Cell-free DNA (cfDNA) and exosome profiling from a year-long human spaceflight reveals circulating biomarkers

Abstract

The health impact of prolonged space flight on the human body is not well understood. Liquid biopsies based on cell-free DNA (cfDNA) or exosome analysis provide a noninvasive approach to monitor the dynamics of genomic, epigenomic and proteomic biomarkers, and the occurrence of DNA damage, physiological stress, and immune responses. To study the molecular consequences of spaceflight we profiled cfDNA isolated from plasma of an astronaut (TW) during a year-long mission on the International Space Station (ISS), sampling before, during, and after spaceflight, and compared the results to cfDNA profiling of the subject’s identical twin (HR) who remained on Earth, as well as healthy donors. We characterized cfDNA concentration and fragment size, and the positioning of nucleosomes on cfDNA, observing a significant increase in the proportion of cell-free mitochondrial DNA inflight, suggesting that cf-mtDNA is a potential biomarker for space flight-associated stress, and that this result was robust to ambient transit from the International Space Station (ISS). Analysis of exosomes isolated from post-flight plasma revealed a 30-fold increase in circulating exosomes and distinct exosomal protein cargo, including brain-derived peptides, in TW compared to HR and all known controls. This study provides the first longitudinal analysis of astronaut cfDNA during spaceflight, as well as the first exosome profiles, and highlights cf-mtDNA levels as a potential biomarker for physiological stress or immune system responses related to microgravity, radiation exposure, and other unique environmental conditions on the ISS.

Full text

Cell-free DNA (cfDNA) and exosome profiling from a year-long human
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spaceflight reveals circulating biomarkers
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Daniela Bezdan1, Kirill Grigorev1, Cem Meydan1, Fanny A. Pelissier Vatter2, Michele Cioffi2,
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Varsha Rao3, Kiichi Nakahira4, Philip Burnham5, Ebrahim Afshinnekoo1,6,7, Craig Westover1,
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Daniel Butler1, Chris Moszary1, Matthew MacKay1, Jonathan Foox1, Tejaswini Mishra3, Serena
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Lucotti2, Brinda K. Rana8, Ari M. Melnick9, Haiying Zhang10, Irina Matei2, David Kelsen10,
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Kenneth Yu10, David C Lyden2, Lynn Taylor11, Susan M Bailey11, Michael P.Snyder3, Francine E.
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Garrett-Bakelman12,13,14, Stephan Ossowski15, Iwijn De Vlaminck16, Christopher E. Mason1,6,7,17*
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Affiliations:
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1Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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2Children’s Cancer and Blood Foundation Laboratories, Departments of Pediatrics, and Cell and
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Developmental Biology, Drukier Institute for Children’s Health, Meyer Cancer Center, Weill
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Cornell Medical College, New York, NY, USA
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3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
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4Nara Medical University, Kashihara, Nara, Japan
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5Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
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6 The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational
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Biomedicine, Weill Cornell Medicine, New York, NY, USA
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7The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY,
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USA
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8Department of Psychiatry University of California, San Diego, La Jolla, CA, USA
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9Department of Medicine, Weill Cornell Medicine, New York, NY, USA
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10Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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11Department of Environmental & Radiological Health Sciences, Colorado State University, Fort
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Collins, CO, USA.
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12Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
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13Department of Biochemistry and Molecular Genetics, University of Virginia School of
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Medicine, Charlottesville, VA, USA
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14University of Virginia Cancer Center, Charlottesville, VA
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15Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen,
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Germany
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16Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca,
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NY, USA
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17The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY,
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USA
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*Corresponding Author
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Christopher E. Mason
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Weill Cornell Medicine
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1305 York Ave., Y13-05
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New York, NY 10021
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Tel: 203-668-1448
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E-mail: chm2042@med.cornell.edu
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Keywords: NASA, cfDNA, liquid biopsy, mtDNA, mtRNA, exosomes, International Space
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Station, NASA Twins Study
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Abstract
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The health impact of prolonged space flight on the human body is not well understood. Liquid
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biopsies based on cell-free DNA (cfDNA) or exosome analysis provide a noninvasive approach to
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monitor the dynamics of genomic, epigenomic and proteomic biomarkers, and the occurrence of
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DNA damage, physiological stress, and immune responses. To study the molecular consequences
54
of spaceflight we profiled cfDNA isolated from plasma of an astronaut (TW) during a year-long
55
mission on the International Space Station (ISS), sampling before, during, and after spaceflight,
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and compared the results to cfDNA profiling of the subject’s identical twin (HR) who remained
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on Earth, as well as healthy donors. We characterized cfDNA concentration and fragment size,
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and the positioning of nucleosomes on cfDNA, observing a significant increase in the proportion
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of cell-free mitochondrial DNA inflight, suggesting that cf-mtDNA is a potential biomarker for
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space flight-associated stress, and that this result was robust to ambient transit from the
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International Space Station (ISS). Analysis of exosomes isolated from post-flight plasma revealed
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a 30-fold increase in circulating exosomes and distinct exosomal protein cargo, including brain-
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derived peptides, in TW compared to HR and all known controls. This study provides the first
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longitudinal analysis of astronaut cfDNA during spaceflight, as well as the first exosome profiles,
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and highlights cf-mtDNA levels as a potential biomarker for physiological stress or immune
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system responses related to microgravity, radiation exposure, and other unique environmental
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conditions on the ISS.
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Introduction
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A wide range of physiological effects impact the human body during a prolonged stay in
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microgravity, such as headward fluid shift, atrophy of muscles, and decreases in bone density,
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which have been described for astronauts on the international space station (ISS)(Williams et al.,
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2009). In recent years, an increasing number of government and private space agencies have
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formed, and missions to the Moon and Mars are now planned for the late 2020s and 2030s (Iosim
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et al, 2020). These pending missions may span 30 months and require landing on a planet with
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almost no clinical infrastructure for medical monitoring or treatments. Yet, data on physiological
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changes of long-term missions (>6 months) is almost non-existent. These long-duration missions
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and the increasing exposure of humans to spaceflight-specific conditions necessitates the study of
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molecular changes in the human body induced by exposure to spaceflight stressors such as
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microgravity, radiation, noise, restricted diet, and reduced physical work opportunities. The NASA
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Twins study (Garrett-Bakelman et al., 2019) enabled interrogation of the impact of prolonged
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spaceflight on the human biology and cell-to-cell variations in the immune system (Gertz et al.,
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2020); however, there has never been a study on the impact of spaceflight on cell-free DNA
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(cfDNA).
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Molecular signatures informative of human health and disease can be found in cfDNA and nucleic
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acids isolated from plasma, saliva, or urine (Heitzer et al., 2018; Hummel et al., 2018; Siravegna
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et al., 2017; Verhoeven et al., 2018; Volik et al., 2016). Non-invasive methods for monitoring
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health-related biomarkers in liquids such as plasma (‘liquid biopsy’) have already been
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successfully introduced in a wide range of contexts, including: prenatal testing for detection of
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trisomy and micro-deletions (Bianchi et al., 2014; Zhang et al., 2019), cancer diagnostics
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(Bettegowda et al., 2014; Diehl et al., 2008; Wang et al., 2017), monitoring of cancer therapies
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(Birkenkamp-Demtröder et al., 2016; Wan et al., 2020), monitoring of the health of solid-organ
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transplants (Verhoeven et al., 2018; De Vlaminck et al., 2014), and screening for infections
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(Blauwkamp et al., 2019; Burnham et al., 2018; De Vlaminck et al., 2013). Hence, liquid biopsy
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is a potentially useful method for monitoring physiologic conditions of astronauts before, during
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and after spaceflight.
