Microbial production of megadalton titin yields fibers with advantageous mechanical properties
Explore the source record for details and available documents.
Christopher H. Bowen↗
Engineering topics
Jonathan M. Galazka
Publications and source records attributed to Jonathan M. Galazka.
RNAseq Analysis of Rodent Spaceflight Experiments Is Confounded by Sample Collection Techniques
Explore the source record for details and available documents.
San-Huei Lai Polo↗
Novel insights enabled by combining mouse muscle datasets from the Rodent Research-1 mission
Biological space experiments are often expensive and difficult to conduct. As such, it is critical to maximize the value of the data that is collected during these experiments. One way to do this is to combine multiple–previously separate–datasets. This can increase the number of replicates for the conditions of interest (and hence statistical power), allow new multi-factor questions to be asked, and potentially highlight new patterns that otherwise would not have been identified from single-dataset studies. However, the process of combining datasets introduces noise due to inherent technical variations between experiments. To better understand the insights that can be gained from multi-dataset analyses and the problems that may arise from joining multiple datasets, several mouse muscle RNA-Seq datasets from the Rodent Research-1 mission were first selected. Then, using the R package DESeq2, principal component analysis (PCA) plots and differentially expressed gene (DEG) lists between ground and flight muscle samples were generated for individual datasets and for different pairwise combinations of datasets. Several new DEGs were identified in the combined datasets, and patterns in the PCA plots were affected depending on which datasets were joined. Understanding the results of this work will be critical for future studies that seek to perform multi-dataset analyses.
spaceflight↗
A Pipeline for Assessing the Quality of Rna-Seq Datasets in GeneLab
Transcriptome profiling by RNA sequencing (RNA-seq) is a powerful approach to identify gene expression changes in organisms exposed to unique environments such as spaceflight. One of the challenges of evaluating RNA-seq data both within and across different space-relevant studies is the ability to control for technical differences, including the use of different library preparation kits, sequencing platforms, RNA yield, and person-to-person variation. To help address this issue, the National Institute of Standards and Technology (NIST, nist.gov) initiated a consortium, at the request of industry and academia, to develop a set of controls for gene expression measurements. The result was a set of 92 unlabeled, polyadenylated transcripts that range from 250 – 2,000 nucleotides in length to mimic natural eukaryotic mRNAs. These External RNA Controls Consortium (ERCC) genes can be used in any RNA-seq experiment, by adding known concentrations of the ERCC genes to samples after RNA extraction, to offer a standard measurement for data comparison. At NASA GeneLab, we employ these controls as part of our standard operating procedures for every in-house RNA-seq study to assess the limit of detection, dynamic range, and power of differential expression analysis both within and across experiments. Here we will discuss the use, benefits, and limitations of ERCC genes and other types of controls, such as universal RNA references, to generate quality control information for RNA-seq studies conducted at GeneLab.
GeneLab↗
Responses of Microbes to Modeled Space Radiation
The built environment of spaceships is host to a microbial community that affects crew and craft alike. While the static composition of this community has been characterized and its temporal dynamics examined, the mechanisms controlling its make-up and evolutionary trajectory are not understood. Systematic analyses of microbial diversity show consistent patterns in community composition and function. Understanding these patterns' ecological origins remains a significant challenge, as it requires connecting processes at varying temporal and spatial scales. However, it is clear that the state and trajectories of microbial communities are in-part determined by their physical environment. In this regard, the spaceflight environment includes numerous interacting factors that differentiate it from Earth environments, including an altered atmospheric composition, reduced gravity (and thus altered fluid dynamics), and increased ionizing radiation. These factors impart selective pressures on microbial communities that affect their evolutionary trajectories and thus the risks and benefits these communities represent to crew and craft. The radiation environment of space leads to chronic exposure to low doses and is difficult to mimic on Earth. Thus, little is known about how microbial communities in spacecraft will respond and evolve. Therefore, given the limitations of existing studies, we aim to empirically determine how exposure to low doses of ionizing radiation for thousands of cell divisions affects rates of mutation accumulation in bacteria and the trajectory of their evolution. In this way, we will provide a critical set of data for designing safe and robust space missions. Here we discuss our progress towards this aim, including the construction of exposure facilities, our culturing and analysis approach, and preliminary data.
radiation↗
Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets
RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.
Lauren M. Sanders↗