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Michael Padgen

Publications and source records attributed to Michael Padgen.

Automated Fluidics Device for Extraction and Quantification of miRNA Biomarkers From Blood

Radiation Assessment DuRing Exposure And long-Duration Spaceflight (RADREADS) demonstrates space-compatible point-of-care technology for quantitative biological monitoring of blood miRNA biomarkers in response to long-term low dose radiation exposure. This individualized monitoring approach will inform targeted treatment strategies to maximize medical resource utilization by accounting for individual susceptibility to radiation-related illnesses. As human spaceflight progresses beyond Earth’s magnetic shielding, radiation exposure poses a significant risk to astronaut health and safety. Extended operation in this environment comes with an increased risk of radiation exposure, leading to higher risks of radiation sickness, cancer, central nervous system effects, and degenerative diseases. While conventional physical dosimetry techniques capture radiation dose, individualistic susceptibility to radiation damage is varied. Multiple characteristics, including age, body weight, sex, genetics, and immune status, have been found to influence radiosensitivity (Liu et al. 2011, and Bouffler 2016). This differential response necessitates individualized monitoring and targeted treatment strategies to maximize medical resource utilization; however, a practical diagnostic platform for quantifying long-term, low dose radiation-induced tissue damage does not currently exist. MicroRNAs (miRNAs) are a class of small, non-coding RNAs that regulate gene expression by mediating the degradation of messenger RNA. The levels of particular miRNAs are influenced by biological processes such as inflammation and serve as biomarkers for a variety of conditions including cancer (Singh et al. 2017). MicroRNAs are found in various bodily fluids and are amenable to collection via liquid biopsies, providing a minimally invasive and easily quantifiable readout for a variety of radiosensitive reporters. A preliminary signature of 15 spaceflight sensitive miRNA has been identified in rodent and human studies, including miR-21-5p, miR-24-3p, miR-92a-3p, miR-17-5p, miR-16a-3p, miR-34a-3p, and miR-223-3p. These targets generally increased expression with radiation dose and linear energy transfer, though variation between individuals is not yet described. Current gaps in the field include a lack of understanding of longitudinal biological responses to long-term, low dose radiation exposure and the absence of space-compatible point-of-care technology for quantitative biological monitoring. In this body of work, we aim to develop an automated bleed-to-read system to process whole blood for the detection of miRNA biomarkers in order to monitor individualistic responses to radiation exposure. This will be achieved via separating serum (or plasma) from whole blood, followed by extraction, amplification, and quantification of the miRNA using a RT-qPCR reaction. Previously, the WetLab-2 hardware enabled execution of a RT-qPCR reaction aboard ISS; however, it is a manual system that requires crew manipulation and bulky components (Parra et al. 2017). To address these issues, automated fluid handling hardware was developed for each stage of sample preparation. Extraction of total RNA is achieved by sequentially pumping reagents through an off-the-shelf nucleic acid binding column (miRNeasy Serum/Plasma Advanced Kit, Qiagen). This approach eliminates several manual pipetting and centrifuging steps and limits the use of toxic chemicals commonly found in other sample processing techniques. The resulting elution will then be automatically dispensed for RT-qPCR analysis using a compact rotary qPCR (Mic qPCR Cycler, Bio Molecular Systems) that will improve spaceflight compatibility by removing bubbles from the detection region, another challenge highlighted by WetLab-2 (Parra et al. 2017). Efforts are also being made to simplify the RT-qPCR reaction to a 1-step air-dryable mix to improve long-term reagent stability at room temperature and reduce system complexity. By automating the RT-qPCR processes via microfluidic manipulation, RADREADS will reduce crewmember hands-on time and enable the personalized detection of radiation-induced tissue damage during long duration missions. Minimally invasive, longitudinal monitoring of individual’s response to radiation exposure will inform how the physiological system responds to long-term low dose space radiation and enables development of targeted countermeasures by the medical team. Ultimately, this portable technology will require minimal technical expertise and can also be used to monitor miRNA biomarkers associated with other diseases.

Tristen Head

BioSensor Users' Guide

The BioSensor is a fully autonomous 3-color LED-based spectrophotometer paired with a fluidics system that supports microbes in liquid culture. Originally developed for the BioSentinel CubeSat mission to study the response of a wild type and mutant strain of yeast to the deep space environment, the BioSensor consists of a series of 16-well, independently plumbed fluidics cards. The cards utilize filters to allow the fluidic system to administer reagents while constraining the microbes in their wells. Heaters on each card incubate to the appropriate growth temperature once the experiment begins. During the active experiment, the LED/detector system measures the transmission of light through each well at three specific wavelengths, similar to a standard benchtop spectrophotometer. The transmission/ absorbance kinetics curves for each well are telemetered back to Earth, along with temperature data, for analysis on the ground. The BioSensor is being upgraded from its original CubeSat free-flyer interface to be a secondary payload on lunar landers, Gateway, and other BLEO applications, while maintaining the same functionality and science utility for future experiments.

