A microRNA signature and TGF-β1 response were identified as the key master regulators for spaceflight response
A microRNA signature and TGF-?1 response were identified as the key master regulators for spaceflight response
Afshin Beheshti 0 1
Shayoni Ray 1
Homer Fogle 0 1
Daniel Berrios 1
Sylvain V. Costes 1
? These authors contributed equally to this work. 1
0 WYLE, NASA Ames Research Center, Moffett Field, California, United States of America , 2 USRA , NASA Ames Research Center, Moffett Field, California, United States of America, 3 NASA Ames Research Center, Space Biosciences Division , Moffett Field, California , United States of America
1 Editor: Andre van Wijnen, University of Massachusetts Medical School , UNITED STATES
Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential ?master regulators? coordinating a systemic response to microgravity and/or space radiation with TGF-?1 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-?1 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis?based research.
Funding: Research funding was provided by the
GeneLab Project at NASA Ames Research Center,
through NASA's Space Biology Program in the
Division of Space Life and Physical Sciences
Research and Applications (SLPSRA). Any use of
trade names is for descriptive purposes only and
does not imply endorsement by the US
The spaceflight environment has been evaluated for human health risks due to hazardous
factors such as ionizing radiation, microgravity, hypoxia, hypothermia as well as other associated
physiological/psychological stressors. Space radiation and microgravity have been considered
as the primary potential show-stoppers for long duration space exploration missions beyond
LEO (Low Earth Orbit) and are thought to be driving many of the responses observed in flown
organisms. Due to the high cost and limited availability of larger cohort for spaceflight
samples, rodents have emerged as the primary choice among the model organisms for flight
experiments. This is due to its similarity to the human genome [
] and their relative small size
compared to other rodents. Omics data related to these studies have been made available to
the public through NASA's GeneLab platform (genelab.nasa.gov). GeneLab is an open
repository that houses fully coordinated and curated experimental results, raw data and
metadata pertaining to model organisms onboard International Space Station (ISS), Space
Transportation Systems (STS), or the russian animal carry capsule called Bion-M1 [
GeneLab also houses ground NASA studies which model spaceflight environment. The vast
amounts of genomic, proteomic, transcriptomic and metabolomic data from several
organisms and cell culture experiments are available to researchers across the globe for in silico
analysis and generating hypothesis-driven future research.
Several mice studies have been conducted in the past to assess the organ-specific
physiological responses to microgravity in short-term and long-term space missions. X.W. Mao et.al.
investigated the effect of a 13-day mission on the cutaneous tissue and found numerous genes
encoding anti-oxidants and extra-cellular matrix (ECM) proteases along with genes
responsible for reactive oxygen species (ROS) generation and gluconeogenesis to be upregulated in the
space flown mice [
]. Spaceflight conditions have also been shown to exert deleterious effects
on the musculoskeletal system. The short-term spaceflight response in the gastrocnemius
muscle included decreased PI3-kinase/Akt/mTOR signaling, myogenic cell proliferation, and
]. The long-term responses in the calf soleus identified changes in expression of
genes involved in maintaining calcium homeostasis, supporting contractile machinery, muscle
development, cell metabolism, and decreasing inflammatory and oxidative stress response [
Hepatic lipid metabolism was also evaluated in a 13.5-day mission and early signs of liver
injury was detected in mice [
]. Although these previous studies are starting to reveal the
biological impact of microgravity and/or space radiation on single components of the host, the
overall global response on the host has not yet been determined.
Analysis of complex systems have indicated the difficulty in interpreting collective
emergent behaviors occurring as a result of interaction between various components through
separate analyses of those components [
]. Hence in this study, we provide a systems-level analyses
to assess effects of spaceflight in rodents flown in different habitats for varying time-points, by
evaluating the transcriptional changes in several tissues harvested either post-flight or
onorbit. Through an established systems biology approach [7?9] we identified a master regulator,
TGF-?1, coordinating systemic responses to microgravity, at multiple biological scales. We
were further able to predict that the global host response between the multiple linked tissues
based on TGF-?1 was driven by circulating microRNAs (miRNAs). MiRNAs are small
noncoding RNA, which can impact a large number of genes, proteins and DNA [
]. Due to the
small size of miRNAs it has been found to be stably circulating throughout the blood, both free
floating and encapsulated in exosomes [
]. We were able to predict that a spaceflight specific
circulating miRNA signature has the potential to drive systemic TGF-?1 response in the host
and display subsequent impact on health, calculated from a biological ?health risk score?. The
genes and miRNAs identified from our analyses can be targeted for future research involving
efficient countermeasure design. Our study epitomizes the value of GeneLab data repository,
which can be used not only to retrieve spaceflight data to assess organ/tissue-specific
perturbations in molecular signaling networks, but also to establish the foundation of novel
hypothesis?based spaceflight research aimed at characterizing the global impact of environmental
stressors at multiple biological scales.