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Indeed, cfDNA is extremely dynamic and responsive, providing strong indicators of DNA damage
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and tumor growth in distal tissues (Newman et al., 2016), immune response or infection (Zwirner
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et al., 2018), and RNA regulatory changes, with an innate capacity to reveal the cells of origin
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undergoing apoptosis or necrosis (Thierry et al., 2016). Various studies have reported changes in
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cfDNA concentration (Zwirner et al., 2018), cfDNA fragment length distribution (Mouliere et al.,
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2011; Underhill et al., 2016), mutation profiles and signatures (Newman et al., 2016), and cfDNA
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methylation (Shen et al., 2018) indicative of physiological conditions such as cancer.
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Mitochondrial DNA (mtDNA) can also be found in the extracellular space, circulating as short
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DNA fragments, encapsulated in vesicles and even as whole functional mitochondria (Amir Dache
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et al., 2020; Song et al, 2020). Several recent studies observed increased levels of cell-free
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mitochondrial DNA (cf-mtDNA) in psychological conditions (Lindqvist et al., 2016, 2018) and
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reduced cf-mtDNA levels in Hepatitis B infected patients associated with a higher risk of
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developing hepatocellular carcinoma (Li et al., 2016). However, since no such information exists
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for using these metrics for astronauts, we investigated the utility of cfDNA for the monitoring of
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the physiologic conditions of astronauts to spaceflight.
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Of note, cfDNA comprises the footprints of nucleosomes, and these nucleosome features enable
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tracing of the tissue-of-origin for cfDNA in normal and disease states, through analysis of nuclear
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architecture, gene structure and expression (Murtaza and Caldas, 2016; Snyder et al., 2016). In
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particular, nucleosome positioning and depletion of short cfDNA sequences reveal footprints of
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transcription factor binding, promoter activity, and splicing, ultimately informing gene regulatory
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processes in the tissue/cell of origin (Snyder et al., 2016). Similar information can be revealed
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from exosomes, which are nano-sized vesicles (size 30–150nm) derived from perinuclear luminal
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membranes of late endosomes/multivesicular bodies and released into extracellular environment
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via multivesicular body fusion within the cell membrane ((Kalluri and LeBleu, 2020; Mathieu et
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al., 2019)) that can mediate long-range physiological crosstalk (Hoshino et al., 2015; Mathieu et
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al., 2019). Exosomes act as vehicles for horizontal transfer of information through their cargo:
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proteins, lipids, metabolites and DNA, as well as coding and non-coding RNAs (Valadi et al.,
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2007; Wortzel et al., 2019). Moreover, exosomes can be powerful mediators of responses to
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environmental stimuli as external and physiological stress impact their release, cargo and function,
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contributing to pathogenesis (Harmati et al., 2019; O’Neill et al., 2019; Qin et al., 2020). Since
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exosomes are abundant in plasma, they are critical components of liquid biopsies (Colombo et al.,
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2014; Hoshino et al., 2020) and analysis of their content can complement the information obtained
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from cfDNA, but there is no information about exosomes in astronauts.
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To address this gap in knowledge, we profiled cfDNA isolated from plasma samples before,
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during, and after the one-year mission on the International Space Station (ISS) to evaluate the
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utility of cfDNA as a means to monitor physiological problems during extended missions in space.
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We also profiled the exosomes of both astronauts after the mission completion. While bulk RNA
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sequencing data have shown widespread gene expression changes in astronauts, including
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mitochondrial RNA (mtRNA) spikes in flight samples from the One-Year Mission (Garrett-
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Bakelman et al., 2019), there has not yet been a study of astronauts that has leveraged cfDNA and
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exosomes. We focused on quantitative measures such as the levels of mitochondrial DNA, cfDNA
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fragment length, and the depletion of nucleosome signatures at transcription start sites. Together,
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our NGS results provide a “whole-body molecular scan”, which can provide a novel measurement
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of the impact of spaceflight on the human body, as well as serve as a continued metric of
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physiology and cellular stress for future long-during missions.
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Results
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Study design and sample collection
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We analyzed circulating cfDNA of a pair of male monozygotic twins over two years, starting when
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they were both 50 years old. During the NASA Twin Study, the flight subject (TW) was aboard
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the International Space Station (ISS) for 340 days, while his identical twin, the ground subject
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(HR), remained on Earth. We collected cfDNA at 12 time points from HR and 11 time points from
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TW. Of the latter, four samples were collected inflight on board of the ISS or space shuttle. In
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addition, we profiled the cfDNA of an unrelated control subject (MS) to simulate the ambient
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return from the ISS. To control for ambient return (AR) effects (return of samples in the Soyuz
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capsule) on the molecular signatures of cfDNA, we subjected two MS samples and one HR sample
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to an extended shipping procedure (see Methods). Plasma and cfDNA were extracted using the
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same protocol for all samples (Methods). We observed a broad range of cfDNA concentration
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between 6.7 ng/ml and 79.9 ng/ml plasma (mean = 27.9 ng/ml, median = 23 ng/ml) across samples
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(Table 1). However, we found no significant difference in cfDNA concentrations between flight,
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ground or control subjects (ANOVA p = 0.49, Supp. Fig. 1A), TW and HR (Wilcoxon rank test p
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= 0.65), and flight and ground samples (Wilcoxon rank test p = 0.352). TW showed borderline
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significantly higher cfDNA concentration pre- and post-flight compared to inflight (Wilcoxon rank
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test p = 0.043), however, this is not significant when comparing TW inflight, TW ground, and
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HR/MS ground (ANOVAR p = 0.4, Supp. Fig 1B). Complementary metadata on the health status
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of TW and HW during the mission has been previously published (Garret-Bakelman et al., 2019),
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and no deviations in medication or exercise regimen were noted in the medical records.
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Cf-DNA fragment length distribution is influenced by the ambient return
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It has previously been shown that cfDNA derived from tumor cells is shorter than cfDNA derived
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from healthy cells (Jiang et al., 2015; Mouliere et al., 2011). This effect can be explained by a
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change in nucleosome binding or by a degradation of nucleotides at the end of nucleosome loops.
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We therefore hypothesized that environmental stressors such as microgravity or radiation could
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also impact the length distribution of cfDNA. Indeed, we found a slight shift to longer cfDNA
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fragment lengths in TW inflight samples (Fig. 1A). However, a similar shift was observed in
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ground samples subjected to ambient return simulation (Fig. 1A, boxplots with yellow border).
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Ambient return samples show a similar peak at the 300 to 400bp fragment length, which is only
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marginally visible for fresh samples (Fig. 1B). Thus, some proportion of long cfDNA fragments
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likely originate from blood cells damaged during return flight or transport from the ISS.