Matthew Lera

BioSensor Users' Guide

The BioSensor is a fully autonomous 3-color LED-based spectrophotometer paired with a fluidics system that supports microbes in liquid culture. Originally developed for the BioSentinel CubeSat mission to study the response of a wild type and mutant strain of yeast to the deep space environment, the BioSensor consists of a series of 16-well, independently plumbed fluidics cards. The cards utilize filters to allow the fluidic system to administer reagents while constraining the microbes in their wells. Heaters on each card incubate to the appropriate growth temperature once the experiment begins. During the active experiment, the LED/detector system measures the transmission of light through each well at three specific wavelengths, similar to a standard benchtop spectrophotometer. The transmission/absorbance kinetics curves for each well are telemetered back to Earth, along with temperature data, for analysis on the ground. The BioSensor is being upgraded from its original CubeSat free-flyer interface to be a secondary payload on lunar landers, Gateway, and other BLEO applications, while maintaining the same functionality and science utility for future experiments.

Matthew Lera

BioLEAD: BioLogical Exploration via Autonomous Detection – Using Dielectric Spectroscopy to Monitor Biological Changes in Response to Deep Space Radiation

Leveraging the bio-fluidic hardware developed for BioSentinel, we propose a new payload for autonomous missions: BioLEAD – BioLogical Exploration via Autonomous Detection. With NASA's renewed focus to continue human exploration of the Moon (Artemis Program), the BioLEAD payload aims to investigate the effects of the lunar environment on biology, whether onboard a lunar lander, the Lunar Gateway, or as a free-flying CubeSat orbiting the Moon. In place of an optical detection system, BioLEAD will employ a miniaturized, non-invasive dielectric spectroscopy sensor to enable real-time monitoring of biological activity. The sensor operates by relating capacitance measurements to the dielectric properties of the cell, such as cell morphology, doubling time, and cell cycle stage. The implementation of this new sensor technology will address limitations of the optical measurement system used on BioSentinel. It will also advance the use of autonomous bioanalytical microsystems and reveal new information regarding biological responses to the Moon’s radiation environment. Most importantly, BioLEAD’s technology will be adaptable for a wide array of future missions.

Biosensor

Improving Data Analyses for a Biological Mission to Deep Interplanetary Space

Onboard the Artemis 1 rocket, NASA plans to launch the first deep space bioscience mission past low Earth orbit (LEO) since 1972, BioSentinel, a 6-Unit (6U) biological CubeSat. BioSentinel’s goals are to assess the effect of deep space ionizing radiation (IR) on DNA and cell damage response, using Saccharomyces cerevisiae, or budding yeast, as a model organism. BioSentinel accomplishes this by measuring Optical Density (OD) in addition to metabolic activity using the redox dye alamarBlue, both of which will be read through light emitting diode (LED) lights of differing wavelengths. With the largest and most sophisticated energy-providing solar panels utilized on a biological CubeSat to date, BioSentinel will be equipped with an IR dosimeter to identify what doses of radiation the yeast is exposed to at any point in time, in addition to a transponder to send such data back to NASA Ames’ Multi Mission Operations Center (MMOC). Biology computational programs and data processing scripts are necessary to analyze this complex data once it is received, including automated subroutines to analyze duplication rate, alamarBlue reduction, and time periods when paired against numerous other variables. Overall, we have implemented several VisualBasic biocomputational programs to analyze such data in an organized and efficient manner so that conclusions can be made rapidly. Furthermore, we showcase why efficient analysis of particular parameters is important to better understand the risks that deep space IR poses to astronauts, especially when considering the upcoming Artemis missions.

Bijan Harandi

Growth Curve Parameterization of Metabolic Activity of Yeast Cells for BioSentinel

The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.

Growth Curve

Life Finds A Way, the Dynamics of E. Coli Evolution in Microgravity

Investigating the evolutionary dynamics of Escherichia coli in microgravity offers a unique opportunity to understand microbial adaptation to extreme environments. Here, we explored the effects of simulated microgravity (SµG) on gene expression and genome evolution of Escherichia coli REL606, a strain continuously evolved and documented terrestrially for 35 years. We used transcriptomic profiling over a 24-hour growth cycle to examine how short-term exposure to SµG under glucose-limiting and glucose-replete conditions may influence the genetic adaptations in microbial populations. Pathway analyses of differentially expressed genes suggest that SµG may alter cell membrane structure and function across all conditions, while changes to protein synthesis machinery were uniquely observed in glucose-replete samples. Furthermore, altered expression of several prophage genes across conditions in SµG samples and upregulation of general stress response factors hints at the potential for stress-induced mutagenesis in response to microgravity. We further investigated the impact of long-term exposure to SµG on genome evolution and observed a more rapid accumulation of base substitutions and deletions in SµG sample genomes across time. Specifically, mutations in the mraZ and elyC genes suggest a mechanism for increased production of peptidoglycan in the cell membrane. These findings offer insights into bacterial adaptations in long-term microgravity environments and pave the way for further detailed investigations.

Brittney Lozzi