Materials and methods
This study was conducted in accordance with all ethical standards.
Data from GeneLab platform
All data used for this manuscript were obtained from GeneLab (genelab.nasa.gov). The
following datasets were used: GLDS-25, -21, -63, -111, -4, -61 and 48. Spaceflight mission and
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Fig 1. Illustration of methodology and tissues used for analysis. A schematic representation of the process flow in obtaining the omics dataset from GeneLab
database from the tissues used for analyses in this manuscript is shown on the top left. The different tissues have been plotted along an x-axis for the amount of the
time the rodents were in space before sacrificing. The upper panel (blue) represents tissues from rodents on the international space station (ISS) and the lower
panel (yellow) represents the tissues from rodents on space shuttle missions (STS).
experimental details for each dataset such as, the handling of the rodents, tissue processing,
RNA extraction and raw data pertaining to either microarray or RNA-sequencing, can be
found in the GeneLab database. Briefly, we used 7 different murine and rat datasets for our
analysis and examined the following tissues: liver, kidney, adrenal gland, thymus, mammary
gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior,
quadriceps, and gastrocnemius) (Fig 1). Specific Details regarding these rodents during spaceflight
are available in the supplemental materials and methods section.
The available transcriptomic data for tissues from GLDS-25, -21, -63, -111, -4, -61 datasets
were previously performed on different versions of Affymetrix platforms. The transcriptomic
data for the tissues from the GLDS-48, -98, -101, -102, -103, -104, -105 datasets was processed
using RNA-sequencing. Due to the incompatibility of processed data from these different
platforms, all datasets were analyzed independently and the processed data was compared across
all tissues. For the GLDS-25 and -4 datasets raw data was background adjusted and quantile
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normalized done using GenePattern [
]. For GLDS-21, -63, -111, and -61 datasets were
background adjusted and quantile normalized using RMAExpress [
]. The probes from the
preprocessed data were median collapsed using GenePattern. All tissue for the GLDS-48, -98,
-101, -102, -103, -104, -105 data was pre-processed by having Illumina reads trimmed for
sequencing adapters and phred quality score of 20 with Trim-Galore. Alignment to Gencode
Release M15 (GRCm38.p5) transcript sequences and quantification of Gencode
comprehensive gene annotations was performed with Kallisto (Kmer length = 31, bootstrap sequences =
100, paired-end, strand specific first read forward) [
]. Read and mapping quality was
evaluated with RseQC [
]. Transcript and gene estimated counts and transcripts per million
normalizations were performed with Sleuth [
]. All the data for each tissue/dataset was imported
into MultiExperiment Viewer [
] and statistically significant genes was determined either by
t-test with a p-value 0.05 or False Discovery Rate (FDR) 0.05 depending on statistical
stringency needed to produce a reasonable number of genes to take forward for the rest of the
Pathway analysis and subsequent predictions in each tissue were done using the statistically
significant genes with a fold-change 1.2 (or -1.2) comparing Flight Conditions versus
AEM ground controls. Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems) was
used to predict statistically significant activation or inhibition of upstream regulators,
canonical pathways, biofunctions, and toxic functions using activation Z-score statistics ( 2,
activated or ?2, inhibited) [
]. Gene set enrichment analysis (GSEA) was done using the C2,
C5, and Hallmarks gene sets with a FDR 0.05 from the entire list of genes and additional
leading edge analysis was performed as described by Subramanian et. al [
]. All heat maps
and principle component analysis (PCA) plots were generated using packages available
through R (pheatmap for heat maps and scatterplot3d for PCA plots).
Determination of key genes/drivers
We used a previously established unbiased systems biology method to determine key genes/
7, 9, 20, 21
] for each tissue. Briefly, this was done by determining the overlapping
genes involved in the predictions made through IPA's upstream regulators, canonical
pathways, biofunctions and GSEA gene sets (C2, C5, and Hallmarks gene sets). The common genes
identified through these statistically significant predictions can be thought of as the central
drivers for the experiment being studied, since the absence of the genes will make these
predictions null. To determine the key gene, which has the highest number of connections and can
be thought of as the central hub for the set of key genes, we utilized IPA to define the total
number of connections between all the key genes. Next, we plotted the genes using the radial
plot tool which places the most connected gene in the center of the plot. Previous work
involving similar statistically identified key genes provided experimental validation for this
methodology using Western blots, qPCR, and other functional assays [
MicroRNA (miRNA) predictions and health risk score calculation
Through the use of p-value and activation Z-score statistics in IPA, the top 13 miRNAs
impacting the key genes were determined. The upstream regulator tool in IPA was used to determine
the top 10 statistically relevant miRNAs (p-values 1.17 ? 10?8) and the prediction of
activation/inhibition of all miRNAs was performed with activation Z-score 2 or ?2 (with
corresponding p-values 1.44 ? 10?5). Activation Z-score statistics provides the actual functional
activity for the miRNAs, which provides a more meaningful biological representation of the
impact of the miRNAs than p-value alone. The activation Z-score analysis resulted in 4
miRNAs, with one miRNA overlapping with the top 10 p-value determined miRNAs. The activity
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of the remaining 9 miRNAs was determined through activation Z-score statistics in IPA. A full
list of all predicted miRNAs can be found in S1 Table.
