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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Differential Gene Expression in A Cross-Feeding Two-Species Model Microbial Community Under Simulated Microgravity and Deep-Space Radiation

A long-term goal of space biology is to understand interspecies microbial interactions in space. Presently, little is known about the combined effect of microgravity and ionizing radiation on bacterial community response when species are interdependent through exchange of metabolites in fluid medium (cross-feeding). Microgravity is expected to slow interspecies mass transfer and growth in cross-feeding communities in the low-shear, diffusion-limited environment, while ionizing radiation may influence stress response to direct (DNA damage) and indirect damage (ROS). Using a well-understood, two-species (Escherichia coli and Salmonella enterica) microbial community engineered to be a model for studying cross-feeding, we simulated galactic cosmic rays (GCRsim) and microgravity to test the hypothesis: exposure to ionizing radiation causes cell damage or stress, altering transcriptomic community responses in metabolically interdependent cells, which is exacerbated by microgravity. We expect to see differential gene expression between cross-feeding and non-cross-feeding communities. We measured GCRsim effects on growth and gene expression in well-mixed versus simulated-microgravity conditions and in cross-feeding and non-cross-feeding medium. Microbial cultures were inoculated into liquid medium in rotating wall vessels (RWV) with different rotation rates: 5 RPM (simulated microgravity) and 50 RPM (well-mixed). The E. coli-S. enterica consortium, under simulated microgravity, were exposed to 500 mGy of Simplified 5-ion Galactic Cosmic Ray Simulation for 2 hours at Brookhaven National Lab. We harvested samples 40 minutes after irradiation for extraction and sequencing (NASA GeneLab). Here we present the differential gene expression analysis results, which reveal altered transcriptomic community responses, even where growth rate differences are not observed. Gene expression of these actively metabolizing microbial communities in GCRsim may illuminate molecular mechanisms of microbial interactions in space. Understanding how microbial community gene expression, metabolism, and other cellular processes are influenced by spaceflight stressors can inform the use of microbes in human life support for low Earth orbit missions and beyond.

microgravity↗
NASA NTRS1991

Endolithic microbial model for Martian exobiology: The road to extinction

Martian exobiology is based on the assumption that on early Mars, liquid water was present and that conditions were suitable for the evolution of life. The cause for life to disappear from the surface and the recognizable fingerprints of past microbial activity preserved on Mars are addressed. The Antarctic cryptoendolithic microbial ecosystem as a model for extinction in the deteriorating Martian environment is discussed.

Oscampo-Friedmann, R.↗
NASA NTRS1966

Log-normal model for microbial survival in heat sterilization

An analytical model is developed for the survival times of organisms in heat sterilization in which the probability of inactivation as a function of exposure time is log-normally distributed. Experimental data is examined relative to this model and it is concluded that the model is valid except during the initial period of heating when an additional interaction between the organisms and their surrounding medium appears to be present. At long heating times, the log-normal model appears more accurate for extrapolating to low survival probabilities than the usual logarithmic survivor curves and is generally more conservative.

SURVIVAL↗
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

AMMPER: Agent-based Model for Microbial Populations Exposed to Radiation

Exposure of microbial populations to galactic cosmic radiation (GCR) in the deep-space radiation environment may lead to intracellular damage that compromises the ability of cells to repair and replicate. While simulation programs of deep-space radiation do exist, they focus primarily on single-cell damage, rather than population-wide effects. In this work, a new application and graphical user interface, Agent-Based Model for Microbial Populations Exposed to Radiation (AMMPER) is presented, which simulates the effects of proton-based GCR on Saccharomyces cerevisiae population growth. AMMPER consists of a 50x50x50 µm simulation space, analogous to an aqueous culture medium with non-limiting nutrient and pH buffering, in a microwell plate or microfluidic culture card. This model utilizes Relativistic Ion Tracks (RITRACKS) to create detailed track structures of the radiation traversals. AMMPER then calculates the radiation dose present at each cell, and subsequently determines the damage (chromosomal aberrations, oxidative stress, etc.) and resulting loss of cell viability from both primary and secondary radiative effects. Through implementing cell replication, repair, damage, and death, the effect of radiation exposure on the population growth can be determined. With AMMPER, long-duration effects of the deep space environment on entire populations can be determined and used to assess the feasibility of sustaining life in space.

