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Research Article

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight

Francine E. Garrett-Bakelman, Manjula Darshi https://orcid.org/0000-0002-1373-7488, Stefan J. Green https://orcid.org/0000-0003-2781-359X, Ruben C. Gur, Ling Lin https://orcid.org/0000-0002-3146-8648, Brandon R. Macias https://orcid.org/0000-0003-2527-5089, Miles J. McKenna https://orcid.org/0000-0001-6469-4353, Cem Meydan https://orcid.org/0000-0002-0663-6216, Tejaswini Mishra https://orcid.org/0000-0001-9931-1260, Jad Nasrini https://orcid.org/0000-0003-3769-1186, Brian D. Piening https://orcid.org/0000-0002-2683-8157, Lindsay F. Rizzardi https://orcid.org/0000-0002-2866-9625, Kumar Sharma https://orcid.org/0000-0002-7550-8525, Jamila H. Siamwala https://orcid.org/0000-0001-9482-9183, Lynn Taylor https://orcid.org/0000-0002-2074-7262, Martha Hotz Vitaterna https://orcid.org/0000-0001-6243-2093, Maryam Afkarian https://orcid.org/0000-0001-8428-8264, Ebrahim Afshinnekoo https://orcid.org/0000-0002-2206-1205, Sara Ahadi https://orcid.org/0000-0002-7849-2135, Aditya Ambati https://orcid.org/0000-0001-8462-3552, Maneesh Arya https://orcid.org/0000-0002-2445-7975, Daniela Bezdan https://orcid.org/0000-0002-1203-8239, Colin M. Callahan https://orcid.org/0000-0002-0385-1121, Songjie Chen https://orcid.org/0000-0002-9568-5705, Augustine M. K. Choi, George E. Chlipala https://orcid.org/0000-0003-0203-3191, Kévin Contrepois https://orcid.org/0000-0001-9678-5161, Marisa Covington https://orcid.org/0000-0002-4587-1008, Brian E. Crucian, Immaculata De Vivo https://orcid.org/0000-0002-7185-7402, David F. Dinges https://orcid.org/0000-0003-2151-4492, Douglas J. Ebert https://orcid.org/0000-0001-9489-9229, Jason I. Feinberg, Jorge A. Gandara https://orcid.org/0000-0002-7720-9676, Kerry A. George https://orcid.org/0000-0002-9318-9839, John Goutsias https://orcid.org/0000-0002-0192-3857, George S. Grills https://orcid.org/0000-0003-1037-8616, Alan R. Hargens https://orcid.org/0000-0002-4722-1375, Martina Heer https://orcid.org/0000-0001-5534-911X, Ryan P. Hillary https://orcid.org/0000-0001-7284-4040, Andrew N. Hoofnagle https://orcid.org/0000-0002-6449-0243, Vivian Y. H. Hook, Garrett Jenkinson https://orcid.org/0000-0003-2548-098X, Peng Jiang https://orcid.org/0000-0003-3647-8500, Ali Keshavarzian https://orcid.org/0000-0002-7969-3369, Steven S. Laurie https://orcid.org/0000-0002-8794-3583, Brittany Lee-McMullen https://orcid.org/0000-0002-8905-6021, Sarah B. Lumpkins, Matthew MacKay, Mark G. Maienschein-Cline https://orcid.org/0000-0001-6619-6788, Ari M. Melnick https://orcid.org/0000-0002-8074-2287, Tyler M. Moore https://orcid.org/0000-0002-1384-0151, Kiichi Nakahira, Hemal H. Patel https://orcid.org/0000-0001-6722-9625, Robert Pietrzyk, Varsha Rao https://orcid.org/0000-0002-7348-526X, Rintaro Saito, Denis N. Salins https://orcid.org/0000-0002-3416-3487, Jan M. Schilling https://orcid.org/0000-0002-8038-7473, Dorothy D. Sears https://orcid.org/0000-0002-9260-3540, Caroline K. Sheridan https://orcid.org/0000-0003-4621-7507, Michael B. Stenger, Rakel Tryggvadottir https://orcid.org/0000-0003-0225-9086, Alexander E. Urban https://orcid.org/0000-0001-9772-933X, Tomas Vaisar https://orcid.org/0000-0002-7406-6606, Benjamin Van Espen https://orcid.org/0000-0003-1587-4942, Jing Zhang, Michael G. Ziegler, Sara R. Zwart, John B. Charles [email protected], Craig E. Kundrot https://orcid.org/0000-0002-5445-3844 [email protected], Graham B. I. Scott https://orcid.org/0000-0003-3467-5957 [email protected], Susan M. Bailey https://orcid.org/0000-0001-5595-9364 [email protected], Mathias Basner https://orcid.org/0000-0002-8453-0812 [email protected], Andrew P. Feinberg https://orcid.org/0000-0002-8364-1991 [email protected], Stuart M. C. Lee https://orcid.org/0000-0001-7065-5182 [email protected], Christopher E. Mason https://orcid.org/0000-0002-1850-1642 [email protected], Emmanuel Mignot https://orcid.org/0000-0002-6928-5310 [email protected], Brinda K. Rana https://orcid.org/0000-0002-9856-6728 [email protected], Scott M. Smith [email protected], Michael P. Snyder [email protected], and Fred W. Turek https://orcid.org/0000-0002-4031-4229 [email protected]
Science
12 Apr 2019
Vol 364, Issue 6436

What to expect after a year in space

Space is the final frontier for understanding how extreme environments affect human physiology. Following twin astronauts, one of which spent a year-long mission on the International Space Station, Garrett-Bakelman et al. examined molecular and physiological traits that may be affected by time in space (see the Perspective by Löbrich and Jeggo). Sequencing the components of whole blood revealed that the length of telomeres, which is important to maintain in dividing cells and may be related to human aging, changed substantially during space flight and again upon return to Earth. Coupled with changes in DNA methylation in immune cells and cardiovascular and cognitive effects, this study provides a basis to assess the hazards of long-term space habitation.
Science, this issue p. eaau8650; see also p. 127

Structured Abstract

INTRODUCTION

To date, 559 humans have been flown into space, but long-duration (>300 days) missions are rare (n = 8 total). Long-duration missions that will take humans to Mars and beyond are planned by public and private entities for the 2020s and 2030s; therefore, comprehensive studies are needed now to assess the impact of long-duration spaceflight on the human body, brain, and overall physiology. The space environment is made harsh and challenging by multiple factors, including confinement, isolation, and exposure to environmental stressors such as microgravity, radiation, and noise. The selection of one of a pair of monozygotic (identical) twin astronauts for NASA’s first 1-year mission enabled us to compare the impact of the spaceflight environment on one twin to the simultaneous impact of the Earth environment on a genetically matched subject.

RATIONALE

The known impacts of the spaceflight environment on human health and performance, physiology, and cellular and molecular processes are numerous and include bone density loss, effects on cognitive performance, microbial shifts, and alterations in gene regulation. However, previous studies collected very limited data, did not integrate simultaneous effects on multiple systems and data types in the same subject, or were restricted to 6-month missions. Measurement of the same variables in an astronaut on a year-long mission and in his Earth-bound twin indicated the biological measures that might be used to determine the effects of spaceflight. Presented here is an integrated longitudinal, multidimensional description of the effects of a 340-day mission onboard the International Space Station.

RESULTS

Physiological, telomeric, transcriptomic, epigenetic, proteomic, metabolomic, immune, microbiomic, cardiovascular, vision-related, and cognitive data were collected over 25 months. Some biological functions were not significantly affected by spaceflight, including the immune response (T cell receptor repertoire) to the first test of a vaccination in flight. However, significant changes in multiple data types were observed in association with the spaceflight period; the majority of these eventually returned to a preflight state within the time period of the study. These included changes in telomere length, gene regulation measured in both epigenetic and transcriptional data, gut microbiome composition, body weight, carotid artery dimensions, subfoveal choroidal thickness and peripapillary total retinal thickness, and serum metabolites. In addition, some factors were significantly affected by the stress of returning to Earth, including inflammation cytokines and immune response gene networks, as well as cognitive performance. For a few measures, persistent changes were observed even after 6 months on Earth, including some genes’ expression levels, increased DNA damage from chromosomal inversions, increased numbers of short telomeres, and attenuated cognitive function.

CONCLUSION

Given that the majority of the biological and human health variables remained stable, or returned to baseline, after a 340-day space mission, these data suggest that human health can be mostly sustained over this duration of spaceflight. The persistence of the molecular changes (e.g., gene expression) and the extrapolation of the identified risk factors for longer missions (>1 year) remain estimates and should be demonstrated with these measures in future astronauts. Finally, changes described in this study highlight pathways and mechanisms that may be vulnerable to spaceflight and may require safeguards for longer space missions; thus, they serve as a guide for targeted countermeasures or monitoring during future missions.
Multidimensional, longitudinal assays of the NASA Twins Study.
(Left and middle) Genetically identical twin subjects (ground and flight) were characterized across 10 generalized biomedical modalities before (preflight), during (inflight), and after flight (postflight) for a total of 25 months (circles indicate time points at which data were collected). (Right) Data were integrated to guide biomedical metrics across various “-omes” for future missions (concentric circles indicate, from inner to outer, cytokines, proteome, transcriptome, and methylome).