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To examine how this might affect other cfDNA fractions, we next examined cell-free
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mitochondrial DNA (cf-mtDNA). Recent studies indicate that a prominent fraction of cf-mtDNA
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in the plasma is contained within intact, circulating mitochondria (Al Amir Dache, 2020) and that
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larger mtDNA fragments can also arise from blood cell degradation. However, our centrifugation
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step largely removed intact mitochondria and our library preparation comprised mostly smaller
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DNA fragments (at least 75% are <350bp )(Supplemental Fig. 2), including an even smaller
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fraction (<10%) of the aligned reads (Fig. 2). Thus, the observed fractions of cf-mtDNA are mostly
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derived from shorter cf-mtDNA molecules and should represent cf-mtDNA that is randomly
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fragmented and sequenced across the entire mitochondria.
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As further evidence of this, the mitochondrial genome showed continuous read coverage in all
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samples, ranging from 50x-200x coverage (Supplemental Fig. 3), regardless of the collection
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method. Indeed, the length distribution of cf-mtDNA is not affected by ambient return as observed
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for chromosomal cfDNA (Fig. 2B), and the average length does not change significantly in inflight
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samples or AR simulation samples. Even though cf-mtDNA amounts can significantly vary based
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on the donor profiles (Lindqvist et al, 2016) and degree intact vs. fragmented mitochondria, these
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NGS data showed that the total cf-mtDNA profiles show relative uniformity in both length and
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proportion of reads (Fig. 2).
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Levels of cell-free mitochondrial DNA are increased during space flight
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Next we investigated the fraction of cf-mtDNA relative to chromosomal cfDNA in plasma of
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TW, HR, and MS. In order to characterize the cfDNA originating from mitochondria during
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spaceflight, we normalized the count of NGS reads mapping to the mitochondrial chromosome
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(chrM) by chromosome length and the total number of reads in the library, generating a RPKM
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measurement. For comparison, we performed the same procedure with reads mapping to
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chromosome 21. We found a sharp increase of cf-mtDNA for subject TW for inflight samples
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(Fig. 3A) compared to TW ground samples (Wilcoxon rank test p= 0.012), compared to HR
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ground samples (Wilcoxon rank test p= 0.018), and compared to all ground samples of HR and
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TW (Wilcoxon rank test p=0.0045, ANOVA p=0.00049). In contrast, we found no significant
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increase in cfDNA mapping to chromosome 21 (Fig. 3B) in TW-inflight compared to ground
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samples of TW and HR.
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Notably, the mtDNA levels in whole blood increased steadily inflight while on the ISS. Indeed,
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TW had the highest fraction of cf-mtDNA within the first inflight timepoint (T4), including more
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than a 24-fold increase, when compared to ground samples (Fig. 3C, 3D). In the two later
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inflight time points, he had 4- and 8-fold increases compared to pre-flight levels. The normalized
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levels of chromosome 21 cfDNA were stable for both TW and HR for the duration of the
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mission (0.25-0.26 RPKM), revealing no obvious bias due to sample handling (Fig. 3D).
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Interestingly, a positive correlation between mtDNA copy number and telomere length in healthy
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adults has been previously reported, and telomere elongation in blood and urine was also
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observed during spaceflight for TW (Garrett-Bakelman et al., 2019, Luxton et al, 2020).
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Given the previously discussed effects of AR on cfDNA lengths, we tested for potential bias in
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cf-mtDNA levels due to AR. To do this, we compared the cf-mtDNA fraction observed in the
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MS simulated-AR samples (2 samples) to the MS control sample. We found that cf-mtDNA
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levels were actually lower in AR than in FR samples (Fig. 3E), suggesting that the shipping
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procedure from the ISS is likely not causing the observed increase in cf-mtDNA levels seen in
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the inflight samples. In addition, the AR simulation of the ground subject (HR) did not show a
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significant increase of cf-mtDNA levels compared to other HR samples (Fig. 3F). Thus, these
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data suggest that the cf-mtDNA fraction was significantly increased during space flight, and not
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due to the AR blood-from-ISS transport process.
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Nucleosome positioning suggests a shift in cell of origin of cfDNA due to transport conditions
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Given that nucleosome positions are associated with both cfDNA and gene expression (Jiang and
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Pugh, 2009), we computed the nucleosome depletion around nucleosomes at transcription start
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sites (TSS) to infer gene expression (Fig. 4A), as previously demonstrated by Ulz and colleagues
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(Ulz et al., 2016). Indeed, these data indicated that the strength of nucleosome depletion is
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correlated to bulk gene expression from RNA-seq of the same subjects (Garrett-Bakelman et al.,
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2019) (Fig. 4A), with a decreased coverage at the site of the transcriptional start site (TSS) for
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highly expressed genes. Second, we identified the nucleosome footprint of CTCF in gene bodies,
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hypothesizing that nucleosome positioning patterns could reveal broad changes in gene
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regulation during spaceflight. A t-SNE analysis of TW and HR samples showed no flight-
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specific clustering (Fig 4B), indicating that nucleosome positioning identified through cfDNA
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may not be sensitive enough to identify spaceflight-related gene expression changes.
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However, based on the correlations between per-tissue gene expression values (Kim et al., 2014)
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and nucleosome positioning observed on cfDNA, clear tissue signals in cfDNA were inferred for
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all plasma samples. Higher values (Pearson's correlation coefficient) suggested higher gene
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expression and stronger tissue signal (Fig. 5A) for hematopoietic lineages (up to rho = 0.156, n =
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1087411335), mid-range for liver, adrenal gland, and the retina (0.04-0.07) and less so for other
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peripheral tissues (e.g. lung, esophagus, 0.00-0.01). These results are consistent with the
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expected cfDNA prevalence in blood and with previous findings (Snyder et al., 2016). Despite
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such clear signals on tissue of origin, strong clustering of samples was observed, due to the
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confounding effect of ambient return. This was seen in both the tissue-of-origin analysis (Fig.
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5A) as well as TSS protection (Fig. 5B), highlighting the need for controls and correction for any
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degradation. Further, this analysis does not take into account the cf-mtDNA reads, and therefore
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may not reflect the tissue of origin for mitochondrial reads or heteroplasmy.
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Analysis of plasma-circulating exosomes post-flight
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To determine how prolonged space missions and Earth re-entry impact circulating exosomes, we
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analyzed exosomes from the plasma of TW three years post-return to Earth, and compared their
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size, number and proteomes to plasma-derived exosomes isolated from HR and 6 age-matched,
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healthy controls. Exosomes were isolated by differential ultracentrifugation and both the size and
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number of exosomes were characterized by nanoparticle tracking analysis (NTA) (Fig. 6A-E).
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While the median size of exosomes was similar between HR, TW and healthy controls (Fig. 6 A-
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D), the number of particles was ~30 times higher in TW compared to HR and healthy controls
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(Fig. 6 E). Proteomic mass spectrometry analysis revealed that TW, HR and control exosomes
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packaged similar numbers of proteins, including a total of 191 exosomal proteins shared among
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all samples. HR’s exosome catalog contained 26 unique proteins, TW exosomes contained 61
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unique proteins, and healthy controls contained 105 unique proteins (Fig. 6F).