Amrita Singh↗
NASA NTRS1999

Extremes of Survival Achieved by the Radiophile Deinococcus Radiodurans: A Model for Microbial Life on Mars

Deinococcus radiodurans is an extremophile in more than one defined way. First it is extreme in its resistance to freeze drying. Second it is probably uniquely extreme on Earth in its resistance to ionizing radiation. The polyextremophilic capacity of D. radiodurans will be considered. The selection pressures on Mars will then be considered in relation to D. radiodurans in order to support a hypothesis that if microbial life exists on Mars, then it likely includes polyextremophiles.

Daly, M.↗
NASA NTRSDate not supplied

The Pheno-Evo Model: Evolution of Microbial Phenotypic Diversity in 2D Space

Biologists appreciate microbes for their simplicity and predictability: we assume that a population of genetically identical cells in a uniform environment will all behave the same way. While this assumption is often useful, it is also often wrong. Not only might microbes in a clonal population act differently from one another, the differences may be categorical (growing v. non-growing; toxin-sensitive v. tolerant), and this diversity might be an evolved trait conferring increased fitness on the population. How does such phenotypic heterogeneity evolve? And how does a population find the optimal distribution of phenotypes for a given environment? Efforts at modeling microbial phenotypic heterogeneity often focus on populations with two discrete phenotypic types; phenotypes in continuous distributions remain poorly explored. To address this gap and to explore the role of spatial relationships, we use agent-based modeling to simulate a phenotypically diverse population of microbial cells evolving in the presence of periodic toxic stress. Cells on patches in a 2D grid may degrade toxin, suffer damage from toxin, switch phenotype, and reproduce. An individual's phenotype its toxin degradation rate-- for which there is a tradeoff with reproduction-- and the genotype encodes the distribution of phenotypic values in the population. The rate of toxin diffusion determines how individuals influence their neighbors' environments. We examine the effects of toxin concentration, diffusion rate, and environmental predictability on the survival success of populations with different phenotype distributions, and populations' evolutionary trajectories when phenotype distribution is allowed to evolve. We conduct all simulations on the platform NetLogo, which provides a friendly interface allowing users of any experience level to tweak parameters and run their own simulations. We have also created custom tools in R for analyzing and visualizing the results of multiple runs.

microbial evolution↗
NASA NTRS2002

Rejuvenation of Spent Media via Supported Emulsion Liquid Membranes

The overall goal of this project is to maximize the reuseability of spent fermentation media. Supported emulsion liquid membrane separation, a highly efficient extraction technique, is used to remove inhibitory byproducts during fermentation; thus, improving the yield while reducing the need for fresh water. The key objectives of this study are: Develop an emulsion liquid membrane system targeting low molecular weight organic acids which has minimal toxicity on a variety of microbial systems; Conduct mass transfer studies to allow proper modeling and design of a supported emulsion liquid membrane system; Investigate the effect of gravity on emulsion coalescence within the membrane unit; Access the effect of water re-use on fermentation yields in a model microbial system; Develop a perfusion-type fermentor utilizing a supported emulsion liquid membrane system to control inhibitory fermentation byproducts; Work for the coming year will focus on the determination of toxicity of various solvents, selection of the emulsifying agents, as well as characterizing the mass transfer of hollow-fiber contactors.

Wiencek, John M.↗
NASA NTRS2002

Rejuvenation of Spent Media via Supported Emulsion Liquid Membranes

The overall goal of this project was to maximize the reuseability of spent fermentation media. Supported emulsion liquid membrane separation, a highly efficient extraction technique, was used to remove inhibitory byproducts during fermentation; thus, improve the yield while reducing the need for fresh water. The key objectives of this study were: (1) Develop an emulsion liquid membrane system targeting low molecular weight organic acids which has minimal toxicity on a variety of microbial systems. (2) Conduct mass transfer studies to allow proper modeling and design of a supported emulsion liquid membrane system. (3) Investigate the effect of gravity on emulsion coalescence within the membrane unit. (4) Access the effect of water re-use on fermentation yields in a model microbial system. and (5) Develop a perfusion-type fermentor utilizing a supported emulsion liquid membrane system to control inhibitory fermentation byproducts (not completed due to lack of funds)

Wiencek, John M.↗
NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗
NASA NTRS1971

Microbial burden prediction model program

Model supplements biological surveys of spacecraft by simulating microbial burden accumulation process during periods when surveys are not taken. Important application of model is to predict microbial loading on spacecraft landing capsule immediately prior to terminal heat sterilization.