Abstract

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress–related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

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Supplementary Material

Summary

Materials and Methods
Figs. S1 to S15
Tables S1 to S11
References (85156)

Resources

File (aau8650_garrett-bakelman_sm.pdf)
File (aau8650_table_s1.xlsx)
File (aau8650_table_s2.xlsx)
File (aau8650_table_s3.xlsx)
File (aau8650_table_s4.xlsx)
File (aau8650_table_s5.xlsx)
File (aau8650_table_s6.xlsx)
File (aau8650_table_s8.xlsx)
File (aau8650_table_s9.xlsx)

References and Notes

1
M. Barratt, S. L. Pool, Eds., Principles of Clinical Medicine for Space Flight (Springer, 2008).
2
V. A. Convertino, “Exercise and adaptation to microgravity environments” in Supplement 14: Handbook of Physiology, Environmental Physiology, Comprehensive Physiology (Wiley, 2011), pp. 815–843.
3
W. E. Thornton, T. P. Moore, S. L. Pool, Fluid shifts in weightlessness. Aviat. Space Environ. Med. 58, A86–A90 (1987).
4
C. S. Leach, C. P. Alfrey, W. N. Suki, J. I. Leonard, P. C. Rambaut, L. D. Inners, S. M. Smith, H. W. Lane, J. M. Krauhs, Regulation of body fluid compartments during short-term spaceflight. J. Appl. Physiol. 81, 105–116 (1996).
5
J. V. Meck, C. J. Reyes, S. A. Perez, A. L. Goldberger, M. G. Ziegler, Marked exacerbation of orthostatic intolerance after long- vs. short-duration spaceflight in veteran astronauts. Psychosom. Med. 63, 865–873 (2001).
6
S. M. C. Lee, A. H. Feiveson, S. Stein, M. B. Stenger, S. H. Platts, Orthostatic intolerance after ISS and Space Shuttle missions. Aerosp. Med. Hum. Perform. 86 (suppl. 1), A54–A67 (2015).
7
T. P. Moore, W. E. Thornton, Space shuttle inflight and postflight fluid shifts measured by leg volume changes. Aviat. Space Environ. Med. 58, A91–A96 (1987).
8
M. A. Perhonen, F. Franco, L. D. Lane, J. C. Buckey, C. G. Blomqvist, J. E. Zerwekh, R. M. Peshock, P. T. Weatherall, B. D. Levine, Cardiac atrophy after bed rest and spaceflight. J. Appl. Physiol. 91, 645–653 (2001).
9
H. Akima, Y. Kawakami, K. Kubo, C. Sekiguchi, H. Ohshima, A. Miyamoto, T. Fukunaga, Effect of short-duration spaceflight on thigh and leg muscle volume. Med. Sci. Sports Exerc. 32, 1743–1747 (2000).
10
R. Gopalakrishnan, K. O. Genc, A. J. Rice, S. M. C. Lee, H. J. Evans, C. C. Maender, H. Ilaslan, P. R. Cavanagh, Muscle volume, strength, endurance, and exercise loads during 6-month missions in space. Aviat. Space Environ. Med. 81, 91–104 (2010).
11
S. Trappe, D. Costill, P. Gallagher, A. Creer, J. R. Peters, H. Evans, D. A. Riley, R. H. Fitts, Exercise in space: Human skeletal muscle after 6 months aboard the International Space Station. J. Appl. Physiol. 106, 1159–1168 (2009).
12
J. C. Hayes, M. E. Guilliams, S. M. C. Lee, K. R. MacNeill, A. D. M. Jr, “Exercise: developing countermeasure systems for optimizing astronaut performance in space” in Biomedical Results of the Space Shuttle Program, D. Risin, P. C. Stepaniak, Eds. (NASA/SP-2013-607, NASA, Washington, DC, 2013), pp. 289–314.
13
S. M. Smith, M. A. Heer, L. C. Shackelford, J. D. Sibonga, L. Ploutz-Snyder, S. R. Zwart, Benefits for bone from resistance exercise and nutrition in long-duration spaceflight: Evidence from biochemistry and densitometry. J. Bone Miner. Res. 27, 1896–1906 (2012).
14
T. H. Mader, C. R. Gibson, A. F. Pass, L. A. Kramer, A. G. Lee, J. Fogarty, W. J. Tarver, J. P. Dervay, D. R. Hamilton, A. Sargsyan, J. L. Phillips, D. Tran, W. Lipsky, J. Choi, C. Stern, R. Kuyumjian, J. D. Polk, Optic disc edema, globe flattening, choroidal folds, and hyperopic shifts observed in astronauts after long-duration space flight. Ophthalmology 118, 2058–2069 (2011).
15
L. F. Zhang, A. R. Hargens, Spaceflight-induced intracranial hypertension and visual impairment: Pathophysiology and countermeasures. Physiol. Rev. 98, 59–87 (2018).
16
M. B. Stenger et al., “Evidence report: Risk of spaceflight associated neuro-ocular syndrome (SANS)” (NASA, Lyndon B. Johnson Space Center, Houston, 2018).
17
M. V. Carminati, D. Griffith, M. R. Campbell, Sub-orbital commercial human spaceflight and informed consent. Aviat. Space Environ. Med. 82, 144–146 (2011).
18
K. Contrepois, L. Jiang, M. Snyder, Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol. Cell. Proteomics 14, 1684–1695 (2015).
19
E. C. Borresen, D. G. Brown, G. Harbison, L. Taylor, A. Fairbanks, J. O’Malia, M. Bazan, S. Rao, S. M. Bailey, M. Wdowik, T. L. Weir, R. J. Brown, E. P. Ryan, A randomized controlled trial to increase navy bean or rice bran consumption in colorectal cancer survivors. Nutr. Cancer 68, 1269–1280 (2016).
20
R. M. Cawthon, Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 37, e21 (2009).
21
L. S. Honig, M. S. Kang, R. Cheng, J. H. Eckfeldt, B. Thyagarajan, C. Leiendecker-Foster, M. A. Province, J. L. Sanders, T. Perls, K. Christensen, J. H. Lee, R. Mayeux, N. Schupf, Heritability of telomere length in a study of long-lived families. Neurobiol. Aging 36, 2785–2790 (2015).
22
J. Lin, J. Cheon, R. Brown, M. Coccia, E. Puterman, K. Aschbacher, E. Sinclair, E. Epel, E. H. Blackburn, Systematic and cell type-specific telomere length changes in subsets of lymphocytes. J. Immunol. Res. 2016, 5371050 (2016).
23
P. M. Lansdorp, N. P. Verwoerd, F. M. van de Rijke, V. Dragowska, M. T. Little, R. W. Dirks, A. K. Raap, H. J. Tanke, Heterogeneity in telomere length of human chromosomes. Hum. Mol. Genet. 5, 685–691 (1996).
24
B. J. Sishc, C. B. Nelson, M. J. McKenna, C. L. R. Battaglia, A. Herndon, R. Idate, H. L. Liber, S. M. Bailey, Telomeres and telomerase in the radiation response: Implications for instability, reprograming, and carcinogenesis. Front. Oncol. 5, 257 (2015).
25
C. W. Greider, E. H. Blackburn, Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell 43, 405–413 (1985).
26
M. Hou, D. Xu, M. Björkholm, A. Gruber, Real-time quantitative telomeric repeat amplification protocol assay for the detection of telomerase activity. Clin. Chem. 47, 519–524 (2001).
27
F. A. Cucinotta, Space radiation risks for astronauts on multiple International Space Station missions. PLOS ONE 9, e96099 (2014).
28
K. George, J. Rhone, A. Beitman, F. A. Cucinotta, Cytogenetic damage in the blood lymphocytes of astronauts: Effects of repeat long-duration space missions. Mutat. Res. 756, 165–169 (2013).
29
F. A. Ray, E. Zimmerman, B. Robinson, M. N. Cornforth, J. S. Bedford, E. H. Goodwin, S. M. Bailey, Directional genomic hybridization for chromosomal inversion discovery and detection. Chromosome Res. 21, 165–174 (2013).
30
F. A. Ray, E. Robinson, M. McKenna, M. Hada, K. George, F. Cucinotta, E. H. Goodwin, J. S. Bedford, S. M. Bailey, M. N. Cornforth, Directional genomic hybridization: Inversions as a potential biodosimeter for retrospective radiation exposure. Radiat. Environ. Biophys. 53, 255–263 (2014).
31
M. N. Cornforth, M. Durante, Radiation quality and intra-chromosomal aberrations: Size matters. Mutat. Res. 836 (part A), 28–35 (2018).
32
K. George, L. J. Chappell, F. A. Cucinotta, Persistence of space radiation induced cytogenetic damage in the blood lymphocytes of astronauts. Mutat. Res. 701, 75–79 (2010).
33
G. Jenkinson, E. Pujadas, J. Goutsias, A. P. Feinberg, Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat. Genet. 49, 719–729 (2017).
34
A. Koyanagi, C. Sekine, H. Yagita, Expression of Notch receptors and ligands on immature and mature T cells. Biochem. Biophys. Res. Commun. 418, 799–805 (2012).
35
M. Nakaya, Y. Xiao, X. Zhou, J.-H. Chang, M. Chang, X. Cheng, M. Blonska, X. Lin, S.-C. Sun, Inflammatory T cell responses rely on amino acid transporter ASCT2 facilitation of glutamine uptake and mTORC1 kinase activation. Immunity 40, 692–705 (2014).
36
J. E. Thaventhiran, D. T. Fearon, L. Gattinoni, Transcriptional regulation of effector and memory CD8+ T cell fates. Curr. Opin. Immunol. 25, 321–328 (2013).
37
J. de Batlle, J. Sauleda, E. Balcells, F. P. Gómez, M. Méndez, E. Rodriguez, E. Barreiro, J. J. Ferrer, I. Romieu, J. Gea, J. M. Antó, J. Garcia-Aymerich; PAC-COPD Study Group, Association between Ω3 and Ω6 fatty acid intakes and serum inflammatory markers in COPD. J. Nutr. Biochem. 23, 817–821 (2012).