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Hierarchal clustering of the exosomal proteins revealed distinct signatures of HR and TW, which
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clustered apart from the six controls. Interestingly, classification of the pathways using
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Metascape (GO processes, KEGG pathways, Reactome gene sets, canonical pathways, and
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CORUM complexes)(Zhou et al., 2019) revealed that TW exosomes were enriched in proteins
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involved in proteasome pathways (Fig. 6H). TW exosomes also packaged CD14, a pro-
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inflammatory monocyte marker, consistent with the increase in CD14+ monocytes observed post-
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return to gravity in immune markers studied upon return to Earth (Gertz et al, 2020). Notably,
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basigin and integrin β1 proteins, which are correlated with cancer progression and inflammation
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(Hoshino et al., 2015, 2020; Keller et al., 2009; Yoshioka et al., 2014), were also detected in TW
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exosomes, but not in HR or healthy control exosomes.
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Consistent with previous findings demonstrating microgravity downregulating adaptive
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immunity, particularly B cells (Cao et al., 2019), both TW and HR exosomes contained fewer
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immunoglobulins compared to healthy controls (Fig. 6G). Surprisingly, two brain-specific
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proteins, Brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2) and Brain-
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specific angiogenesis inhibitor 1-associated protein 2-like protein 1 (BAIAP2L1), were found in
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TW plasma-derived exosomes (Supplemental Table 1, Supplemental Fig. 4A), yet were not
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detected in the plasma of HR or healthy controls. In contrast, HR exosomal cargo was enriched
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in proteins associated with regulation of apoptotic pathways (Théry et al., 2001) and ATP
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biosynthesis (Fig. 6I). Moreover, we observed that the 20S proteasome, but not the regulatory
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19S proteasome, is found uniquely associated with the plasma-circulating exosomes in the flight
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subject (TW) 3 years after his return to Earth (Fig. 6G). Finally, both TW and HR exosomes, but
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not controls, were enriched in specific components of the humoral immune response and
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leukocyte migration, including the CD53 tetraspanin (Supplemental Table 2, Supplemental
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Fig. 4B) which could reflect either biology shared by the twins or changes associated with travel
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to space; however, analysis of plasma exosome samples from genetically unrelated astronauts
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would be required to distinguish between these possibilities.
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Discussion
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Our study focused on cfDNA and exosomes collected during the NASA Twins study, a
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longitudinal, multi-omic experiment examining the effects of long-term spaceflight on the human
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body. In particular, we revealed cf-mtDNA fraction to be a potential new biomarker of
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physiological stress during prolonged spaceflight, though the total cfDNA concentration is not
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significantly correlated with spaceflight. We further observed unique exosome and exosomal
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protein signatures within TW several years after the year-long mission, including an increased
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amount of exosomes and brain-specific proteins (BAIAP2 and BAIAP2L1). Of note, we identified
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multiple biases likely caused by ambient return (AR) blood draws from the ISS, including results
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of tissue of origin deconvolution through nucleosome positioning as well as cfDNA fragment
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length. As such, future studies will need to control for AR affects if they wish to examine these
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molecular dynamics. As an example, DNA could be extracted in space (Castro-Wallace et al.,
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2017) and either cryopreserved to increase its stable during transport or directly sequencing inflight
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to minimize biases and obtain results faster (McIntyre et al., 2016, McIntyre et al., 2019).
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Interestingly, analysis of plasma exosomes isolated post-return to Earth revealed unique
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alterations in TW relative to HR and healthy controls, such as a dramatic increase in the number
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of circulating particles as well as changes in the types of protein cargo. Since the majority of
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plasma circulating exosomes are derived from immune cells, it is likely that these alterations
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reflect immune dysfunction associated with space travel and return to gravity. Specifically, the
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reduction in TW exosomal immunoglobulin levels and the presence of CD14, a macrophage
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marker, may signal a shift towards innate immunity, as even short-term chronic exposure to
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cosmic radiation and microgravity leads to a decrease in adaptive immune cells (Cao et al., 2019;
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Fernandez-Gonzalo et al., 2017). However, circulating exosomes also reflect systemic changes in
324
homeostasis and physiology, as demonstrated by the packaging of brain-specific proteins in TW
325
which were not seen in control exosomes, which may indicate long-term altered expression of
326
exosomes from the brain after spaceflight. Previous studies had shown that microgravity affects
327
tight junction protein localization within intestinal epithelial cells (Alvarez et al., 2019). It is
328
conceivable that prolonged space travel could exert similar effects on tight junctions within the
329
blood-brain barrier, allowing for more exosomes to enter the peripheral blood.
330
331
One remarkable finding of our study is that the 20S proteasome, but not the regulatory 19S
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proteasome, is found uniquely associated with the plasma-circulating exosomes in the flight
333
subject 3 years post his return to Earth. Recent research has discovered the ubiquitin-independent
334
proteolytic activity of the 20S proteasome and its role as the major degradation machinery under
335
oxidizing conditions (Aiken et al., 2011; Deshmukh et al., 2019; Pickering and Davies, 2012).
336
Elevated levels of 20S proteasome have been detected in the blood plasma from patients with
337
various blood cancers, solid tumors, autoimmune diseases and other non-malignant diseases
338
(Deshmukh et al., 2019; Sixt and Dahlmann, 2008). It is also reported that active 20S
339
proteasomes within apoptotic exosome-like vesicles can induce autoantibody production and
340
accelerate organ rejection after transplantation (Dieudé et al., 2015), reduce the amount of
341
oligomerized proteins (Schmidt et al., 2020).and reduce tissue damage after myocardial injury
342
(Lai et al., 2012), and are correlated with cancer and other pathological status such as viral
343
infection and vascular injury (Dieudé et al., 2015; Gunasekaran et al., 2020; Tugutova et al.,
344
2019). The elevated circulating exosomal 20S proteasome in the flight subject may reflect the
345
increased physiological need to clear these proteins resulting from long-term blood, immune or
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other physiological disorders caused by various stress factors during the flight or return to
347
gravity (Ben-Nissan and Sharon, 2014, Vernice et al, 2020). Study of plasma exosomes obtained
348
from flight subjects at other time points including pre- and inflight will be necessary to further
349
examine whether plasma exosomal proteasome can serve as biomarker for pathological
350
processes associated with space flight.
351
352
There are limitations in the study design that prevent broad biological conclusions. First, the
353
sample number is too small to control for all types of potential biases and results may be
354
somewhat driven by individual health issues. Second, there is no comparable experimental data
355
to date and the effect of return to gravity in the Soyuz capsule on the integrity of the sampled
356
material is unknown. Third, the exosome samples have been taken post-flight and can only
357
inform about long-term effects of extended spaceflight. However, this study stands as a
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demonstration of the applications and possibilities of utilizing cfDNA and exosome profiling to
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monitor astronaut health and can improve the study design of future missions and research
360
(Iosim et al, 2019, Nangle et al, 2020).
