, their interoperability is hindered by a lack of a common file format that is fully descriptive of the many design paradigms. Therefore, in this paper, we propose a Unified Nanotechnology Format (UNF) designed specifically for the biomimetic nanotechnology field. UNF allows storage of both design and simulation data in a single file, including free-form and lattice-based DNA structures. By defining a logical and versatile format, we hope it will become a widely accepted and used file format for the nucleic acid nanotechnology community, facilitating the future work of researchers and software developers. Together with the format description and publicly available documentation, we provide a set of converters from existing file formats to simplify the transition. Finally, we present several use cases visualizing example structures stored in UNF, showcasing the various types of data UNF can handle.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems (Final Technical Report)

Genomic variation at the sequence level fundamentally affects the phenotypic state of all organisms at all stages of development, while dynamic processes such as changes in the epigenome (e.g. DNA methylation state) and transcriptome regulate the specific phenotype expressed at any given state of development based upon that genomic variation. In plants, DNA methylation is a particularly important mechanism for both regulating transcriptomic expression and for management of genomic variations that could be deleterious to the organism due to the presence of active retrotransposons in plant genomes. While DNA methylation is heritable, it is also dynamic through a given plant’s development and life cycle, particularly during the development from seed to mature specimen suggesting variations in DNA methylation could be critical regulators of biologically and commercially important phenotypes such as time to flowering; in addition, plant DNA methylation is more complex than that of animals, with methylation of CHG and CHH trinucleotides evident in addition to the better-known CG methylation. The complexity of plant DNA methylation and its interplay with genomic sequence variation, transcriptomics and other epigenomic factors demand a storage and analysis framework that can cope with the complexity both within a single specimen and with analyses that span many individuals and even many species, such as attempts to extend models from model organisms to commercially relevant species. In addition to complexity, the rapid development and proliferation of sequencing technology has led to an explosion of data volume that conventional storage and analysis solutions will likely be unable to cope with in the long run. We proposed to study these with suitable distributed storage and computation and therefore for the application of cloud computing to biological analyses; integrate with existing data sources and compatible with virtually any interface use case, from fully automated shell scripts to notebooks and do all these at scale in this STTR grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Inhibition of DNA methylation in P. soloecismus alters algae productivity

Eukaryotic organisms regulate the organization, structure, and accessibility of their genomes through chromatin remodeling that can be inherited as epigenetic modifications. These DNA and histone protein modifications are ultimately responsible for an organism’s molecular adaptation to the environment, resulting in distinctive phenotypes. Epigenetic manipulation of algae holds yet untapped potential for the optimization of biofuel production and bioproduct formation; however, epigenetic machinery and modes-of-action have not been well characterized in algae. We sought to determine the extent to which the biofuel platform species Picochlorum soloecismus utilizes DNA methylation to regulate its genome. We found candidate genes with domains for DNA methylation in the P. soloecismus genome. Whole-genome bisulfite sequencing revealed DNA methylation in all three cytosine contexts (CpG, CHH, and CHG). While global DNA methylation is low overall (~1.15%), it occurs in appreciable quantities (12.1%) in CpG dinucleotides in a bimodal distribution in all genomic contexts, though terminators contain the greatest number of CpG sites per kilobase. The P. soloecismus genome becomes hypomethylated during the growth cycle in response to nitrogen starvation. Algae cultures were treated daily across the growth cycle with 20 μM 5-aza-2'-deoxycytidine (5AZA) to inhibit propagation of DNA methylation in daughter cells. 5AZA treatment significantly increased optical density and forward and side scatter of cells across the growth cycle (16 days). This increase in cell size and complexity correlated with a significant increase (~66%) in lipid accumulation. Site specific CpG DNA methylation was significantly altered with 5AZA treatment over the time course, though nitrogen starvation itself induced significant hypomethylation in CpG contexts. Genes involved in several biological processes, including fatty acid synthesis, had altered methylation ratios in response to 5AZA; we hypothesize that these changes are potentially responsible for the phenotype of early induction of carbon storage as lipids. This is the first report to utilize epigenetic manipulation strategies to alter algal physiology and phenotype. Collectively, these data suggest these strategies can be utilized to fine-tune metabolic responses, alter growth, and enhance environmental adaption of microalgae for desired outcomes.

5-aza-2'-deoxycytidine↗
DOE OSTI2024

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SARS-CoV-2 spike protein variant binding affinity to an angiotensin-converting enzyme 2 fusion glycoproteins

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of the Coronavirus disease 2019 (Covid-19) pandemic, continues to evolve and circulate globally. Current prophylactic and therapeutic countermeasures against Covid-19 infection include vaccines, small molecule drugs, and neutralizing monoclonal antibodies. SARS-CoV-2 infection is mainly mediated by the viral spike glycoprotein binding to angiotensin converting enzyme 2 (ACE2) on host cells for viral entry. As emerging mutations in the spike protein evade efficacy of spike-targeted countermeasures, a potential strategy to counter SARS-CoV-2 infection is to competitively block the spike protein from binding to the host ACE2 using a soluble recombinant fusion protein that contains a human ACE2 and an IgG1-Fc domain (ACE2-Fc). Here, we have established Chinese Hamster Ovary (CHO) cell lines that stably express ACE2-Fc proteins in which the ACE2 domain either has or has no catalytic activity. The fusion proteins were produced and purified to partially characterize physicochemical properties and spike protein binding. Our results demonstrate the ACE2-Fc fusion proteins are heavily N-glycosylated, sensitive to thermal stress, and actively bind to five spike protein variants (parental, alpha, beta, delta, and omicron) with different affinity. Our data demonstrates a proof-of-concept production strategy for ACE2-Fc fusion glycoproteins that can bind to different spike protein variants to support the manufacture of potential alternative countermeasures for emerging SARS-CoV-2 variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Transportability of exogenous microbial community correlates with interwell connectivity in deep aquifers

Subsurface resource engineering operations often utilize continuous injection of externally-sourced water into geological reservoirs for formation pressure maintenance, resource recovery or energy/waste storage. Such injected water generally contains naturally occurring microbes. Little is known, however, about how the injectate microbes transport through geological media as a community, how such transportability is affected by injector-producer connectivity, and whether such knowledge can be utilized for flowpath characterization. In this study, we analyzed daily-to-weekly timeseries microbial community data from the injected- and produced-fluids of a ten-month flow test at a deep, well-characterized engineered aquifer. We found that the injectate microbial community was distinct from the indigenous community at the amplicon sequence variant (ASV) level, and that the transportability of injectate community towards a given producer, quantified by an “nASV-Overlap” metric we propose, had strong and significant positive correlation with known injector-producer connectivities at our site. This suggests that the better the connectivity, the higher the probability for more injectate species to flow through the interwell region and arrive at a producer. Because interwell connectivity is an important yet usually unknown parameter in subsurface resource engineering, such correlation in turn points to nASV-Overlap as a useful indicator of interwell connectivity for aquifer characterization and long-term monitoring. Based on our findings, an nASV-Overlap-based microbial tracing approach was developed for characterizing and monitoring the relative connectivities across multiple producers with a given injector. A side-by-side comparison between the new nASV-Overlap approach and traditional artificial tracer methods is presented, and their respective strengths and limitations are discussed.

Deep biosphere↗