38
P. C. Calder, Polyunsaturated fatty acids and inflammatory processes: New twists in an old tale. Biochimie 91, 791–795 (2009).
39
Human Microbiome Project Consortium, Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
40
S. Li, S. W. Tighe, C. M. Nicolet, D. Grove, S. Levy, W. Farmerie, A. Viale, C. Wright, P. A. Schweitzer, Y. Gao, D. Kim, J. Boland, B. Hicks, R. Kim, S. Chhangawala, N. Jafari, N. Raghavachari, J. Gandara, N. Garcia-Reyero, C. Hendrickson, D. Roberson, J. A. Rosenfeld, T. Smith, J. G. Underwood, M. Wang, P. Zumbo, D. A. Baldwin, G. S. Grills, C. E. Mason, Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915–925 (2014).
41
SEQC/MAQC-III Consortium, A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).
42
C. M. Gardiner, D. K. Finlay, What fuels natural killers? Metabolism and NK cell responses. Front. Immunol. 8, 367 (2017).
43
A. Beheshti, E. Cekanaviciute, D. J. Smith, S. V. Costes, Global transcriptomic analysis suggests carbon dioxide as an environmental stressor in spaceflight: A systems biology GeneLab case study. Sci. Rep. 8, 4191 (2018).
44
P. Norsk, A. Asmar, M. Damgaard, N. J. Christensen, Fluid shifts, vasodilatation and ambulatory blood pressure reduction during long duration spaceflight. J. Physiol. 593, 573–584 (2015).
45
P. Arbeille, R. Provost, K. Zuj, Carotid and femoral artery intima-media thickness during 6 months of spaceflight. Aerosp. Med. Hum. Perform. 87, 449–453 (2016).
46
S. R. Zwart, R. D. Launius, G. K. Coen, J. L. L. Morgan, J. B. Charles, S. M. Smith, Body mass changes during long-duration spaceflight. Aviat. Space Environ. Med. 85, 897–904 (2014).
47
S. M. Smith, M. Heer, L. C. Shackelford, J. D. Sibonga, J. Spatz, R. A. Pietrzyk, E. K. Hudson, S. R. Zwart, Bone metabolism and renal stone risk during International Space Station missions. Bone 81, 712–720 (2015).
48
S. M. Smith, S. A. Abrams, J. E. Davis-Street, M. Heer, K. O. O’Brien, M. E. Wastney, S. R. Zwart, Fifty years of human space travel: Implications for bone and calcium research. Annu. Rev. Nutr. 34, 377–400 (2014).
49
X. Wang, S. Abraham, J. A. G. McKenzie, N. Jeffs, M. Swire, V. B. Tripathi, U. F. O. Luhmann, C. A. K. Lange, Z. Zhai, H. M. Arthur, J. Bainbridge, S. E. Moss, J. Greenwood, LRG1 promotes angiogenesis by modulating endothelial TGF-β signalling. Nature 499, 306–311 (2013).
50
S. R. Zwart, J. F. Gregory, S. H. Zeisel, C. R. Gibson, T. H. Mader, J. M. Kinchen, P. M. Ueland, R. Ploutz-Snyder, M. A. Heer, S. M. Smith, Genotype, B-vitamin status, and androgens affect spaceflight-induced ophthalmic changes. FASEB J. 30, 141–148 (2016).
51
S. R. Zwart, C. R. Gibson, T. H. Mader, K. Ericson, R. Ploutz-Snyder, M. Heer, S. M. Smith, Vision changes after spaceflight are related to alterations in folate- and vitamin B-12-dependent one-carbon metabolism. J. Nutr. 142, 427–431 (2012).
52
M. Basner, A. Savitt, T. M. Moore, A. M. Port, S. McGuire, A. J. Ecker, J. Nasrini, D. J. Mollicone, C. M. Mott, T. McCann, D. F. Dinges, R. C. Gur, Development and validation of the cognition test battery for spaceflight. Aerosp. Med. Hum. Perform. 86, 942–952 (2015).
53
Y. Zhao, K. Lai, I. Cheung, J. Youds, M. Tarailo, S. Tarailo, A. Rose, A mutational analysis of Caenorhabditis elegans in space. Mutat. Res. 601, 19–29 (2006).
54
M. A. Shammas, Telomeres, lifestyle, cancer, and aging. Curr. Opin. Clin. Nutr. Metab. Care 14, 28–34 (2011).
55
W. R. Pendergrass, P. E. Penn, J. Li, N. S. Wolf, Age-related telomere shortening occurs in lens epithelium from old rats and is slowed by caloric restriction. Exp. Eye Res. 73, 221–228 (2001).
56
M. Laimer, A. Melmer, C. Lamina, J. Raschenberger, P. Adamovski, J. Engl, C. Ress, A. Tschoner, C. Gelsinger, L. Mair, S. Kiechl, J. Willeit, P. Willeit, C. Stettler, H. Tilg, F. Kronenberg, C. Ebenbichler, Telomere length increase after weight loss induced by bariatric surgery: Results from a 10 year prospective study. Int. J. Obes. 40, 773–778 (2016).
57
L. F. Cherkas, J. L. Hunkin, B. S. Kato, J. B. Richards, J. P. Gardner, G. L. Surdulescu, M. Kimura, X. Lu, T. D. Spector, A. Aviv, The association between physical activity in leisure time and leukocyte telomere length. Arch. Intern. Med. 168, 154–158 (2008).
58
L. Carulli, C. Anzivino, E. Baldelli, M. F. Zenobii, M. B. L. Rocchi, M. Bertolotti, Telomere length elongation after weight loss intervention in obese adults. Mol. Genet. Metab. 118, 138–142 (2016).
59
V. Boccardi, G. Paolisso, P. Mecocci, Nutrition and lifestyle in healthy aging: The telomerase challenge. Aging 8, 12–15 (2016).
60
N. C. Arsenis, T. You, E. F. Ogawa, G. M. Tinsley, L. Zuo, Physical activity and telomere length: Impact of aging and potential mechanisms of action. Oncotarget 8, 45008–45019 (2017).
61
S. Turroni, S. Rampelli, E. Biagi, C. Consolandi, M. Severgnini, C. Peano, S. Quercia, M. Soverini, F. G. Carbonero, G. Bianconi, P. Rettberg, F. Canganella, P. Brigidi, M. Candela, Temporal dynamics of the gut microbiota in people sharing a confined environment, a 520-day ground-based space simulation, MARS500. Microbiome 5, 39 (2017).
62
A. V. Mardanov, M. M. Babykin, A. V. Beletsky, A. I. Grigoriev, V. V. Zinchenko, V. V. Kadnikov, M. P. Kirpichnikov, A. M. Mazur, A. V. Nedoluzhko, N. D. Novikova, E. B. Prokhortchouk, N. V. Ravin, K. G. Skryabin, S. V. Shestakov, Metagenomic analysis of the dynamic changes in the gut microbiome of the participants of the MARS-500 experiment, simulating long term space flight. Acta Naturae 5, 116–125 (2013).
63
M. J. Mienaltowski, D. E. Birk, Structure, physiology, and biochemistry of collagens. Adv. Exp. Med. Biol. 802, 5–29 (2014).
64
S. Sasaki, Aquaporin 2: From its discovery to molecular structure and medical implications. Mol. Aspects Med. 33, 535–546 (2012).
65
H. Yamada, D. Chen, H. J. Monstein, R. Håkanson, Effects of fasting on the expression of gastrin, cholecystokinin, and somatostatin genes and of various housekeeping genes in the pancreas and upper digestive tract of rats. Biochem. Biophys. Res. Commun. 231, 835–838 (1997).
66
A. Nguyen Dinh Cat, R. M. Touyz, A new look at the renin-angiotensin system—Focusing on the vascular system. Peptides 32, 2141–2150 (2011).
67
F. S. Michel, G. R. Norton, M. J. Maseko, O. H. I. Majane, P. Sareli, A. J. Woodiwiss, Urinary angiotensinogen excretion is associated with blood pressure independent of the circulating renin-angiotensin system in a group of African ancestry. Hypertension 64, 149–156 (2014).
68
J. Zhang, G. Rane, X. Dai, M. K. Shanmugam, F. Arfuso, R. P. Samy, M. K. P. Lai, D. Kappei, A. P. Kumar, G. Sethi, Ageing and the telomere connection: An intimate relationship with inflammation. Ageing Res. Rev. 25, 55–69 (2016).
69
R. C. Stone, K. Horvath, J. D. Kark, E. Susser, S. A. Tishkoff, A. Aviv, Telomere length and the cancer-atherosclerosis trade-off. PLOS Genet. 12, e1006144 (2016).
70
B. E. Crucian, A. Choukèr, R. J. Simpson, S. Mehta, G. Marshall, S. M. Smith, S. R. Zwart, M. Heer, S. Ponomarev, A. Whitmire, J. P. Frippiat, G. L. Douglas, H. Lorenzi, J.-I. Buchheim, G. Makedonas, G. S. Ginsburg, C. M. Ott, D. L. Pierson, S. S. Krieger, N. Baecker, C. Sams, Immune system dysregulation during spaceflight: Potential countermeasures for deep space exploration missions. Front. Immunol. 9, 1437 (2018).
71
T. H. Mader, C. R. Gibson, A. F. Pass, A. G. Lee, H. E. Killer, H.-C. Hansen, J. P. Dervay, M. R. Barratt, W. J. Tarver, A. E. Sargsyan, L. A. Kramer, R. Riascos, D. G. Bedi, D. R. Pettit, Optic disc edema in an astronaut after repeat long-duration space flight. J. Neuroophthalmol. 33, 249–255 (2013).
72
S. R. Zwart, C. R. Gibson, J. F. Gregory, T. H. Mader, P. J. Stover, S. H. Zeisel, S. M. Smith, Astronaut ophthalmic syndrome. FASEB J. 31, 3746–3756 (2017).
73
P. Arbeille, R. Provost, K. Zuj, N. Vincent, Measurements of jugular, portal, femoral, and calf vein cross-sectional area for the assessment of venous blood redistribution with long duration spaceflight (Vessel Imaging Experiment). Eur. J. Appl. Physiol. 115, 2099–2106 (2015).