361
362
In summary, we identified cell-free mitochondrial DNA (cf-mtDNA) as a novel biomarker of
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physiological stress during prolonged spaceflight, which is stable even during transport from the
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ISS. However, we demonstrated that transport-induced biases for cell-type deconvolution from
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cfDNA needs to be improved in order to be used as a “molecular whole body scan”, and/or
366
deployment of more real-time methods (e.g. inflight sequencing). Also, we observed that
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exosome concentration in plasma and unique exosomal proteins such as 20S proteasomes, CD14,
368
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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9
and BAIAP2 demonstrate characteristic changes in the flight subject (TW), potentially caused by
369
physiological stress during prolonged spaceflight. Overall, these data and methods provide novel
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metrics and data types that can be used in planning for future types of astronaut health
371
monitoring, as well as help establish non-invasive molecular tools for tracking the impact of
372
stress and spaceflight during future missions.
373
374
375
Acknowledgements
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We would like to thank the Epigenomics Core Facility and the Scientific Computing Unit (SCU)
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at Weill Cornell Medicine, as well as the Starr Cancer Consortium (I9-A9-071) and funding from
378
the Irma T. Hirschl and Monique Weill-Caulier Charitable Trusts, Bert L and N Kuggie Vallee
379
Foundation, the WorldQuant Foundation, The Pershing Square Sohn Cancer Research Alliance,
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NASA (NNX14AH51G (all Twins Study principal investigators); NNX14AB01G (S.M.B.); and
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NNX17AB26G (C.E.M.), NNX14AH52G), the National Institutes of Health (R25EB020393,
382
R01NS076465, R01AI125416, R01ES021006, R01AI151059, 1R21AI129851, 1R01MH117406),
383
TRISH (NNX16AO69A:0107, NNX16AO69A:0061, NIH/NCATS KL2-TR-002385), the Bill
384
and Melinda Gates Foundation (OPP1151054), the Leukemia and Lymphoma Society (LLS)
385
grants (LLS 9238-16, Mak, LLS-MCL-982, Chen-Kiang).
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Disclosure Statement
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S.M.B. is a cofounder and Scientific Advisory Board member of KromaTiD, Inc., CEM is a
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cofounder and board member for Biotia, Inc. and Onegevity Health, Inc., as well as an advisor or
390
grantee for Abbvie, Inc., ArcBio, Daiichi Sankyo, DNA Genotek, Karius, Inc., and Whole Biome,
391
Inc. DB is a cofounder of Poppy Health, Inc. and Analog Llc.
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393
Author Contributions
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CEM, DB and DCL conceived the study, DB, CM, EA, SO, FAV, HZ, IM, KG and PB and, wrote
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the manuscript DB,BS, FEG, DBU, DPK, KHY, KN, TL, VR, FAV sample collection and/or
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processing DB, CM, SO, HZ, IM, JF, KG and PB Bioinformatic and Analytics, AM, BS, CEM,
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CM, CW, EA, FEG, IV, MC, MPS, RKB, SL, SO and TM review manuscript and guided
398
interpretation. All authors read and approved the manuscript.
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400
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Methods
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403
Sample collection
404
In the NASA Twin Study spanning 24 months we collected blood samples at 12 time points from
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the twin on earth (HR) and 11 time points from the twin in space (TW), as previously
406
described(Garrett-Bakelman et al., 2019). From TW, samples were collected before the flight
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(PRE-FLIGHT), during the flight (FLIGHT) and after the flight (POST-FLIGHT). Specimens
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were processed as previously described(Garrett-Bakelman et al., 2019). Briefly, whole blood was
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collected in 4mL CPT vacutainers (BD Biosciences Cat # 362760,) per manufacturer’s
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recommendations, which contained 0.1M sodium citrate, a thixotropic polyester gel and a FICOLL
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Hypaque solution. Hence our specimens were not exposed to heparin. Samples were mixed by
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inversion. Samples collected on ISS were stored at 4°C after processing and returned by the Soyuz
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capsule. There was an average of 35-37 hours from collection to processing, including repatriation
414
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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10
time. Plasma was obtained by centrifugation of the CPT vacutainers at 1800 X g for 20 minutes at
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room temperature, both for the ISS and for the ground-based samples. Finally, plasma was
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collected from the top layer in the CPT vacutainer and flash frozen prior to long term storage at -
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80oC.
418
419
To simulate batch effects between fresh material (samples collected on earth) and ambient return
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material (samples collected during flight and returned via Soyuz capsule at 4°C), we generated 3
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control samples (MS) representing fresh (FR) and ambient return (AR) material as described
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before (Garrett-Bakelman et al., 2019). Whole blood of a male volunteer of similar age and
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ethnicity as HR/TW was collected in three CPT tubes. Plasma was collected and stored as
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described for the TW and HR specimens. To generate the ambient return control (AR) two CPT
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vacutainers were shipped at ambient temperature (4°C) from Stanford University to Weill Cornell
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Medicine and back as air cargo. The returned CPT vacutainers were spun at 300 X g for 3 minutes
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and aliquoted. One aliquot from each tube was spun once more at 1800 X g for 3 minutes to
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completely clear the plasma of cell debris, resulting in the final AR controls. The aliquoted plasma
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was stored at -80C.
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431
cf DNA extraction and comparison of cfDNA concentrations in ground and flight subjects
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Between 250ul and 1 ml plasma was retrieved from HR, TW and MS samples. The frozen plasma
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was thawed at 37C for 5min and spun at 16000g for 10 minutes at 4C to remove cryo-precipitates.
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The volume of each plasma sample was brought up to 1ml using sterile, nuclease-free 1X
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phosphate buffered saline pH 7.4. Circulating cell-free nucleic acid (ccfNA) was extracted using
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the Qiamp Circulating Nucleic Acid kit (Qiagen, USA) following the manufacturer’s protocol.
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ccfNA was extracted in 50ul AE buffer. Concentration and size distribution information was
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obtained by running 1ul of ccfNA on the Agilent Bioanalyzer using the High Sensitivity DNA chip
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(Agilent technologies, CA, USA). ~15ul aliquots were set aside for cell-free DNA or DNA
440
methylation analyses and stored at -80C. A range of extracted cfDNA of 1ng-38ng/mL plasma has
441
been reported for healthy donors, while cancer patients often show higher levels of 30-50ng/ml
442
(Table 1). In the HR, TW and control samples we extracted between 6.7 ng/ml and 79.9 ng/ml
443
plasma (mean = 27.9 ng/ml, median = 23 ng/ml)(Table 1). We tested if there is a significant
444
difference between HR, TW or MS as well as FR and AR samples using Wilcoxon rank test (R
445
function wilcox) for pairwise comparisons and ANOVA (R function anova) for multi-group
446
comparisons. We furthermore visualized the distributions of the groups HR, TW and MS as
447
boxplots using the R package ggplot2.
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449
Q-PCR Analysis of cfDNA
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The frozen plasma was thawed at 37C for 5min and spun at 16000g for 10 minutes at 4C to remove
451
cryo-precipitates. DNA level in samples was measured by SYBR Green dye-based qPCR assay
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using a PRISM 7300 sequence detection system (Applied Biosystems) as described previously
453
(Nakahira PLoS Med. 2013, Garrett-Bakelman Science 2019, PMIDs: 24391478 and 30975860).