The ?health risk score? (HRS) was determined by first associating the overall general impact
on health for each miRNA from the literature. Then the HRS was calculated by subtracting the
activation Z-score values (used to determine amount of activation or inhibition) of the
miRNAs that provided a negative health impact from the miRNAs that provided a positive health
impact. If the overall HRS is positive then it will indicate a beneficial impact on health, while a
negative value will indicate a negative impact on health.
Results and discussion
Description of datasets utilized from GeneLab
GeneLab database was used to obtain microarray and RNA sequencing data pertaining to 12
different mouse datasets (GLDS-25, -21, -111, -4, -61, -48, -98, -101, -102, -103, -104, and
-105) and one rat dataset (GLDS-63 ?mammary gland). While microarray expression data was
used for GLDS-25, -21, -63, -111, -4, -61 data sets, NGS (RNA-seq) data was utilized for all
tissues related to GLDS-48, -98, -101, -102, -103, -104, -105 data sets. Several tissues were
characterized in these datasets: liver, kidney, adrenal gland, thymus, mammary gland, skin, and
skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and
gastrocnemius). Fig 1 outlines the various tissues as well as the three different flight systems?five STS,
two ISS and one BION-M1 biosatellite (BF) flights with their respective flight durations. The
detailed description of the age, sex and strain of the animals and type of tissue harvested for
each experiment can be found in the Methods section.
In order to visualize similarity between various animal samples, Principle Component
Analysis (PCA) technique was used for each dataset (S1 Fig). PCA results show that skin
samples from ISS, the majority of the muscle tissue, and mammary glands from STS had clear
separation between the flight and ground Animal Enclosure Module (AEM) controls [
focusing on the largest experimental dataset and the only RNA-seq dataset (i.e. GLDS-48),
PCA graph shows strong clustering by tissue type, suggesting tissue type is the driving factor
for changes in gene expression (all muscle tissues cluster together?lower left panel, S1 Fig).
Even though, separation between flight and ground AEM is not a predominant feature, we still
ran statistical test to identify genes that were differentially expressed between both
experimental condition for each given tissue and flight condition (see Material and method). We either
used a p-value (p<0.05) cutoff for ISS-flown rodents or a FDR adjusted p values (FDR<0.05)
for STS and Bion-M1- flown samples to reduce the probability of false positive genes (Fig 2).
For each dataset due to the variability in noise and in the number of biological replicates, we
used the lowest stringency for the statistics to produce a reasonable number of significant
genes and the best overlap between each dataset. For example, in the STS-135 liver samples,
STS-70 mammary gland samples, BF SLS and EDL samples, and STS-108 skeletal muscle
samples, a first pass at the data using p-value < 0.05 as a cutoff for significant genes yielded a large
amount of genes: i.e. 17,168 genes, 10,729 genes, 6,801 genes and 9,087 genes respectively. In
contrast, dataset using FDR statistical significant cutoff < 0.05 led to a lower number of genes
suggesting this approach was more stringent statistically and reduced chances of getting false
positives. With this approach, we showed that, overall a higher number of genes were
significantly upregulated in livers of ISS flown mice compared to the STS flown mice. While in the
ISS- flown mice, the muscles?Soleus, extensor digitorum longus and tibialis anterior,
displayed the highest number of significant genes, in the STS dataset, the highest number of
upregulated genes was found in mammary glands (Fig 2). Interestingly the mammary gland
tissue was the only tissue from STS flown rats, while all other tissue was from mice. Muscle
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Fig 2. The number of statistically significant genes determined for each tissue and dataset. A bar plot representing the number of significant genes for each
tissue from all datasets either determined by t-test with p-value 0.05 or with FDR 0.05. The tissues are separated by the flight conditions for the rodents with
ISS = International Space Station, STS = Space Shuttle Mission, and BF = Bion. The color-coded bar on the bottom of the plot represents datasets associated with
Flight Duration and Rodent Species.
tissues from Bion-M1 animals displayed a significantly lower number of upregulated genes
than the same tissues from ISS.