74
R. L. Hughson, A. D. Robertson, P. Arbeille, J. K. Shoemaker, J. W. E. Rush, K. S. Fraser, D. K. Greaves, Increased postflight carotid artery stiffness and inflight insulin resistance resulting from 6-mo spaceflight in male and female astronauts. Am. J. Physiol. Heart Circ. Physiol. 310, H628–H638 (2016).
75
M. W. Lorenz, H. S. Markus, M. L. Bots, M. Rosvall, M. Sitzer, Prediction of clinical cardiovascular events with carotid intima-media thickness: A systematic review and meta-analysis. Circulation 115, 459–467 (2007).
76
G. Walldius, I. Jungner, The apoB/apoA-I ratio: A strong, new risk factor for cardiovascular disease and a target for lipid-lowering therapy—a review of the evidence. J. Intern. Med. 259, 493–519 (2006).
77
G. Florvall, S. Basu, A. Larsson, Apolipoprotein A1 is a stronger prognostic marker than are HDL and LDL cholesterol for cardiovascular disease and mortality in elderly men. J. Gerontol. A Biol. Sci. Med. Sci. 61, 1262–1266 (2006).
78
M. Benn, B. G. Nordestgaard, G. B. Jensen, A. Tybjaerg-Hansen, Improving prediction of ischemic cardiovascular disease in the general population using apolipoprotein B: The Copenhagen City Heart Study. Arterioscler. Thromb. Vasc. Biol. 27, 661–670 (2007).
79
C. J. Ade, R. M. Broxterman, J. M. Charvat, T. J. Barstow, Incidence rate of cardiovascular disease end points in the National Aeronautics and Space Administration Astronaut Corps. J. Am. Heart Assoc. 6, e005564 (2017).
80
R. J. Reynolds, S. M. Day, Mortality among U.S. astronauts: 1980–2009. Aviat. Space Environ. Med. 81, 1024–1027 (2010).
81
A. P. Mulavara, B. T. Peters, C. A. Miller, I. S. Kofman, M. F. Reschke, L. C. Taylor, E. L. Lawrence, S. J. Wood, S. S. Laurie, S. M. C. Lee, R. E. Buxton, T. R. May-Phillips, M. B. Stenger, L. L. Ploutz-Snyder, J. W. Ryder, A. H. Feiveson, J. J. Bloomberg, Physiological and functional alterations after spaceflight and bed rest. Med. Sci. Sports Exerc. 50, 1961–1980 (2018).
82
S. Behjati, G. Gundem, D. C. Wedge, N. D. Roberts, P. S. Tarpey, S. L. Cooke, P. Van Loo, L. B. Alexandrov, M. Ramakrishna, H. Davies, S. Nik-Zainal, C. Hardy, C. Latimer, K. M. Raine, L. Stebbings, A. Menzies, D. Jones, R. Shepherd, A. P. Butler, J. W. Teague, M. Jorgensen, B. Khatri, N. Pillay, A. Shlien, P. A. Futreal, C. Badie, U. McDermott, G. S. Bova, A. L. Richardson, A. M. Flanagan, M. R. Stratton, P. J. Campbell; ICGC Prostate Group, Mutational signatures of ionizing radiation in second malignancies. Nat. Commun. 7, 12605 (2016).
83
F. Berardinelli, A. Antoccia, R. Buonsante, S. Gerardi, R. Cherubini, V. De Nadal, C. Tanzarella, A. Sgura, The role of telomere length modulation in delayed chromosome instability induced by ionizing radiation in human primary fibroblasts. Environ. Mol. Mutagen. 54, 172–179 (2013).
84
F. A. Cucinotta, N. Hamada, M. P. Little, No evidence for an increase in circulatory disease mortality in astronauts following space radiation exposures. Life Sci. Space Res. 10, 53–56 (2016).
85
J. T. Leek, W. E. Johnson, H. S. Parker, A. E. Jaffe, J. D. Storey, The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
86
ACK lysis buffer. Cold Spring Harb. Protoc. 2014, pdb.rec083295 (2014).
87
E. S. Williams, M. N. Cornforth, E. H. Goodwin, S. M. Bailey, CO-FISH, COD-FISH, ReD-FISH, SKY-FISH. Methods Mol. Biol. 735, 113–124 (2011).
88
K. J. Livak, T. D. Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCt method. Methods 25, 402–408 (2001).
89
H. P. Wong, P. Slijepcevic, Telomere length measurement in mouse chromosomes by a modified Q-FISH method. Cytogenet. Genome Res. 105, 464–470 (2004).
90
F. Krueger, S. R. Andrews, Bismark: A flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).
91
K. D. Hansen, B. Langmead, R. A. Irizarry, BSmooth: From whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).
92
G. Jenkinson, J. Abante, A. P. Feinberg, J. Goutsias, An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data. BMC Bioinformatics 19, 87 (2018).
93
R. Breitling, P. Armengaud, A. Amtmann, P. Herzyk, Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004).
94
E. Eden, R. Navon, I. Steinfeld, D. Lipson, Z. Yakhini, GOrilla: A tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).
95
E. Eden, D. Lipson, S. Yogev, Z. Yakhini, Discovering motifs in ranked lists of DNA sequences. PLOS Comput. Biol. 3, e39 (2007).
96
P. Ewels, M. Magnusson, S. Lundin, M. Käller, MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
97
S. Andrews, FastQC: A quality control tool for high throughput sequence data (2010); www.bioinformatics.babraham.ac.uk/projects/fastqc/.
99
M. Martin, Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
100
J. Harrow, A. Frankish, J. M. Gonzalez, E. Tapanari, M. Diekhans, F. Kokocinski, B. L. Aken, D. Barrell, A. Zadissa, S. Searle, I. Barnes, A. Bignell, V. Boychenko, T. Hunt, M. Kay, G. Mukherjee, J. Rajan, G. Despacio-Reyes, G. Saunders, C. Steward, R. Harte, M. Lin, C. Howald, A. Tanzer, T. Derrien, J. Chrast, N. Walters, S. Balasubramanian, B. Pei, M. Tress, J. M. Rodriguez, I. Ezkurdia, J. van Baren, M. Brent, D. Haussler, M. Kellis, A. Valencia, A. Reymond, M. Gerstein, R. Guigó, T. J. Hubbard, GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
101
A. Dobin, C. A. Davis, F. Schlesinger, J. Drenkow, C. Zaleski, S. Jha, P. Batut, M. Chaisson, T. R. Gingeras, STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
102
D. R. Zerbino, P. Achuthan, W. Akanni, M. R. Amode, D. Barrell, J. Bhai, K. Billis, C. Cummins, A. Gall, C. G. Girón, L. Gil, L. Gordon, L. Haggerty, E. Haskell, T. Hourlier, O. G. Izuogu, S. H. Janacek, T. Juettemann, J. K. To, M. R. Laird, I. Lavidas, Z. Liu, J. E. Loveland, T. Maurel, W. McLaren, B. Moore, J. Mudge, D. N. Murphy, V. Newman, M. Nuhn, D. Ogeh, C. K. Ong, A. Parker, M. Patricio, H. S. Riat, H. Schuilenburg, D. Sheppard, H. Sparrow, K. Taylor, A. Thormann, A. Vullo, B. Walts, A. Zadissa, A. Frankish, S. E. Hunt, M. Kostadima, N. Langridge, F. J. Martin, M. Muffato, E. Perry, M. Ruffier, D. M. Staines, S. J. Trevanion, B. L. Aken, F. Cunningham, A. Yates, P. Flicek, Ensembl 2018. Nucleic Acids Res. 46 (D1), D754–D761 (2018).
103
Y. Liao, G. K. Smyth, W. Shi, featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
104
N. L. Bray, H. Pimentel, P. Melsted, L. Pachter, Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
105
M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
106
A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, J. P. Mesirov, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550 (2005).
107
C. L. Smith, J. A. Blake, J. A. Kadin, J. E. Richardson, C. J. Bult; Mouse Genome Database Group, Mouse Genome Database (MGD)-2018: Knowledgebase for the laboratory mouse. Nucleic Acids Res. 46 (D1), D836–D842 (2018).
108
A. Liberzon, A. Subramanian, R. Pinchback, H. Thorvaldsdóttir, P. Tamayo, J. P. Mesirov, Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
109
A. Sergushichev, An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv 060012 [Preprint]. 20 June 2016. .
110
M. D. Robinson, A. Oshlack, A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
111
J. T. Leek, W. E. Johnson, H. S. Parker, E. J. Fertig, A. E. Jaffe, J. D. Storey, sva: Surrogate Variable Analysis, R Package version 3.20.0 (2016).
112
W. E. Johnson, C. Li, A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
113
V. Nygaard, E. A. Rødland, E. Hovig, Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17, 29–39 (2016).
114
L. V. D. Maaten, G. Hinton, Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
115
C. Wang, C. M. Sanders, Q. Yang, H. W. Schroeder Jr.., E. Wang, F. Babrzadeh, B. Gharizadeh, R. M. Myers, J. R. Hudson Jr.., R. W. Davis, J. Han, High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets. Proc. Natl. Acad. Sci. U.S.A. 107, 1518–1523 (2010).
116
T. Magoč, S. L. Salzberg, FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
117
S. F. Altschul, W. Gish, W. Miller, E. W. Myers, D. J. Lipman, Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