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The primer sequences were as follows: human NADH dehydrogenase 1 gene (hu mtNd1): forward
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5’-ATACCCATGGCCAACCTCCT-3’, reverse 5’- GGGCCTTTGCGTAGTTGTAT-3’. Plasmid
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DNA with complementary DNA sequences for human mtDNA was obtained from ORIGENE
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(SC101172). Concentrations were converted to copy number using the formula;
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mol/gram×molecules/mol = molecules/gram, via a DNA copy number calculator
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(http://cels.uri.edu/gsc/cndna.html; University of Rhode Island Genomics and Sequencing Center).
460
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted November 8, 2020. ; https://doi.org/10.1101/2020.11.08.373530doi: bioRxiv preprint

11
The thermal profile for detecting mtDNA was carried out as follows: an initiation step for 2 min
461
at 50°C is followed by a first denaturation step for 10 min at 95°C and a further step consisting of
462
40 cycles for 15 s at 95°C and for 1 min at 60°C. MtDNA levels in all of the plasma analyses were
463
expressed in copies per microliter of plasma based on the following calculation: c=Q x
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VDNA/VPCR x 1/Vext; where c is the concentration of DNA in plasma (copies/microliter
465
plasma); Q is the quantity (copies) of DNA determined by the sequence detector in a PCR; VDNA
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is the total volume of plasma DNA solution obtained after extraction; VPCR is the volume of
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plasma DNA solution used for PCR; and Vext is the volume of plasma extracted.
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469
Library generation and sequencing
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DNA libraries were generated using the NEBNext DNA Library Preparation Kit Ultra II (New
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England Biolabs, USA). Libraries were generated using 15ul of the ccfNA according to the
472
manufacturer’s instruction. Following end-repair and dA-tailing, adaptor ligation was performed
473
using 15-fold diluted adaptors. After removal of free adaptors using Agencourt magnetic beads
474
(Beckman Coulter, USA), the libraries were PCR-amplified for 12 cycles using primers
475
compatible Illumina dual-index sequences. Following bead cleanup for primer removal, the
476
libraries were run on the Agilent Bioanalyzer to estimate size and concentration. All libraries were
477
pooled at equal concentration and sent to New England Biolabs, Ipswich MA for sequencing.
478
Preliminary sequencing on Illumina Miseq indicated the presence of adaptor dimers in some of the
479
libraries. Therefore, the individual libraries were subjected to an additional round of bead
480
purification and size and concentration estimation. Subsequently, all libraries were pooled again
481
and sequenced on the NovaSeq 6000 using an S2 flow cell and 200-cycle kits (2x100). We finally
482
obtained 4.9 and 4.1 billion reads passing quality filters.
483
484
cfDNA sequence analysis
485
Samples were de-multiplexed using the standard Illumina tools. Low quality bases were trimmed
486
and Illumina-specific sequences and low quality sequences were removed from the sequencing
487
data using Trimmomatic-0.32. Filtered, paired-end reads were aligned using BWA-mem to the
488
hg38 human reference genome with the bwa-postalt option to handle alternative alignments. The
489
resulting BAM files were post-processed (e.g. sorted) using samtools. Duplicate sequences were
490
removed and only reads aligning in concordant pairs were used for further analysis. The fragment
491
length distribution was generated by plotting the distance between read 1 and 2 obtained from the
492
BAM file of each sample. Histograms and boxplots of the fragment length distribution for the
493
autosomes (Figure 1) and for the mitochondrial genome (Figure 2) for all samples were generated
494
using the R package ggplot2.
495
496
Analysis of cell free mitochondrial DNA
497
cfDNA read counts by chromosome (including the mitochondrial genome labeled ChrMT) were
498
extracted using a ‘edtools coverage -a feature_file -b sample.bam –counts’, where feature_file
499
contains the definition (name, start, end) of all chromosomes. Read counts per feature were length-
500
normalized using the well-established reads-per-kilobase per million formula frequently applied
501
to normalize RNA-seq data(Mortazavi et al., 2008). We used the R package ggplot 2 to visualize
502
differences in the normalized cfDNA fraction (RPKM) originating from the mitochondrial genome
503
between HR, TW and MS and over time during the mission (longitudinal analysis). We used
504
Wilcoxon rank test (R function Wilcox) to test if the measurements of two conditions (e.g. TW on
505
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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12
ground vs. TW in flight) are significantly different. To analyze the differences among multiple
506
groups we applied ANOVA (R function anova).
507
508
Nucleosome positioning analysis
509
Filtered, paired-end reads were aligned using BWA-mem to the hg37 human reference genome
510
and post-processed using samtools: duplicate sequences were removed, and only reads aligning in
511
concordant pairs were used for final analysis. The sequence read coverage in 10-kbp windows (-5
512
kbp to 5kbp) around the transcription start sites of all genes was determined using the samtools
513
depth function. From the positions of the reads, nucleosome occupancy was inferred, and its
514
periodograms calculated. A list of transcription start sites organized by transcriptional activity
515
(measured in FPKM) was used to assign activity(Ulz et al., 2016). The depth of coverage was
516
summed across genes according to transcriptional activity category (as depicted in Figure 4A).
517
The coverage was normalized by subtracting the mean value from the intervals [TSS-3 kbp, TSS-
518
1 kbp] and [TSS+1 kbp, TSS+3 kbp]. According to the method in Snyder et al., 2015, FFT values
519
for the periods of 193-199bp were correlated with the gene level expression matrix, and the
520
resulting tissue-periodicity correlations ranked by the value of Pearson's correlation coefficient
521
and clustered (Ward method with Euclidean distances) to investigate characteristics of tissue-of-
522
origin dependent on sample type.
523
524
Purification and Mass spectrometry analysis of plasma-circulating exosomes
525
Blood plasma was collected from TW 3 years post return of TW to Earth. Blood was also collected
526
from HR (within one day of blood collection from TW) and from six age-matched healthy controls.
527
Exosomes were purified by sequential ultracentrifugation, as previously described (Hoshino et al.,
528
2015). Plasma samples were centrifuged for 10 minutes at 500xg, 20 minutes at 3,000xg, 20
529
minutes at 12,000xg, and the supernatant was collected and stored at -80°C for exosome isolation
530
and characterization by NTA (NanoSight NS500, Malvern Instruments, equipped with a violet
531
laser (405 nm). Samples were thawed on ice and centrifuged at 12,000xg for 20 min to remove
532
large microvesicles. Exosomes were collected by spinning at 100,000xg for 70min, washed in PBS
533
and pelleted again by ultracentrifugation in a 50.2 Ti rotor, Beckman Coulter Optima XE or XPE
534
ultracentrifuge. The final exosome pellet was resuspended in PBS, and protein concentration was
535
measured by BCA (Pierce, Thermo Fisher Scientific). Mass spectrometry analyses of exosomes
536
were performed at the Rockefeller University Proteomics Resource Center using 10 μg of
537
exosomal protein as described previously(Hoshino et al., 2015; Zhang et al., 2018). Heatmap and
538
complete Euclidean clustering was performed with Morpheus,
539
(https://software.broadinstitute.org/morpheus). Pathway analysis was performed with
540
Metascape(Zhou et al., 2019).