Determination of pan-tissue `master regulators' in flight vs ground AEM studies
In order to gain a comprehensive understanding of how microgravity affected the various
rodent datasets, we first isolated the different factors that varied in the datasets?age, sex,
duration of flight, flight condition (ISS, STS or BF), tissue type and the dataset in question. Using
Ingenuity Pathway Analysis (IPA?QIAGEN Bioinformatics) and the significantly
differentially-expressed genes reported in Fig 2, we predicted (based on activation Z-score statistics)
prevalence of change in upstream regulators across all the datasets along with the
corresponding pathways that were affected per cluster of upstream regulators (Fig 3A). Upstream
regulators are molecular factors (enzymes, kinases, transcriptional regulators, cytokines, growth
factors, etc.) which act as central hubs involved with numerous significantly
differentiallyexpressed genes [
]. The Prevalence of Change indicates the percentage of datasets in which a
specific upstream regulator (Fig 3A), a canonical pathway (Fig 3B), or a toxicity function (Fig
3C) was predicted to be either activated or inhibited from the list of differentially-expressed
gene for each dataset. We determined factors included in the Prevalence of Change with either
an activation Z-score >0 or <0. For all three analysis we did not find any clustering for
activation or inhibition which correlated with experimental factors (e.g. age, flight duration, sex,
tissue type). Among the upstream regulators, TP53 had 100% prevalence of change (Fig 3A) in
response to microgravity, while TGF-?1, UCP1, STAT5B, Ins1, HRAS, MYC, ERK, TNF and
PPARGC1A showed significant activation/inhibition in 87% of the dataset (Fig 3A). Among
the impacted pathways, immune system and inflammation-related and TGF-?1 mediated
pathways were the most prevalent ones (Fig 3A and 3B). Interestingly, mild liver toxicity was
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Fig 3. Predicted functions and upstream regulators affected by microgravity. The predicted statistically significant upstream regulators (A), canonical
pathways (B), and toxicity functions (C) determined through IPA from data for each individual tissue/dataset using activation Z-score statistics. Heat map
representation of the activation Z-score values (red = positive activation Z-score for activation and blue = negative activation Z-score for inhibition) were used to
display the data. The prevalence of change (or % of dataset) on the left side of the heat maps represents how common that factor is throughout all datasets/tissues
with the darkest color representing factors with the highest degree in common. Age, sex, tissue type, time in flight, flight conditions, and gene lab dataset reference
is color coded on the top of the heat maps. For the upstream regulators (A) each major cluster of upstream regulators is further analyzed for the major functions it
will impact represented by the Resulting Pathways. D) A bar graph representing predicted activity of TGF-?1 and TP53 through z-score statistics comparing tissue
type, time of flight, and flight conditions, age and sex of the animals.
suggested, with alterations in the pathways ?apoptosis of liver cells? and ?liver tumor?, along
with changes in the pathways associated with ?nephritis? and ?cell death of cardiomyocytes?
The predicted factors, TP53, TGF-?1, ERK, HRAS, Ins1, MYC, PPARGC1A, STAT5B, TNF
and UCP1, were affected by microgravity and exerted an overall global impact on all tissues,
and hence were considered as master regulators. Specifically, TP53 and TGF-?1 were predicted
to have modestly high activity in skeletal muscle (STS), liver, kidney and skin of ISS flown
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animals. While the highest expression of TGF-?1 was observed in EDL, TA and ADR from ISS
flown animals TP53 was relatively highly expressed in the STS derived skeletal muscle (Fig
3D). It is interesting to note in this last figure the high correlation between TP53 and TGF-?1
activation levels across the various experiments, suggesting some synergism between both
Principal Component Analysis technique was used once more on the predicted activity of
the upstream regulators this time to capture similarity between the different datasets. In
contrast to PCA of gene expressions (S1 Fig) which could only be done for the same dataset,
activation Z-score for upstream regulators are platform independent and generate a set of vectors
for each dataset that can be plotted simultaneously into one single PCA plot (Fig 4). Doing so,
we clearly identified some tissue separating from all other tissues. The most separated one was
Thymus, which had been reported during the study to show some degree of atrophy with
significant decrease in leukocyte populations, higher DNA fragmentation, and modulation of
expression of 15 cancer-related genes and 6 T-cell related genes [
]. Mammary gland (MG)
from rats also showed a strong separation at the upstream regulation level. Not as obvious was
Fig 4. Global clustering of the upstream regulators for all tissue types. Principle Component Analysis (PCA) on the upstream regulators for each A) tissue
type, B) age, C) flight duration, and D) flight condition. In A) the Thymus and Mammary Gland (MG) are specifically labeled for clarity in and the muscle data
points are circles in blue. In B) a dark yellow circles all date points representing 16 weeks of age.
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the cluster of all muscle tissues which separated based on the second principal component PC2
(vertical axis in Fig 4A). It was also revealed that there is a slight age dependence with older
mice ( 16 weeks) grouping closer together (Fig 4B). This indicates that the overall age of the
rodents used has a slight impact on the systemic biological response to microgravity. Changes
in predicted activity of the upstream regulators on all the tissues seem to be sex, flight
conditions and duration of flight independent (Fig 4C and 4D).