Meuser, R. F.↗
NASA NTRS1991

Microbiology on Space Station Freedom

This panel discussion convened in Houston, Texas, at the Lunar and Planetary Institute, on November 6 to 8, 1989, to review NASA's plans for microbiology on Space Station Freedom. A panel of distinguished scientists reviewed, validated, and recommended revisions to NASA's proposed acceptability standards for air, water, and internal surfaces on board Freedom. Also reviewed were the proposed microbiology capabilities and monitoring plan, disinfection procedures, waste management, and clinical issues. In the opinion of this advisory panel, ensuring the health of the Freedom's crews requires a strong goal-oriented research effort to determine the potential effects of microorganisms on the crewmembers and on the physical environment of the station. Because there are very few data addressing the fundamental question of how microgravity influences microbial function, the panel recommended establishing a ground-based microbial model of Freedom, with subsequent evaluation using in-flight shuttle data. Sampling techniques and standards will be affected by both technological advances in microgravity-compatible instrumentation, and by changes in the microbial population over the life of the station.

Pierson, Duane L.↗
NASA NTRS2007

Effect Of Spaceflight On Microbial Gene Expression And Virulence: Preliminary Results From Microbe Payload Flown On-Board STS-115

Human presence in space, whether permanent or temporary, is accompanied by the presence of microbes. However, the extent of microbial changes in response to spaceflight conditions and the corresponding changes to infectious disease risk is unclear. Previous studies have indicated that spaceflight weakens the immune system in humans and animals. In addition, preflight and in-flight monitoring of the International Space Station (ISS) and other spacecraft indicates the presence of opportunistic pathogens and the potential of obligate pathogens. Altered antibiotic resistance of microbes in flight has also been shown. As astronauts and cosmonauts live for longer periods in a closed environment, especially one using recycled water and air, there is an increased risk to crewmembers of infectious disease events occurring in-flight. Therefore, understanding how the space environment affects microorganisms and their disease potential is critically important for spaceflight missions and requires further study. The goal of this flight experiment, operationally called MICROBE, is to utilize three model microbial pathogens, Salmonella typhimurium, Pseudomonas aeruginosa, and Candida albicans to examine the global effects of spaceflight on microbial gene expression and virulence attributes. Specifically, the aims are (1) to perform microarray-mediated gene expression profiling of S. typhimurium, P. aeruginosa, and C. albicans, in response to spaceflight in comparison to ground controls and (2) to determine the effect of spaceflight on the virulence potential of these microorganisms immediately following their return from spaceflight using murine models. The model microorganisms were selected as they have been isolated from preflight or in-flight monitoring, represent different degrees of pathogenic behavior, are well characterized, and have sequenced genomes with available microarrays. In particular, extensive studies of S. typhimurium by the Principal Investigator, Dr. Nickerson, using ground-based analog systems demonstrate important changes in the genotypic, phenotypic, and virulence characteristics of this pathogen resulting from exposure to a flight-like environment (i.e. modeled microgravity).

Wilson, J. W.↗
NASA NTRS1990

The ECLSS Advanced Automation Project Evolution and Technology Assessment

Viewgraphs on Environmental Control and Life Support System (ECLSS) advanced automation project evolution and technology assessment are presented. Topics covered include: the ECLSS advanced automation project; automatic fault diagnosis of ECLSS subsystems descriptions; in-line, real-time chemical and microbial fluid analysis; and object-oriented, distributed chemical and microbial modeling of regenerative environmental control systems description.