541
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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13
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Figures, Tables, and Supplementary Tables/Figures for:
Cell-free DNA (cfDNA) and exosome profiling from a year-long human
spaceflight reveals circulating biomarkers
Daniela Bezdan1, Kirill Grigorev1, Cem Meydan1, Fanny A. Pelissier Vatter2, Michele Cioffi2,
Varsha Rao3, Kiichi Nakahira4, Philip Burnham5, Ebrahim Afshinnekoo1,6,7, Craig Westover1,
Daniel Butler1, Chris Moszary1, Matthew MacKay1, Jonathan Foox1, Tejaswini Mishra3, Serena
Lucotti2, Brinda K. Rana8, Ari M. Melnick9, Haiying Zhang10, Irina Matei2, David Kelsen10,
Kenneth Yu10, David C Lyden2, Lynn Taylor11, Susan M Bailey11, Michael P.Snyder3, Francine E.
Garrett-Bakelman12,13,14, Stephan Ossowski15, Iwijn De Vlaminck16, Christopher E. Mason1,6,7,17*
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Figures
Figure 1. Size distribution of cfDNAs in ambient return, ambient return simulation and fresh
samples. (A). Ambient return simulation samples (control and ground samples with yellow border)
show a highly similar pattern as observed for inflight samples (blue box with yellow border). Long
cfDNA fragments likely originate from blood cells damaged during transport. (B) Ambient return
samples show an increased fraction of cfDNA with fragment length > 300bp compared to fresh
samples. Our experimental procedure does only allow interrogation of DNA fragments up to a
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length of 500bp, thus the content of long mtDNA fragments contained in intact circulating
mitochondria is not reflected in this analysis.
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Figure 2. Size distribution of cf-mtDNAs. We observed a wider range of cf-mtDNA lengths
compared to total cfDNA (from 100 to 600bp). (A) cf-mtDNA size distributions are similar in
ground, flight and control samples, and are not affected by ambient return (AR) or AR simulation.
(B) Average length of cf-mtDNA is significantly longer than the average length reported for
chromosomal cfDNA (~250bp vs. ~160bp). The average length of cf-mtDNA is not affected by
sample type (control, flight, ground) or sample handling (fresh, AR, AR simulation).
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Figure 3. Analysis of normalized cfDNA read counts by chromosome, including the
mitochondrial genome. (A) TW exhibits a significant increase in cell-free mtDNA during space
flight compared to TW and HR ground samples. Counts are reads per kilobase per Million reads,
or RPKM (B) Chromosomes do not show any change in RPKM during space flight, as exemplified
using chr21. (C) Q-PCR based validation of increased cf-mtDNA fraction in plasma during space
flight. (D) Normalized cf-mtDNA fraction and fraction of reads mapping to chr21 for 12 time point
during the mission (T4-T6 = space flight). The highest increase in cf-mtDNA fraction is observed
during the first months on ISS. (E) Ambient return simulation using two control samples showed
no increase in cf-mtDNA compared to fresh samples, but a slight reduction. (F) Ambient return
simulation (AR) using one HR ground sample did not show a significant increase in cf-mtDNA
fraction. Two outliers within the fresh samples (FR) indicate that other conditions (e.g. stress,
disease, immune reaction) could have influenced cf-mtDNA levels of HR on the ground.
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Figure 4. cfDNA nucleosome footprinting. (A) Nucleosome depletion in cfDNA around
transcription start sites (TSS) is highly correlated with the expression of the respective genes and
can therefore be used to estimate promoter activity and gene expression. (B) t-SNE based on
genome-wide promoter nucleosome footprint of cfDNA samples reveals no clustering of flight
subject and ground subject samples.
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Figure 5. Tissue of origin deconvolution. (A) Correlation coefficients (multiplied by -1) for each
tissue in each sample, clustered by sample and by tissue. The highest signals are, expectedly, from
cells of hematopoietic origin. Spaceflight-dependent dynamics of tissue signal are confounded by
the effect of ambient return, as suggested by ambient return samples tending to cluster together
regardless of other features. (B) Clustering of samples using TSS protection in cfDNA as a measure
of gene expression (lower protection correlates to higher expression). Ambient return samples
cluster tightly together and uncover two major clusters of genes whose expression differs
significantly from other samples, suggesting transport-related degradation processes or
nucleosome detachment. Distribution of mean TSS protection per gene in ambient return and fresh
samples is significantly different (t-test p<1e-3).
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Figure 6. Characterization of plasma-derived exosomes isolated from HR and TW. Plasma
samples were collected 3 years (TW) and 9 years (HR) post-flight. Nanosight profiles showing
size distribution for exosomes isolated from the plasma of (A) Control, (B) HR, and (C) TW.
Median size of exosomes (D) and exosome concentration (E) in TW (n=1), HR (n=1), and controls
(n=6). (F) Venn diagram of exosomal proteins identified by mass spectrometry in plasma isolated
from HR, TW and age-matched healthy controls. (G) Heatmap of plasma-derived exosomal
proteins for HR, TW, and age-matched healthy controls. Pathway analysis of exclusive plasma-
derived exosomal proteins from (H) TW, (I) HR, and (J) age-matched healthy controls.
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Table 1. Overview of all plasma samples obtained during the 1-year mission. Subjects for this mission included
the ground subject HR (blue), flight subject TW (green) and control subject MS (yellow). Samples taken on the ISS
are highlighted in red. The last two columns show the concentration of cfDNA per ml plasma and the Q-PCR results
for the mitochondrial transcript mtNd1 in copy/µl plasma.
Time
Subject
Sample name
Total per
plasma
mtNd1
[ng/ml
plasma]
Q-PCR
[cp/µl
plasma]
PRE-FLIGHT
GD-114
15.5
44
GD-104
79.9
2159.6
GD-66
11.6
251.7
FLIGHT
GD 125
6.7
277
GD 189
20.5
215.1
GD 204
64.4
193.7
GD 298
43.4
1502.8
POST-FLIGHT
GD+2
60.6
157.7
GD+65
16.1
136.3
GD+137
19.1
68.7
GD+181
7.1
147.3
GD+192
22.9
1165.2
AMBIENT
MS
8.6
3080.6
RETURN
MS_AR
19.7
3590.8
CONTROL
MS_AR_1800
16.8
1330.4
PRE-FLIGHT
L-162
44.8
543.1
L-148
16.3
737.6
L-71
46.9
466
FLIGHT
FD 76
20.1
6379.7
FD 259
11.2
786.9
FD 340
22.7
1735.3
R+0
16
374.7
POST-FLIGHT
R+35
23.5
86.4
R+104
21.5
37.7
R+190
58.8
138.5
R+201
29.9
349.1
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Supplemental Figure 1: cfDNA concentration in plasma of ground subject (HR), flight subject
(TW) and ground controls (MS). (A) Comparison of cfDNA concentrations between HR, MS and
TW samples. (B) Comparison of TW pre- and –post-flight to TW inflight and to the combined
ground samples of HR and MS. No significant differences were observed.
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Supplemental Figure 2. Library Fragment distribution for the cell-free DNA libraries.
Pooled libraries were run on the Agilent Bioanalyzer 2100, with the entire fragment range area
estimated to be 4,888.9. The first fraction peak was estimated to be at 313bp and the second peak
was at 466bp. Given that the Illumina adapters add 120bp to each fragment size, this means that
the estimated size of the first fragment set is 193bp and the second set is 346bp. The total area of
the first peak represents 74.8% (3,660.3/4,888.9) of the signal.