Focusing on the muscle tissue cluster identified in Fig 4A (all showing up inside or near the
PC2 positive half), one can note that individual tissue samples have a large spread along the
first component in the PCA plot (PC1 ?horizontal axis), suggesting different biological
processes are at play depending on the muscle type along this component. Performing hierarchical
clustering analysis for the activation Z-scores of the upstream regulators in muscle tissue
alone, muscle types naturally divided into two sub groups: Gastrocnemius and Soleus (?Group
1?, green-bordered box in Fig 5) which displayed similar patterns of predicted altered activity
for the majority of the upstream regulators, and Extensor Digitorum Longus, Quadriceps,
Tibialis Anterior and Skeletal Muscle (?Group 2?) which consistently had opposite patterns
from Group 1 (Fig 5). The two groups of muscle tissues displayed significant differences in the
level of predicted activity of crucial signaling genes such as TNF, TGF-?1, TGF?, p38MAPK,
ERK, and IL1?. We also found that the predicted activity of PPARGC-1? was highly
downregulated in Group 1 muscle and upregulated in all but one of the types in Group 2. This could
be due to potential compensatory mechanisms occurring in certain muscle types of the
musculoskeletal system as a result of microgravity exposure. By looking at the anatomy of a mouse
muscle (S2 Fig), one could interpret these results as an unloading of the muscles in Group 1
not supporting the weight of the animal in microgravity anymore, while muscles in Group 2
would see an increased loading due to pull and grabbing to the cage. Interestingly,
microgravity responsive changes in immune-related pathway regulation were observed to be the most
prevalent in the muscle Group 1.
Determination of key microgravity responsive genes and the most connected gene(s)
To determine key genes that impacted the physiological and biochemical processes in rodents
exposed to microgravity, we used a system biology approach we previously introduced [
]. Briefly, this method combines statistical tools contained in Gene Set Enrichment Analysis
] and IPA (detailed description available in methods) to identify the ?key genes? driving the
observed differentially-expressed genes. We hypothesize, these ?key genes? are driving the
biological response to spaceflight conditions. To determine if any commonality exists between the
sets of key genes for each dataset, we diagrammed connectivity between each dataset to show
that a significant number of key genes are shared (Fig 6A and S3 Fig). The specific connections
between the key genes associated with each dataset can be thought of central hubs (Fig 6A).
The details for the rest of the key genes for each dataset can be found in S3 Fig. These key
genes that are shared between the datasets are hypothesized to have the highest global impact
in the host affecting multiple tissues, in response to microgravity.
Note that Fig 6A identified CDKN1A, IL6, ICAM and MAPK11 as key genes, which have
common immune pathways and have previously been shown to be impacted by microgravity
across various tissues [
]. Similarly, Thymus isolated from STS-118 mice also showed
downregulation of expression in several of the signaling nodes such as CCND1, TGF-?1, and
MYC as previously shown . The most connected key gene/driver in all tissues (for studies
comparing Flight versus ground AEM) was TGF-?1 (Fig 6B). Transforming growth factor beta
(TGF-?) is a pleiotropic cytokine; known to play a context specific role in sustaining tissue
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Fig 5. Predicted upstream regulators for muscle tissue affected by microgravity. The statistically significant predicted upstream regulators
determined by IPA from data for each individual tissue/dataset using activation Z-score statistics. Heat map representation with hierarchical
clustering of the activation Z-score values (red = positive activation Z-score for activation and blue = negative activation Z-score for
inhibition) were used to display the data. The prevalence of change (or % of dataset) on the left side of the heat maps represents how common
that factor is throughout all datasets/tissues with the darkest color representing factors with the highest degree in common. Age, sex, tissue
type, time in flight, flight conditions, and gene lab dataset reference is color coded on the top of the heat maps. Each major cluster of
PLOS ONE | https://doi.org/10.1371/journal.pone.0199621
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upstream regulators is further analyzed for the major functions it will impact represented by the Resulting Pathways. The green
box represents the division between group 1 of muscles (consisting of Soleus and Gastrocnemius) and group 2 (consisting of Extensor
Digitorum Longus, Tibialis Anterior, and Quadriceps) having an overall opposite regulation for most of the upstream regulators.