118
B. MacLean, D. M. Tomazela, N. Shulman, M. Chambers, G. L. Finney, B. Frewen, R. Kern, D. L. Tabb, D. C. Liebler, M. J. MacCoss, Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
119
Y. S. Ting, J. D. Egertson, J. G. Bollinger, B. C. Searle, S. H. Payne, W. S. Noble, M. J. MacCoss, PECAN: Library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat. Methods 14, 903–908 (2017).
120
B. C. Searle, L. K. Pino, J. D. Egertson, Y. S. Ting, R. T. Lawrence, B. X. MacLean, J. Villén, M. J. MacCoss, Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat. Commun. 9, 5128 (2018).
121
G. Teo, S. Kim, C.-C. Tsou, B. Collins, A.-C. Gingras, A. I. Nesvizhskii, H. Choi, mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J. Proteomics 129, 108–120 (2015).
122
D. W. Huang, B. T. Sherman, Q. Tan, J. R. Collins, W. G. Alvord, J. Roayaei, R. Stephens, M. W. Baseler, H. C. Lane, R. A. Lempicki, The DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).
123
B. D. Piening, W. Zhou, K. Contrepois, H. Röst, G. J. Gu Urban, T. Mishra, B. M. Hanson, E. J. Bautista, S. Leopold, C. Y. Yeh, D. Spakowicz, I. Banerjee, C. Chen, K. Kukurba, D. Perelman, C. Craig, E. Colbert, D. Salins, S. Rego, S. Lee, C. Zhang, J. Wheeler, M. R. Sailani, L. Liang, C. Abbott, M. Gerstein, A. Mardinoglu, U. Smith, D. L. Rubin, S. Pitteri, E. Sodergren, T. L. McLaughlin, G. M. Weinstock, M. P. Snyder, Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 6, 157–170.e8 (2018).
124
K. Nakahira, S.-Y. Kyung, A. J. Rogers, L. Gazourian, S. Youn, A. F. Massaro, C. Quintana, J. C. Osorio, Z. Wang, Y. Zhao, L. A. Lawler, J. D. Christie, N. J. Meyer, F. R. Mc Causland, S. S. Waikar, A. B. Waxman, R. T. Chung, R. Bueno, I. O. Rosas, L. E. Fredenburgh, R. M. Baron, D. C. Christiani, G. M. Hunninghake, A. M. K. Choi, Circulating mitochondrial DNA in patients in the ICU as a marker of mortality: Derivation and validation. PLOS Med. 10, e1001577, discussion e1001577 (2013).
125
S. M. Smith, M. Heer, Z. Wang, C. L. Huntoon, S. R. Zwart, Long-duration space flight and bed rest effects on testosterone and other steroids. J. Clin. Endocrinol. Metab. 97, 270–278 (2012).
126
B. E. Crucian, S. R. Zwart, S. Mehta, P. Uchakin, H. D. Quiriarte, D. Pierson, C. F. Sams, S. M. Smith, Plasma cytokine concentrations indicate that in vivo hormonal regulation of immunity is altered during long-duration spaceflight. J. Interferon Cytokine Res. 34, 778–786 (2014).
127
B. R. Soller, M. Cabrera, S. M. Smith, J. P. Sutton, Smart medical systems with application to nutrition and fitness in space. Nutrition 18, 930–936 (2002).
128
S. M. Smith, J. E. Davis-Street, B. L. Rice, J. L. Nillen, P. L. Gillman, G. Block, Nutritional status assessment in semiclosed environments: Ground-based and space flight studies in humans. J. Nutr. 131, 2053–2061 (2001).
129
S. S. Laurie, G. Vizzeri, G. Taibbi, C. R. Ferguson, X. Hu, S. M. C. Lee, R. Ploutz-Snyder, S. M. Smith, S. R. Zwart, M. B. Stenger, Effects of short-term mild hypercapnia during head-down tilt on intracranial pressure and ocular structures in healthy human subjects. Physiol. Rep. 5, e13302 (2017).
130
T. M. Moore, S. P. Reise, R. E. Gur, H. Hakonarson, R. C. Gur, Psychometric properties of the Penn Computerized Neurocognitive Battery. Neuropsychology 29, 235–246 (2015).
131
R. C. Gur, J. Richard, P. Hughett, M. E. Calkins, L. Macy, W. B. Bilker, C. Brensinger, R. E. Gur, A cognitive neuroscience-based computerized battery for efficient measurement of individual differences: Standardization and initial construct validation. J. Neurosci. Methods 187, 254–262 (2010).
132
R. C. Gur, J. Richard, M. E. Calkins, R. Chiavacci, J. A. Hansen, W. B. Bilker, J. Loughead, J. J. Connolly, H. Qiu, F. D. Mentch, P. M. Abou-Sleiman, H. Hakonarson, R. E. Gur, Age group and sex differences in performance on a computerized neurocognitive battery in children age 8–21. Neuropsychology 26, 251–265 (2012).
133
N. Usui, T. Haji, M. Maruyama, N. Katsuyama, S. Uchida, A. Hozawa, K. Omori, I. Tsuji, R. Kawashima, M. Taira, Cortical areas related to performance of WAIS Digit Symbol Test: A functional imaging study. Neurosci. Lett. 463, 1–5 (2009).
134
J. Lim, D. F. Dinges, Sleep deprivation and vigilant attention. Ann. N. Y. Acad. Sci. 1129, 305–322 (2008).
135
D. R. Roalf, K. Ruparel, R. E. Gur, W. Bilker, R. Gerraty, M. A. Elliott, R. S. Gallagher, L. Almasy, M. F. Pogue-Geile, K. Prasad, J. Wood, V. L. Nimgaonkar, R. C. Gur, Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery. Neuropsychology 28, 161–176 (2014).
136
T. M. Moore, M. Basner, J. Nasrini, E. Hermosillo, S. Kabadi, D. R. Roalf, S. McGuire, A. J. Ecker, K. Ruparel, A. M. Port, C. T. Jackson, D. F. Dinges, R. C. Gur, Validation of the Cognition Test Battery for spaceflight in a sample of highly educated adults. Aerosp. Med. Hum. Perform. 88, 937–946 (2017).
137
R. C. Gur, J. D. Ragland, P. J. Moberg, T. H. Turner, W. B. Bilker, C. Kohler, S. J. Siegel, R. E. Gur, Computerized neurocognitive scanning: I. Methodology and validation in healthy people. Neuropsychopharmacology 25, 766–776 (2001).
138
D. C. Glahn, R. C. Gur, J. D. Ragland, D. M. Censits, R. E. Gur, Reliability, performance characteristics, construct validity, and an initial clinical application of a visual object learning test (VOLT). Neuropsychology 11, 602–612 (1997).
139
J. D. Ragland, B. I. Turetsky, R. C. Gur, F. Gunning-Dixon, T. Turner, L. Schroeder, R. Chan, R. E. Gur, Working memory for complex figures: An fMRI comparison of letter and fractal n-back tasks. Neuropsychology 16, 370–379 (2002).
140
D. C. Glahn, T. D. Cannon, R. E. Gur, J. D. Ragland, R. C. Gur, Working memory constrains abstraction in schizophrenia. Biol. Psychiatry 47, 34–42 (2000).
141
A. L. Benton, N. R. Varney, K. D. Hamsher, Visuospatial judgment. A clinical test. Arch. Neurol. 35, 364–367 (1978).
142
J. C. Raven, Advanced Progressive Matrices Sets I and II (Lewis, London, 1965).
143
B. Perfetti, A. Saggino, A. Ferretti, M. Caulo, G. L. Romani, M. Onofrj, Differential patterns of cortical activation as a function of fluid reasoning complexity. Hum. Brain Mapp. 30, 497–510 (2009).
144
C. W. Lejuez, J. P. Read, C. W. Kahler, J. B. Richards, S. E. Ramsey, G. L. Stuart, D. R. Strong, R. A. Brown, Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). J. Exp. Psychol. Appl. 8, 75–84 (2002).
145
M. Basner, D. Mollicone, D. F. Dinges, Validity and sensitivity of a brief Psychomotor Vigilance Test (PVT-B) to total and partial sleep deprivation. Acta Astronaut. 69, 949–959 (2011).
146
L. K. Barger, E. E. Flynn-Evans, A. Kubey, L. Walsh, J. M. Ronda, W. Wang, K. P. Wright Jr.., C. A. Czeisler, Prevalence of sleep deficiency and use of hypnotic drugs in astronauts before, during, and after spaceflight: An observational study. Lancet Neurol. 13, 904–912 (2014).
147
M. Basner, E. Hermosillo, J. Nasrini, S. McGuire, S. Saxena, T. M. Moore, R. C. Gur, D. F. Dinges, Repeated administration effects on Psychomotor Vigilance Test performance. Sleep 41, 1–6 (2018).
148
D. F. Dinges, An overview of sleepiness and accidents. J. Sleep Res. 4 (S2), 4–14 (1995).
149
B. Buchfink, C. Xie, D. H. Huson, Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
150
D. H. Huson, A. F. Auch, J. Qi, S. C. Schuster, MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).
151
G. G. Silva, K. T. Green, B. E. Dutilh, R. A. Edwards, SUPER-FOCUS: A tool for agile functional analysis of shotgun metagenomic data. Bioinformatics 32, 354–361 (2016).
152
M. D. Robinson, D. J. McCarthy, G. K. Smyth, edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
153
D. J. McCarthy, Y. Chen, G. K. Smyth, Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
154
Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
155
M. E. Futschik, B. Carlisle, Noise-robust soft clustering of gene expression time-course data. J. Bioinform. Comput. Biol. 3, 965–988 (2005).
156
A. Kamburov, R. Cavill, T. M. Ebbels, R. Herwig, H. C. Keun, Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27, 2917–2918 (2011).

