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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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6 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Complex formation by the human Rad51B and Rad51C DNA repair proteins and their activities in vitro

The human Rad51 protein is essential for DNA repair by homologous recombination. In addition to Rad51 protein, five paralogs have been identified: Rad51B/Rad51L1, Rad51C/Rad51L2, Rad51D/Rad51L3, XRCC2, and XRCC3. To further characterize a subset of these proteins, recombinant Rad51, Rad51B-(His)(6), and Rad51C proteins were individually expressed employing the baculovirus system, and each was purified from Sf9 insect cells. Evidence from nickel-nitrilotriacetic acid pull-down experiments demonstrates a highly stable Rad51B.Rad51C heterodimer, which interacts weakly with Rad51. Rad51B and Rad51C proteins were found to bind single- and double-stranded DNA and to preferentially bind 3'-end-tailed double-stranded DNA. The ability to bind DNA was elevated with mixed Rad51 and Rad51C, as well as with mixed Rad51B and Rad51C, compared with that of the individual protein. In addition, both Rad51B and Rad51C exhibit DNA-stimulated ATPase activity. Rad51C displays an ATP-independent apparent DNA strand exchange activity, whereas Rad51B shows no such activity; this apparent strand exchange ability results actually from a duplex DNA destabilization capability of Rad51C. By analogy to the yeast Rad55 and Rad57, our results suggest that Rad51B and Rad51C function through interactions with the human Rad51 recombinase and play a crucial role in the homologous recombinational repair pathway.

Non-NASA Center↗
NASA NTRS1994

Correlation approach to identify coding regions in DNA sequences

Recently, it was observed that noncoding regions of DNA sequences possess long-range power-law correlations, whereas coding regions typically display only short-range correlations. We develop an algorithm based on this finding that enables investigators to perform a statistical analysis on long DNA sequences to locate possible coding regions. The algorithm is particularly successful in predicting the location of lengthy coding regions. For example, for the complete genome of yeast chromosome III (315,344 nucleotides), at least 82% of the predictions correspond to putative coding regions; the algorithm correctly identified all coding regions larger than 3000 nucleotides, 92% of coding regions between 2000 and 3000 nucleotides long, and 79% of coding regions between 1000 and 2000 nucleotides. The predictive ability of this new algorithm supports the claim that there is a fundamental difference in the correlation property between coding and noncoding sequences. This algorithm, which is not species-dependent, can be implemented with other techniques for rapidly and accurately locating relatively long coding regions in genomic sequences.

Non-NASA Center↗
NASA NTRSDate not supplied

Graphical User Interface (GUI) Implementation for Agent-Based Microbial Radiobiology Model

Sending human life past the Low Earth Orbit (LEO) to explore the Moon and Mars will be challenging. The Earth’s magnetic field naturally protects life from deep-space particle radiation such as Galactic Cosmic Rays (GCR) and Solar Particle Events (SPE); these will pose health risks to humans in deep space. Research has been done to investigate these effects, like BioSentinel, the first biological CubeSat to fly beyond the LEO, designed to culture yeast in a microfluidic device and record optical measurements of growth and metabolism. However, experiments can only report cell damage as bulk growth curves, while deep-space radiation causes damage that is heterogeneous among individual cells. AMMPER is an open-source, agent-based, computational model coded in Python to simulate the effects of deep-space radiation on individual yeast cells (Saccharomyces cerevisiae) to facilitate interpretation of biological radiation experiments. Version 1.0 of the code ran in a command line interface (CLI), limiting use to those familiar with modularization, object-oriented programming, and computational models. Here we present a graphical user interface (GUI) for AMMPER to increase its accessibility. GUI development included converting input points and UI files, designing an application and logo, and expanding program packages. Additionally, we added optical assistance that corresponded with simulation parameters, which included simulation type, cell type, ROS model, and radiation dosage, as well as customizable display and file exportation features. Following a pilot testing period, its structure was updated further to enhance abilities, adding increased runs, video visualization, data plotting, and an educational/tutorial component. Future work will include creating a bit installer and runtime environment for AMMPER. Ultimately, the creation of the GUI has two main goals: to facilitate the integration of computational models into the work of researchers in microbial radiobiology, and to act as an interactive and visual resource for space biology education.

yeast↗
NASA NTRSDate not supplied

Yeast strain development to test in-space bioproduction in the Lunar Explorer Instrument for space biology Applications (LEIA) mission

The Lunar Explorer Instrument for space biology Applications (LEIA) is investigating the effects of lunar radiation and gravity on yeast viability, growth, and metabolism. LEIA is part of the CP-22 Commercial Lunar Payload Services (CLPS) surface mission to the south polar region of the Moon. The biological payload will test genetic factors that are likely to influence the tolerance of yeast for deep space and lunar surface radiation. LEIA is also investigating in-space production of β-carotene in bioengineered yeast. This carotenoid is both an antioxidant and pro-vitamin A- an essential human micronutrient. We report progress on engineering carotenoid-expressing strains to test the impacts of strain background and specific genetic variants on growth and production of β-carotene. To test for enhanced sensitivity to the LEIA mission environment, we generated gene knockouts for the RAD51 DNA damage repair locus as well as the SOD1, SOD2, and TSA1 reactive oxygen species (ROS) defense enzymes. We are also generating strains expected to increase tolerance to abiotic stressors and ROS. To be included in the biology payload, each strain needs to satisfy a series of requirements to be compatible with the mission concept of operations. The LEIA mission will conduct experiments using an autonomous light emitting diode optical detection system and microfluidics incubator to quantify growth, metabolism, and carotenoid production. Strains must produce sufficient carotenoids for bioproduction to be detectable with this optical system. Cells will be loaded into microfluidics cards, desiccated, and stored for 8-12 months prior to the initiation of lunar surface operations. The CLPS lander will operate for one lunar day, and strains will need to grow to stationary phase within 96 hours to ensure that telemetry of LEIA data to Earth can be completed. Genetic variants also need to display expected phenotypes within these optical detection, storage, and growth cycle constraints.

Yeast Engineering↗
NASA NTRS1979

Rapid, single-step most-probable-number method for enumerating fecal coliforms in effluents from sewage treatment plants

A single-step most-probable-number method for determining the number of fecal coliform bacteria present in sewage treatment plant effluents is discussed. A single growth medium based on that of Reasoner et al. (1976) and consisting of 5.0 gr. proteose peptone, 3.0 gr. yeast extract, 10.0 gr. lactose, 7.5 gr. NaCl, 0.2 gr. sodium lauryl sulfate, and 0.1 gr. sodium desoxycholate per liter is used. The pH is adjusted to 6.5, and samples are incubated at 44.5 deg C. Bacterial growth is detected either by measuring the increase with time in the electrical impedance ratio between the innoculated sample vial and an uninnoculated reference vial or by visual examination for turbidity. Results obtained by the single-step method for chlorinated and unchlorinated effluent samples are in excellent agreement with those obtained by the standard method. It is suggested that in automated treatment plants impedance ratio data could be automatically matched by computer programs with the appropriate dilution factors and most probable number tables already in the computer memory, with the corresponding result displayed as fecal coliforms per 100 ml of effluent.

Munoz, E. F.↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

imaging↗