homeostasis predominantly via transcriptional regulation of genes involved in differentiation,
cell motility, proliferation, cell survival along with regulating immune responses during
homeostasis and infection [
In an in vitro study by Blaber et al. [
], the effects of 15 days of microgravity were assessed
on early lineage commitment of mouse embryonic stem cells (mESCs) using the embryoid
body (EB) model of tissue differentiation. Although no significant changes were observed in
TRP53 in EBs differentiated in microgravity, a few apoptosis related genes such as BCL2,
CUL9, FADD and CASP9 were found to be upregulated in EBs grown at 1g relative to the
undifferentiated mESCs. A few p53 target genes such as STAT1, JUN, and EGR1 were also
significantly down regulated in microgravity-differentiated EBs, relative to 1g control EBs [
We compared the overlapping key genes (Fig 6 and S3 Fig) from our analysis (comparing gene
expression values) with the genes identified in the microgravity induced EB differentiation
and categorized the genes based on their functions (S4 Fig). The full list of these genes and
associated categories/pathways are found in Blaber et al [
]. Although in vitro conditions may
not accurately replicate those in vivo, we found similar trends in regulation of several genes in
the functional categories of `apoptosis' and `cell cycle regulation'. BIRC5, CASP9, SFN and TNF
involved in apoptosis and CDC25A and KRAS involved in cell cycle regulation varied
consistently between the two studies. The expression of key genes derived from our analysis of in
Fig 6. Key genes and master regulators driving pan-tissue microgravity response. A) A connectivity network for each set of key genes determined
independently for each tissue/dataset. The key genes associated with each individual tissue/dataset are represented by different background colors as indicated in
the legend. Overlapping key genes (blue font) between each tissue type is represented by background colors and is considered as central nodes. The cluster of key
genes for each dataset that are not connected are grouped and shown as one circle. Details for each of these clusters of key genes for each dataset can be seen in S2
Fig. The color of the gene represents whether the gene is upregulated (red) or downregulated (green) with the shade signifying the degree of regulation. The
different line colors represent the predicted effect of each gene on each other. B) A radial plot of all connections between all key genes with the most connected
gene displayed in the middle (TGF?1). The key genes with direct connections to TGF?1 are shown with all other connections shown with faded color.
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vivo flight data, such as IL6, STAT1, PPARG/A and CCND1/2 (Fig 6) also varied consistently in
our study and in Blaber et al. study, suggesting that in vivo pan-tissue analysis of gene
expression can be used to confirm knowledge about spaceflight related biological effects from in vitro
Delineating the spaceflight-induced circulating miRNA signature and global health risk assessment
Examination of a common circulating factor that could connect the TGF-?1 led myriad
signaling pathways coordinating systemic response to spaceflight, was warranted given the influence
of microRNAs (miRNAs) on TGF-? and p53. MiRNAs are endogenous small noncoding
RNAs that each can target hundreds of mRNAs (also protein and DNA) and function as
posttranscriptional modulators of gene expression, leading to dysregulation of protein expression
and/or mRNA levels [
11, 27, 28
]. Simulated microgravity study detected elevated levels of
miR-223 with decreased proliferation of Hepa1-6 cells [
]. Consistent with this report, our
analyses also predicted miR-223 among the top 10 spaceflight-induced miRNAs, which
potentially could result in down regulation of several key genes such as ICAM1, IGF1R and
upregulation of TLR4/7, LPL and CCR3 etc. (Fig 7A and S1 Table). In another report, modeled
microgravity was found to cause significant overexpression of miR-34a in human
lymphoblastoid cells [
]. Our analyses also detected miR-34 to be significantly activated in response to
spaceflight, which is involved in potential downregulation of several key genes such as
CCND2/1, MYC, CDC25a and LEF1 (Fig 7B). Compared to normal gravity, in-silico analysis of
stimulated human leukocytes showed that brief exposure to spaceflight onboard ISS caused
suppression of miR-21 [
]. The same miRNA was predicted in our analyses to cause
downregulation of critical key genes such as TGF-?1, PPARA, PTEN, CDC25a, NRLC5, ICAM1 and
ILIB and activation of COL1A1 (Fig 7A). Using the list of predicted miRNAs a biological
Health Risk Score (HRS) was calculated. We have previously used a similar method to calculate
a Cancer Risk Score [
]. This HRS provides us with a comprehensive idea about how a group
of spaceflight responsive miRNAs could weigh on overall health, positive for lower health risk
and negative for higher health risk (Fig 7C). miR-25 and miR-17-5p which have positive health
risk showed a predicted inhibition (blue color) leading to a negative impact on health. All the
other predicted miRNAs are activated and are known to have a negative impact on health [32?
34]. MiR-26a-5p is shown in Fig 7C to have both positive and negative impact on heath.
Overall, these spaceflight studies suggest microgravity onboard ISS, STS and Bion-M1 have a strong
negative impact on rodent health, with an HRS score of -12.79.
Fig 7D shows the direct interaction of the majority of the predicted microgravity associated
miRNAs with the master regulators TGF-? and p53. For example, TGF-? and p53 have been
previously implicated in biogenesis of miRNAs including miR-215, which may decrease cell
]. TGF-? can function to increase maturation of miR-21, which was shown to
inhibit PTEN and Sprouty 1, the crucial negative regulators of the Akt and Ras/MAPK
pathways, thus leading to tumor progression [
]. MiR-34 has been found to inhibit TP53
through directly targeting its mRNA .