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Science
Volume 364 | Issue 6436
12 April 2019

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Received: 24 July 2018
Accepted: 28 February 2019
Published in print: 12 April 2019

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Acknowledgments

The National Space Biomedical Research Institute partnered with NASA to support this study during the solicitation, design, implementation, and analysis phases via the provision of scientific expertise. We thank S. Horner and N. Gokhale from Duke University Medical Center (Durham, NC, USA) for mtRNA validation assistance, I. Tulchinsky, and the Genomics, Epigenomics, and Applied Bioinformatics Core Facilities at Weill Cornell Medicine for sequencing and data services. We thank the NASA JSC Nutritional Biochemistry Lab personnel, especially Y. Bourbeau, who led efforts on this project for project coordination, sample collection, processing, and analysis; the Cardiovascular and Vision Laboratory personnel for the collection and analysis of vascular, ocular, and fluid shifts data; the NASA Human Research Program’s International Space Station Medical Project Team for their invaluable work to coordinate all of sample and data collection scheduling activities on the ground and during flight; D. Mollicone and C. Mott from Pulsar Informatics Inc., E. Hermosillo, and S. McGuire for support of the Cognition measures; Y.-R. Hasson from the Stanford Human Immune Monitoring Center for advice and support on cytokine profiling data; K. Bettinger from Stanford University for support with the Stanford Twins data repository; P. R. Kiela, D. Laubitz (University of Arizona), and E. Song (Northwestern University) for support of the Microbiome project sample collection; K. Kunstman (University of Illinois at Chicago) for shotgun metagenome library preparation and sequencing; S. Mehta (University of Illinois at Chicago) for help in statistical analysis; and J. Kim for laboratory support in targeted metabolomics. We thank the DRC Quantitative and Functional Proteomics Core and the UW Nutrition and Obesity Research Center for performing proteomics assays and New England Biolabs (NEB) for support. We thank J. X.-J. Yuan at the University of Arizona, Tuscon, for providing his support and laboratory for the Tucson sample collections. We thank J. Krauhs for her editorial assistance with the one-page summary. K.S. is also affiliated with the South Texas U.S. Department of Veterans Affairs as a staff physician. Funding: The study was supported by NASA: NNX14AH51G [all Twins Study principal investigators (PIs)]; NNX14AB02G (S.M.B.); NNX14AH27G/NCC 9-58 (M.B.); NN13AJ12G (A.R.H.); NNX14AN75G (S.M.C.L.); NNX17AB26G, NNX17AB26G, and TRISH: NNX16AO69A:0107 and NNX16AO69A:0061 (C.E.M.); NNX14AH52G (M.P.S.); and NNX14AH26G (F.W.T.). Additional support was provided by NIH grants AG035031, NIH/NIDDK P30 DK017047, and P30 DK035816 (K.S.); NSF grant CCF-1656201 (J.G.); and DLR space program grant 50WB1535 (M.H.), as well as the Bert L. and N. Kuggie Vallee Foundation, the WorldQuant Foundation, The Pershing Square Sohn Cancer Research Alliance, and the Bill and Melinda Gates Foundation (OPP1151054) for funding (C.E.M.). Author contributions: F.E.G.-B., M.D., S.J.G., R.C.G., L.L., B.R.M., M.J.M., C.M., T.M., J.N., B.D.P., L.F.R., K.S., J.H.S., L.T., and M.H.V. led the investigator teams across the study. Conceptualization: G.B.I.S., C.E.K., and J.B.C.; S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.B. (Twins Study PI; Cognition); S.M.C.L. [Twins Study PI, Cardiovascular and Oxidative Stress (Cardio Ox); Twins Study co-investigator (Co-I), Fluid Shifts and Ocular]; S.M.S. and S.R.Z. (Biochem Profile); M.H. (Biochem Profile); A.P.F. (Twins Study PI; Epigenetics); M.B.S. (Fluid Shifts NASA site PI and Cardio Ox Co-I); B.R.M. (Fluids Shifts, Co-I); D.J.E. (Fluid Shifts, Co-I); B.K.R. (PI, Fluids Shifts and Co-I, Cardio Ox); I.D.V. (Co-I, Cardio Ox); D.D.S. (Co-I, Cardio Ox); K.S. (Co-I, Targeted Metabolomics, Cardio Ox); A.R.H. (Co-I, Fluid Shifts and Cardio Ox); V.Y.H.H. (Co-I, Fluid Shifts Proteomics); H.H.P., J.H.S., and J.M.S. (Fluid Shifts; Mitochondrial Function); T.V., A.N.H., and M.Af. (Targeted Urine Proteomics); M.P.S. (Twins Study PI; Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); B.D.P. (Integrative Analysis); S.J.G., A.K., F.W.T., and M.H.V. (Microbiome); C.E.M. (Twins study PI; Transcriptome, and Integration); and D.D.S. (Co-I, Cardio Ox). Data Curation: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.B. (Twins Study PI; Cognition); J.I.F. and L.F.R. (Epigenetics); S.R.Z. (Biochem Profile); R.P.H. (Immune Response); S.M.C.L. (Twins Study PI, Cardiovascular; Twins Study Co-I, Fluid Shifts and Ocular); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); D.J.E. (Fluid Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); I.D.V. (Co-I, Cardio Ox); K.S. (Co-I, Cardio Ox); M.D. and R.S. (Targeted Metabolomics); J.H.S. and J.M.S. (Mitochondrial Function); D.N.S. (Integrative Analysis); G.E.C., S.J.G., P.J., M.G.M.-C., and M.H.V. (Microbiome); A.N.H. and T.V. (Urine Proteomics); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); S.A. (Untargeted Plasma Proteomics); K.C. (Untargeted Plasma Metabolomics); C.M. and C.E.M. (Twins study PI); E.A., D.B., F.E.G.-B., and M.M. (Transcriptome); B.D.P. (Integrative Analysis); and D.N.S. (Integrative Analysis). Formal Analysis: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics), M.J.M. (Telomeres and Cytogenetics; FISH), L.T. (Telomeres and Telomerase; qRT-PCR); M.B. (Twins Study PI; Cognition); J.N. (Cognition); A.P.F., L.F.R., G.J., and J.G. (Epigenetics); R.P.H. (Immune Response); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); S.M.S. and S.R.Z. (Biochem Profile); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); M.G.Z. (Cardio Ox); I.D.V. (Co-I, Cardio Ox); D.D.S. (Co-I, Cardio Ox); K.S., M.D., and R.S. (Targeted Metabolomics); H.H.P., J.H.S., and J.M.S. (Mitochondrial Functional Assays); T.V. and A.N.H. (Urine Proteomics); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); S.A. (Untargeted Plasma Proteomics); K.C. (Untargeted Plasma Metabolomics); B.D.P. (Integrative Analysis); R.S. and M.D. (Metabolomics); P.J. (Microbiome); E.M., L.L., and A.A. (Vaccination study); and C.M., C.E.M., and A.M. (Transcriptome, Integrative Analysis). Funding and Acquisition: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.B. (Twins Study PI; Cognition); D.F.D. (Cognition); R.C.G. (Cognition); S.M.S. and S.R.Z. (Biochem Profile); M.H. (Biochem Profile); A.P.F. (Twins Study PI, Epigenetics); J.G. (Epigenetics); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); M.B.S. (Fluid Shifts and Ocular NASA Site PI; Cardio Ox Co-I); B.R.M. (Fluids Shifts and Ocular; CardioOX, Co-I); D.J.E. (Fluid Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; UCSD Site PI, Cardio Ox); I.D.V. (Co-I, Cardio Ox); A.R.H. (Co-I, Fluid Shifts and Ocular; Cardio Ox); M.P.S. (Twins Study PI, Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); V.Y.H.H. (Co-I, Fluid Shifts); F.W.T. (PI), M.H.V. (Co-I) (Microbiome), C.E.M. (PI, Transcriptome), C.M., F.E.G.-B., and A.M.M. (Co-I, Transcriptome), G.S.G. (Co-I, Transcriptome); D.D.S. (Co-I, Cardio Ox); and B.R.M. (Co-I, Fluid Shifts and Ocular; Cardio Ox). Investigation: M.J.M. (Telomeres and Cytogenetics; FISH), L.T. (Telomeres and Telomerase; qRT-PCR); M.B. (Twins Study PI; Cognition); J.N. (Cognition); R.C.G. (Cognition); S.M.S. and S.R.Z. (Biochem Profile); L.F.R., R.T., C.M.C., and J.I.F. (Epigenetics); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); M.B.S. (Fluid Shifts and Ocular; Cardio Ox, Co-I); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); D.J.E. (Fluid Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); M.G.Z. (Cardio Ox); I.D.V. (Co-I, Cardio Ox); K.S., M.D., B.V.E. (Targeted Metabolomics); M.D. (Cardio Ox); B.V.E. (Cardio Ox); A.R.H. (Co-I, Fluid Shifts and Ocular; Cardio Ox); H.H.P., J.H.S., and J.M.S. (Mitochondrial Function Assays); Fluid Shifts); T.V. and A.N.H. (Urine Proteomics); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); B.D.P. (Integrative Analysis); S.A. (Untargeted Plasma Proteomics); V.R. (Plasma Cytokine Profiling); S.J.G. and P.J. (Microbiome); K.C., B.L.M., and S.C. (Untargeted Plasma Metabolomics); E.M., L.L., and A.A. (Vaccination study); and C.E.M., G.S.G., D.B., F.E.G.