Dewberry, Brandon S.↗
NASA NTRS2017

The Transcriptional Response of Diverse Saccharomyces Cerevisiae Strains to Simulated Microgravity

Spaceflight imposes multiple stresses on biological systems resulting in genome-scale adaptations. Understanding these adaptations and their underlying molecular mechanisms is important to clarifying and reducing the risks associated with spaceflight. One such risk is infection by microbes present in spacecraft and their associated systems and inhabitants. This risk is compounded by results suggesting that some microbes may exhibit increased virulence after exposure to spaceflight conditions. The yeast, S. cerevisiae, is a powerful microbial model system, and its response to spaceflight has been studied for decades. However, to date, these studies have utilized common lab strains. Yet studies on trait variation in S. cerevisiae demonstrate that these lab strains are not representative of wild yeast and instead respond to environmental stimuli in an atypical manner. Thus, it is not clear how transferable these results are to the wild S. cerevisiae strains likely to be encountered during spaceflight. To determine if diverse S. cerevisiae strains exhibit a conserved response to simulated microgravity, we will utilize a collection of 100 S. cerevisiae strains isolated from clinical, environmental and industrial settings. We will place selected S. cerevisiae strains in simulated microgravity using a high-aspect rotating vessel (HARV) and document their transcriptional response by RNA-sequencing and quantify similarities and differences between strains. Our research will have a strong impact on the understanding of how genetic diversity of microorganisms effects their response to spaceflight, and will serve as a platform for further studies.

yeast↗
NASA NTRS2017

The Transcriptional Response of Diverse Saccharomyces Cerevisiae Strains to Simulated Microgravity

Spaceflight imposes multiple stresses on biological systems resulting in genome-scale adaptations. Understanding these adaptations and their underlying molecular mechanisms is important to clarifying and reducing the risks associated with spaceflight. One such risk is infection by microbes present in spacecraft and their associated systems and inhabitants. This risk is compounded by results suggesting that some microbes may exhibit increased virulence after exposure to spaceflight conditions. The yeast, S. cerevisiae, is a powerful microbial model system, and it's response to spaceflight has been studied for decades. However, to date, these studies have utilized common lab strains. Yet studies on trait variation in S. cerevisiae demonstrate that these lab strains are not representative of wild yeast and instead respond to environmental stimuli in an atypical manner. Thus, it is not clear how transferable these results are to the wild S. cerevisiae strains likely to be encountered during spaceflight. To determine if diverse S. cerevisiae strains exhibit a conserved response to simulated microgravity, we will utilize a collection of 100 S. cerevisiae strains isolated from clinical, environmental and industrial settings. We will place selected S. cerevisiae strains in simulated microgravity using a high-aspect rotating vessel (HARV) and document their transcriptional response by RNA-sequencing and quantify similarities and differences between strains. Our research will have a strong impact on the understanding of how genetic diversity of microorganisms effects their response to spaceflight, and will serve as a platform for further studies.

genetic diversity↗
NASA NTRS2018

The Transcriptional Response of Diverse Saccharomyces cerevisiae Strains to Simulated Microgravity

Spaceflight imposes multiple stresses on biological systems resulting in genome-scale adaptations. Understanding these adaptations and their underlying molecular mechanisms is important to clarifying and reducing the risks associated with spaceflight. One such risk is infection by microbes present in spacecraft and their associated systems and inhabitants. This risk is compounded by results suggesting that some microbes may exhibit increased virulence after exposure to spaceflight conditions. The yeast, S. cerevisiae, is a powerful microbial model system, and it's response to spaceflight has been studied for decades. However, to date, these studies have utilized common lab strains. Yet studies on trait variation in S. cerevisiae demonstrate that these lab strains are not representative of wild yeast and instead respond to environmental stimuli in an a typical manner. Thus, it is not clear how transferable these results are to the wild S. cerevisiae strains likely to be encountered during spaceflight. To determine if diverse S. cerevisiae strains exhibit a conserved response to simulated microgravity, we will utilize a collection of 100 S. cerevisiae strains isolated from clinical, environmental and industrial settings. We will place selected S. cerevisiae strains in simulated microgravity using a high-aspect rotating vessel (HARV) and document their transcriptional response by RNA-sequencing and quantify similarities and differences between strains. Our research will have a strong impact on the understanding of how genetic diversity of microorganisms effects their response to spaceflight, and will serve as a platform for further studies.

spaceflight↗