The analyses presented here demonstrate the utility of publicly available repositories like
NASA's GeneLab to generate hypotheses on the biological impact of microgravity. We found
that, irrespective of rodent type, age, sex, flight condition and time of flight, several master
regulators coordinated major systemic responses towards microgravity. TP53, TGF-?1 and
immune associated signaling were identified to be the most prevalent pan-tissue signaling
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Fig 7. A microgravity associated circulating miRNA signature. A) A radial plot showing the top 10 predicted miRNAs with p-values 1.17 ? 10?8 determined
from all key genes and the key genes directly related the miRNAs. B) All statistical significant miRNAs predicted from all key genes with activation Z-score 2 or
-2 and the corresponding key genes associated with these miRNAs. The predicted activity of each miRNA (blue = inhibition and orange = activation) was
determined through activation Z-score statistics through IPA. C) A graphical representation of the health risk score (HRS) illustrating how each miRNA
contributes to the calculation. The outline for each miRNA represents if the miRNA has a negative impact on health (black), positive impact on health (olive), and
has both positive and negative impact on health (grey). D) Radial plot connecting TGF?1 with p53 and all miRNAs. Through activation Z-score statistics in IPA it
was determined that p53 will be activated due to the impact of TGF?1 and the miRNAs.
nodes activated in response to microgravity along with TGF-?1 being the most connected gene
across all datasets.
Transforming growth factor beta (TGF-?) is a pleiotropic cytokine, belonging to a family,
which consists of 33 members, including the activins, inhibins, bone morphogenetic proteins
(BMPs) and growth and differentiation factors. TGF-? is known to play a context-specific role
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in sustaining tissue homeostasis predominantly via transcriptional regulation of genes
involved in differentiation, cell motility, proliferation, and cell survival along with regulating
immune responses during homeostasis and infection. Several previous reports have indicated
the modulation of TGF-? gene expression with microgravity. For example, reduction in
gravitational force was found to diminish TGF-? expression and apoptosis with higher
carcinoembryonic antigen expression in 3D human colorectal carcinoma cells, as compared to 3D
cultures in unit gravity [
]. In another study, differential regulation of blood vessel growth by
TGF-? using basic fibroblast growth factor was identified in modeled microgravity with
induction of early and late apoptosis, extracellular matrix proteins, endothelin-1 and TGF-?1
]. Bone development involves dynamic remodeling involving gravity-regulated
mechanical stimulation for conservation of mineral content and structure. TGF-? has been
implicated to function as an autocrine and paracrine regulator of bone formation. Human
fetal osteoblastic (hFOB) cells grown in space have been shown to exhibit significantly reduced
TGF-?1 and TGF-?2 transcript levels, 24 hours post-flight [
]. Among in vivo studies, both
short term and long-term flight data report changes in TGF-? mRNA levels in response to
weightlessness and microgravity. During an 11-day space mission, rat skeletal muscle
exhibited reduced TGF-? expression [
], and a 91-day mission aboard the ISS revealed lower
TGF? expression in colonic tissue, systemic lymph node, inguinal and brachial lymph nodes [
Microgravity is one possible cause changing TGF-? expression levels. However, ionizing
radiation found in space can also have impact on TGF-? expression levels and the overall
biology of organisms in space [
]. For example, upregulation of TGF-? following ionizing
radiation may improve DNA repair [
] but it can also elicit a stem-cell self-renewal
signaling in breast cells which has been correlated to increased breast cancer risk in young women
exposed to ionizing radiation [
]. In addition, our results show that the space environment is
globally down-regulating TGF-?1 in the rodent tissue (Figs 6 and 7), which may contribute to
additional DNA damage to the host due to HZE irradiation but which may also lower certain
cancer risk. These results also suggest that microgravity is the predominant factor affecting
TGF-?, leading to lower expression levels in contradiction from what one expects from
exposure to ionizing radiation.
TGF-? signaling has also been known to crosstalk with the second key regulator found in
this study: i.e. p53 [
]. p53 is a transcription factor and in response to genotoxic stress, DNA
damage, oncogene activation, and hypoxia, it is recruited to specific sites in chromatin,
promoting transcription of apoptosis related genes [
]. Again, the relationship with TGF-? is
complex as a report showed that inactivation of p53 can also alter TGF-? signaling, which
ironically displayed both tumor-suppressive and pro-oncogenic functions [
Finally, we hypothesized that the global systemic response to microgravity in rodents,
driven by TGF-?1 is arbitrated by a circulating miRNA signature consisting of thirteen
miRNAs predicted from the key genes. Using the miRNAs functional state and impact on health,
we calculated a theoretical ?health risk score?. The Health Risk Score is based on known
association of this miRNA with reported health outcomes, circumventing the challenging
interpretation for health effects resulting from the complex interaction of TGF-?/p53/Immune signaling
discussed previously. With this novel approach, we predict that short and long-term space
missions exert a strong negative impact on rodent health. Suprisingly the miRNAs that we predict
to drive microgravity biological response have been reported to be involved with simulated
microgravitry experiments previously reported by other investigators. For example, modeled
microgravity-triggered miRNAs have been identified in human peripheral blood lymphocytes
], human leukocytes , human lymphoblastoid cells [
], and in murine
hepatoma cell line [
]. In these studies, miR-223 has been found to regulate cell cycle progression
by targeting E2F1, Fbxw7/Cdc4, IGF1R, Cdk2 and TOX and overexpression of the miRNA
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resulting in suppression of c-Myc expression [
]. Human peripheral blood lymphocytes
(PBLs) cultured for 24 h in microgravity with respect to 1 g, revealed dysregulation of 42
miRNAs, of which miR-34a-5p, miR-34b-5p, and let-7i-3p were found to be in common with our
results. Most of the identified miRNAs were correlated with controlling immune-related (TCR
signaling, adaptive and innate immune signaling and cytokine signaling), apoptosis and cell
proliferation related gene expression.