-B., and A.M.M. (Transcriptome, Integrative Analysis). Methodology: M.J.M. (Telomeres and Cytogenetics; FISH), L.T. (Telomeres and Telomerase; qRT-PCR); M.B. (Twins Study PI; Cognition); J.N. (Cognition); R.C.G.(Cognition); T.M.M. (Cognition); G.J., J.G., and J.I.F. (Epigenetics); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); M.B.S. (NASA Site PI, Fluid Shifts and Ocular; Cardio Ox, Co-I); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular; Cardio Ox, Co-I); D.J.E. (Fluid Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); M.G.Z. (Cardio Ox); I.D.V. and D.D.S. (Co-I, Cardio Ox); A.R.H. (Co-I, Fluid Shifts and Ocular; Cardio Ox, Co-I); H.H.P., J.H.S., and J.S. (Mitochondrial Function Assays); T.V., A.N.H., and M.Af. (Urine Proteomics); V.Y.H.H. (Proteomics); S.J.G., A.K., F.W.T., and M.H.V. (Microbiome); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); S.A. (Untargeted Plasma Proteomics); V.R. (Plasma Cytokine Profiling); K.C. (Untargeted Plasma Metabolomics); B.D.P. (Integrative Analysis); E.M., L.L., and A.A. (Vaccination study); C.M. (Transcriptome, Integrative Analysis); K.S. and M.D. (Targeted Metabolomics); S.M.S., S.R.Z., M.H., and B.E.C. (Biochem Profile blood and urine collection, processing, and analysis); S.S., S.Z., K.S., M.D., and D.D.S. (Urine collection and processing); A.M.K.C. and K.N. (Cell-free mtDNA); and L.L., A.F., J.I.F., C.K.S., and F.E.G.-B. (Blood Collection and Processing). Project administration: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.B. (Twins Study PI; Cognition); L.F.R. (Epigenetics); R.P. (subject consent, scheduling, and sample logistics); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); S.M.S. (Twins Study PI, Biochem Profile); M.B.S. (Fluid Shifts and Ocular, Co-I; Cardio Ox, Co-I); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); M.P.S. (Twins study PI; Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); B.D.P. (Integrative Analysis); F.W.T. (Microbiome PI); M.H.V. (Microbiome Co-I); C.E.M. and F.E.G.-B. (Blood Collection and Processing and transcriptome studies); K.S. (Targeted Metabolomics); M.Ar.; S.L.; and M.C. Resources: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics), K.G. (Telomeres; sample coordination, NASA IRB); M.B. (Twins Study PI; Cognition); S.M.S. (Biochem Profile); M.H. (Biochem Profile); B.E.C. (Biochem Profile); R.P. (laboratory facilities, materials, and instrumentation); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); M.B.S. (Fluid Shifts and Ocular, Co-I; Cardio Ox, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); I.D.V. (Co-I, Cardio Ox); D.D.S. (Co-I, Cardio Ox); K.S. (Co-I, Cardio Ox); A.R.H. (Co-I, Fluid Shifts and Ocular; Cardio Ox); T.V. (Fluid Shifts and Ocular); A.N.H. (Fluid Shifts and Ocular); G.E.C., S.J.G., P.J., and M.G.M.-C. (Microbiome); M.P.S. (Twins study PI; Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); and D.S. (Data Storage Repository). Software: C.E.M. (Transcriptome); D.S. (Data Storage Repository); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); and B.D.P. (Integrative Analysis). Supervision: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.B. (Twins Study PI; Cognition); S.M.S. (Twins Study PI, Biochem Profile); A.P.F. (Twins Study PI); J.G. (Epigenetics); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); M.B.S. (Fluid Shifts and Ocular, Co-I; Cardio Ox, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); I.D.V. (Co-I, Cardio Ox); D.D.S. (Co-I, Cardio Ox); K.S. (Co-I, Targeted Metabolomics); H.H.P. (Fluid Shifts and Ocular); J.M.S. (Fluid Shifts and Ocular); M.P.S. (Twins study PI; Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); S.J.G., A.K., F.W.T., and M.H.V. (Microbiome); K.C. (Untargeted Plasma Metabolomics); and C.E.M. and F.E.G.-B. (Blood Collection and Processing and transcriptome studies). Validation: M.J.M. (Telomeres and Cytogenetics; FISH), L.T. (Telomeres and Telomerase; qRT-PCR); C.E.M., A.C., K.N., N.S.G., and S.M.H. (mtDNA and mtRNA PCR); M.B. (Twins Study PI; Cognition); J.N. (Cognition); T.M.M. (Cognition); S.M.C.L. (Twins Study PI, Cardio Ox; Co-I, Fluid Shifts and Ocular), S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); D.J.E. (Fluid Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); S.M.S. and S.R.Z. (Biochem Profile); I.D.V. (Co-I, Cardio Ox PCR Based Telomere Length); K.S., M.D., and B.V.E. (Targeted Metabolomics); H.H.P. and J.M.S. (Proteomics); and L.L., F.E.G.-B., T.M., and C.M. (Ambient return controls). Visualization: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); M.J.M. (Telomeres and Cytogenetics; FISH), L.T. (Telomeres and Telomerase; qRT-PCR); M.B. (Twins Study PI; Cognition); J.N. (Cognition); L.F.R. (Epigenetics); S.R.Z. (Biochem Profile); S.M.C.L. (Twins Study PI, Cardio Ox; Twins Study Co-I, Fluid Shifts and Ocular); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); H.H.P. (Fluid Shifts and Ocular); S.J.G., P.J., and M.G.M.-C. (Microbiome); M.M., C.E.M., and C.M. (Transcriptome, Integrative Analysis); and T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis). Writing, original draft: S.M.B. (Twins Study PI; Telomeres, Telomerase, DNA damage/cytogenetics); K.G. (Telomeres); M.B. (Twins Study PI; Cognition); L.F.R. (Epigenetics); S.M.C.L. (Twins Study PI, Cardiovascular; Twins Study Co-I, Fluid Shifts and Ocular); S.M.S. (Twins Study PI, Biochem Profile); S.R.Z. (Biochem Profile); S.S.L. (Fluids Shifts and Ocular, Co-I); B.R.M. (Fluids Shifts and Ocular, Co-I); D.J.E. (Fluid Shifts, Co-I); B.K.R. (PI, Fluids Shifts and Ocular; Co-I, Cardio Ox); M.G.Z. (Physiology and Proteomics); M.D. (Cardio Ox); T.M. (Untargeted Plasma Metabolomics and Proteomics, Plasma Cytokine Profiling, Integrative Analysis); S.J.G., P.J., A.K., F.W.T., and M.H.V. (Microbiome); H.H.P., J.M.S., and J.H.S. (Mitochondrial Function); C.E.M., C.M., F.E.G.-B., and M.M. (Twins Study PI, Transcriptome, Telomere Validation, Integration); D.D.S., K.S., and M.D. (Metabolomics); and A.H., T.V., and M.Af. (Urine Proteomics). Editing and manuscript finalization: F.E.G.-B., L.F.R., T.M., C.M., and C.E.M. Writing, review and editing: All authors reviewed and edited the manuscript. Competing interests: S.M.B. is a cofounder and scientific advisory board member of KromaTiD, Inc. C.E.M. is a cofounder and board member for Biotia, Inc., and Onegevity Health, Inc., as well as an advisor for Abbvie, Inc.; ArcBio; Daiichi Sankyo; DNA Genotek; Karius, Inc.; and Whole Biome, Inc. M.P.S. is a cofounder and scientific advisory board member of Personalis, SensOmics, Qbio, January, and Fitricine, and a scientific advisory board member of Genapsys, Epinomics, Jungla, and Jupyter. A.M.K.C. is a cofounder and stock holder and serves on the Scientific Advisory Board for Proterris, which develops therapeutic uses for carbon monoxide. A.M.K.C. also has a use patent on carbon monoxide. A.M.K.C. served as a consultant for TEVA Pharmaceuticals in July 2018. A.M.M. is a consultant for Janssen. Data and materials availability: The NASA Life Sciences Data Archive (LSDA) is the repository for all human and animal research data, including that associated with this study. LSDA has a public facing portal where data requests can be initiated (https://lsda.jsc.nasa.gov/Request/dataRequestFAQ). The LSDA team provides the appropriate processes, tools, and secure infrastructure for archival of experimental data and dissemination while complying with applicable rules, regulations, policies, and procedures governing the management and archival of sensitive data and information. The LSDA team enables data and information dissemination to the public or to authorized personnel either by providing public access to information or via an approved request process for information and data from the LSDA in accordance with NASA Human Research Program and JSC Institutional Review Board direction.