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Supplemental Figure 3: Read coverage distribution across the mitochondrial genome for all
samples analyzed in this study. We observed continuous coverage of the complete mitochondrial
genome in all samples.
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Supplemental Figure 4: (A) Pathway analysis of inclusive plasma-derived exosomal proteins
from TW (n=1), HR (n=2), and (J) age-matched healthy controls (n=6). (B) Pathway analysis of
inclusive plasma-derived exosomal proteins from TW and HR excluding age-matched healthy
controls
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Supplementary Tables
Supplemental Table 1: List of inclusive proteins in exosomes isolated from the plasma of TW,
HR and age-matched healthy controls.
Inclusive in TW, HR and control exosomes
35 kDa inter-alpha-trypsin inh ibitor heavy chain H4 Fibrinogen gamma chain Platelet glycoprotein 4
Actin, cytoplasmic 1 Fibulin 1 Polymeric immunoglobul in receptor
Adiponectin Ficolin-2 Protein AMBP
Afamin Ficolin-3 Protein IGHV1-46
Alpha-1-acid glycoprotein 1 Galectin-3-binding protein Protein IGHV1OR15-1
Alpha-1-acid glycoprotein 2 Gelsolin Protein IGHV2-26
Alpha-1-antichymotrypsin Glutathione p eroxidase Protein IGHV3-13
Alpha-1-antitrypsin Glyceraldehyde-3-ph phate dehydrogenase Protein IGHV3-15
Alpha-1B-glycoprotein Haptog lobin Protein IGHV3-21
Alpha-2-antiplasmin Haptoglobin-related p rotein Protein IGHV3-35
Alpha-2-HS-glycoprotein HCG2043239 Protein IGHV3-38
Alpha-2-macroglobulin Heat shock cognate 71 kDa protein Protein IGHV3-43
Angiotensinogen Hemoglobin subun it alpha Protein IGHV3-49
Antithrombin-III Hemoglobin subun it beta Protein IGHV3-64
Apolipoprotein A-I Hemoglobin subun it delta Protein IGHV3-73
Apolipoprotein A-II Hemopexin Protein IGHV3OR15-7
Apolipoprotein A-IV Heparin cofactor 2 Protein IGHV3OR16-12
Apolipoprotein B-100 Histidine-rich glycoprotein Protein IGHV3OR16-13
Apolipoprotein C-III Ig alph a-1 chain C region Protein IGHV3OR16-9
Apolipoprotein C-IV Ig alpha-2 chain C region Protein IGHV4-28
Apolipoprotein D Ig delta ch ain C region Protein IGHV4-34
Apolipoprotein E Ig g amma-1 chain C region Protein IGHV4-4
Apolipoprotein L1 Ig gamma-2 chain C region Protein IGHV5-51
Apolipoprotein M Ig gamma-3 chain C region Protein IGHV6-1
Apolipoprotein(a) Ig gamma-4 chain C region Protein IGKV1-16
Band 3 anion transport protein Ig heavy chain V-I region 5 Protein IGKV1-17
Beta-2-glycoprotein 1 Ig kappa chain V-IV region Protein IGKV1-33
C4b-B Ig lambda-2 chain C regions Protein IGKV2-30
C4b-binding p rotein alpha chain Ig mu chain C region Protein IGKV2-40
C4b-binding p rotein beta chain IgGFc-bin ding protein Protein IGKV2D-24
Carboxypeptidase N catalytic chain Immunoglobul in heavy variable 3-43D Protein IGKV3D-20
Carboxypeptidase N subunit 2 Immunoglobul in heavy variable 4-38-2 Protein IGLV1-47
CD5 antigen-like Immunoglob ulin kappa constant Protein IGLV2-11
CD81 antig en Immunoglobul in lambda variable 1-51 Protein IGLV2-14
Ceruloplasmin Immunoglobul in lambda variable 3-10 Protein IGLV3-19
Clusterin Immunoglobul in lambda variable 8-61 Protein IGLV3-27
Coagulation factor V Immunoglobul in lambda-like polypept ide 5 Protein IGLV7-43
Coagulation factor XII Integrin alph a-IIb Protein IGLV7-46
Coagulation factor XIII A chain Inter-alpha-trypsin inhibitor heavy chain H1 Protein IGLV9-49
Coagulation factor XIII B chain Inter-alpha-trypsin inhibitor heavy chain H2 Protein S100-A8
Collectin-11 Keratin, type I cuticular Ha1 Protein S100-A9
Complement C1q subcompon ent subunit A Keratin, type I cuticular Ha3-II Proteoglycan 4
Complement C1q subcompon ent subunit B Keratin, type I cuticular Ha5 Proth rombin
Complement C1q subcompon ent subunit C Keratin, type I cuticular Ha6 Ras-related protein Rap-1b
Complement C1q tumor necrosis factor-related protein 3 Keratin, typ e I cyt keletal 10 Reelin
Complement C1r subcomponent Keratin, type I cyt keletal 14 Retinol binding p rotein 4, plasma, isoform CRA_b
Complement C1s subcomponent Keratin, type I cyt keletal 9 Serotransferrin
Complement C3 Keratin, type II cyt keletal 1 Serum albu min
Complement C4 beta chain Keratin, type II cyt keletal 2 epidermal Serum amyloid P-component
Complement C5 Keratin, type II cyt keletal 4 Serum paraoxonase/arylesterase 1
Complement component C6 Keratin, type II cyt keletal 5 Solute carrier family 2, f acilitated gluc e transporter memb er 1
Complement component C7 Keratin, type II cyt keletal 6B Thromb pondin-1
Complement component C8 alp ha chain Kininogen-1 Transferrin receptor protein 1
Complement component C8 beta ch ain Lipopolysaccharide-binding protein Transthyretin
Complement component C8 gamma ch ain Lysozyme C Truncated apolipoprotein C-I
Complement component C9 Mannan-binding l ectin serine protease 1 Vitamin D-binding protein
Complement factor B Mannan-b inding lectin serine p rotease 2 Vitamin K-dependent protein S
Complement factor H Oncoprotein-ind uced transcript 3 protein Vitronectin
Complement factor H-related protein 1 Phosphatidylinositol-g lycan-specific phospholip ase D von Will ebrand factor
Complement factor H-related protein 5 Pigment epithelium-d erived factor Zinc-alpha-2-glycop rotein
Complement factor I light chain Plasma kallikrein h eavy chain TTR
Cortic teroid-binding gl obulin Plasma protease C1 inhibitor VTN
Erythrocyte band 7 int egral membrane protein Plasminogen VWF
Extracellular matrix protein 1 Platelet factor 4
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted November 8, 2020. ; https://doi.org/10.1101/2020.11.08.373530doi: bioRxiv preprint

Supplemental Table 2: List of unique proteins in exosomes isolated from the plasma of TW, HR
and age-matched healthy controls.
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted November 8, 2020. ; https://doi.org/10.1101/2020.11.08.373530doi: bioRxiv preprint

.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted November 8, 2020. ; https://doi.org/10.1101/2020.11.08.373530doi: bioRxiv preprint