Several clinical analyses have shown that circulating miRNAs are stable in serum and
plasma, are water-soluble, and are easily detectable. Given their modulation of expression
profile based on physiological and pathological conditions, their role as therapeutics biomarkers is
being investigated . Recent reports on regulation of cell-cell communication by exosomes
have led to researchers to investigate further the role of exosome-derived miRNA that can
function in the target cells in diverse biological processes [
]. Other than microgravity, acute
and chronic levels of ionizing radiation pose threats to astronaut health in space missions [
Radiation induced DNA damage has been studied in detail and changes in miRNA expression
was detected both in vivo and in vitro and based on type of cell, type of radiation and repair
time, miRNA levels were found to differentially synchronize p53 activity [
cellfree, circulating miRNA may be a useful minimally non-invasive biomarker for the detection
of space related health risk, also to monitor progress of the symptoms, and subsequent
response to therapeutic countermeasures.
In conclusion, the current study demonstrates the value of repository data in generating
novel hypotheses through in-silico analysis. This study revealed microgravity-induced critical
genes, signaling pathways and circulating miRNA signatures that may be leveraged for
identification of space related health risks biomarkers and in the design of countermeasures for
long-term manned missions.
S1 Fig. Global clustering of Flight versus AEM ground controls for each individual dataset.
Principle component analysis (PCA) determined from all probes for each dataset comparing
Flight versus AEM ground controls.
S2 Fig. Schematic of the ventral and medial muscles in a mouse. A schematic depicting the
different groups of muscles in the ventral and medial muscles for a mouse. Muscle Group 1
represents the posterior muscles in the lower leg, including the Gastrocnemius and Soleus, and
comprises ~20% of the leg muscle mass. Muscle Group 2a represents the anterior muscles of
the lower leg, including tibialis anterior and extensor digitorum longus, and comprises of
~10% of the leg muscle mass. Muscle Group 2b represents the medial muscles in the upper leg
and comprises of 25% of leg muscle mass. Muscle Group 2c represents the anterior muscles of
the upper leg, including the Quadriceps, and comprises ~20% of the leg muscle mass. Muscle
Group 1 is the cluster represented as Group 1 in Fig 5 and all other muscle groups fall in
Group 2 in Fig 5.
S3 Fig. Detailed network of all key genes and connections between each dataset. A detailed
network of the key genes found in Fig 6A for each dataset. Background color for each dataset
is provided for each set of key genes. The overlapping key genes are similar to Fig 6A. Each
gene is represented by a symbol indicating what type of molecule it is. The color of the gene
represents whether the gene is upregulated (red) or downregulated (green) signifying the
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degree of regulation. The different line colors represent the predicted effect of each gene on
S4 Fig. The overlap of all key genes/drivers compared to genes from existing literature. All
key genes we determined from all tissues/datasets were compared to the genes impacted by
microgravity discussed by Blaber et al [
]. A box plot representation of the fold-change values
of the overlapping genes is displayed with our analysis (black) and the values found by Blaber
et. al. (red) [
]. The background colors represent the functional category for each group of
genes as presented in the Blaber et. al. manuscript [
S1 Table. Predicted microRNAs (miRNAs) from the all key genes determined through
Ingenuity Pathway Analysis (IPA). Activation Z-scores > 0 will predict the miRNA is
activated and activation Z-scores < 0 will predict the miRNAs are inhibited. The bold miRNAs
are the top miRNAs which were used in our analysis.
S1 Materials and Methods. Data from GeneLab platform.
The authors would like to acknowledge the NASA GeneLab Data management team member
Samrawit Gebre for her help in curating and managing the experimental database.
Conceptualization: Afshin Beheshti.
Formal analysis: Afshin Beheshti, Homer Fogle.
Investigation: Afshin Beheshti.
Methodology: Afshin Beheshti.
Supervision: Afshin Beheshti.
Visualization: Afshin Beheshti.
Writing ? original draft: Afshin Beheshti, Shayoni Ray.
Writing ? review & editing: Afshin Beheshti, Daniel Berrios, Sylvain V. Costes.
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