Authors

Affiliations

Francine E. Garrett-Bakelman*
Weill Cornell Medicine, New York, NY, USA.
University of Virginia School of Medicine, Charlottesville, VA, USA.
Center for Renal Precision Medicine, University of Texas Health, San Antonio, TX, USA.
University of Illinois at Chicago, Chicago, IL, USA.
Ruben C. Gur*
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Colorado State University, Fort Collins, CO, USA.
Weill Cornell Medicine, New York, NY, USA.
The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Present address: Providence Portland Medical Center, Portland, OR, USA.
Johns Hopkins University, Baltimore, MD, USA.
Present address: HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.
Center for Renal Precision Medicine, University of Texas Health, San Antonio, TX, USA.
University of California, San Diego, La Jolla, CA, USA.
Present address: Brown University, Providence, RI, USA.
Colorado State University, Fort Collins, CO, USA.
Northwestern University, Evanston, IL, USA.
University of California, Davis, Davis, CA, USA.
Weill Cornell Medicine, New York, NY, USA.
The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Weill Cornell Medicine, New York, NY, USA.
The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.
Johns Hopkins University, Baltimore, MD, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Augustine M. K. Choi
Weill Cornell Medicine, New York, NY, USA.
University of Illinois at Chicago, Chicago, IL, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
National Aeronautics and Space Administration (NASA), Houston, TX, USA.
Brian E. Crucian
National Aeronautics and Space Administration (NASA), Houston, TX, USA.
Harvard T.H. Chan School of Public Health, Boston, MA, USA.
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Jason I. Feinberg
Johns Hopkins University, Baltimore, MD, USA.
Weill Cornell Medicine, New York, NY, USA.
Johns Hopkins University, Baltimore, MD, USA.
Weill Cornell Medicine, New York, NY, USA.
Present address: Augusta University, Augusta, GA, USA.
University of California, San Diego, La Jolla, CA, USA.
University of Bonn, Bonn, Germany.
Present address: IUBH International University of Applied Sciences, Bad Reichenhall, Germany.
Stanford University School of Medicine, Palo Alto, CA, USA.
University of Washington, Seattle, WA, USA.
Vivian Y. H. Hook
University of California, San Diego, La Jolla, CA, USA.
Johns Hopkins University, Baltimore, MD, USA.
Present address: Mayo Clinic, Rochester, MN, USA.
Northwestern University, Evanston, IL, USA.
Rush University Medical Center, Chicago, IL, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Sarah B. Lumpkins
MEI Technologies, Houston, TX, USA.
Matthew MacKay
Weill Cornell Medicine, New York, NY, USA.
Mark G. Maienschein-Cline https://orcid.org/0000-0001-6619-6788
University of Illinois at Chicago, Chicago, IL, USA.
Weill Cornell Medicine, New York, NY, USA.
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Kiichi Nakahira
Weill Cornell Medicine, New York, NY, USA.
Present address: Nara Medical University, Nara, Japan.
University of California, San Diego, La Jolla, CA, USA.
Robert Pietrzyk
KBRwyle, Houston, TX, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
Rintaro Saito
University of California, San Diego, La Jolla, CA, USA.
Present address: Institute for Advanced Biosciences, Keio University, Tokyo, Japan.
Stanford University School of Medicine, Palo Alto, CA, USA.
University of California, San Diego, La Jolla, CA, USA.
University of California, San Diego, La Jolla, CA, USA.
Weill Cornell Medicine, New York, NY, USA.
Michael B. Stenger
National Aeronautics and Space Administration (NASA), Houston, TX, USA.
Johns Hopkins University, Baltimore, MD, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
University of Washington, Seattle, WA, USA.
University of California, San Diego, La Jolla, CA, USA.
Jing Zhang
Stanford University School of Medicine, Palo Alto, CA, USA.
Michael G. Ziegler
University of California, San Diego, La Jolla, CA, USA.
Sara R. Zwart
University of Texas Medical Branch, Galveston, TX, USA.
John B. Charles§§ [email protected]
National Aeronautics and Space Administration (NASA), Houston, TX, USA.
Space Life and Physical Sciences Division, NASA Headquarters, Washington, DC, USA.
National Space Biomedical Research Institute, Baylor College of Medicine, Houston, TX, USA.
Colorado State University, Fort Collins, CO, USA.
University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Johns Hopkins University, Baltimore, MD, USA.
Weill Cornell Medicine, New York, NY, USA.
The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.
The Feil Family Brain and Mind Research Institute, New York, NY, USA.
The WorldQuant Initiative for Quantitative Prediction, New York, NY, USA.
Stanford University School of Medicine, Palo Alto, CA, USA.
University of California, San Diego, La Jolla, CA, USA.
Scott M. Smith§§ [email protected]
National Aeronautics and Space Administration (NASA), Houston, TX, USA.
Michael P. Snyder§§ [email protected]
Stanford University School of Medicine, Palo Alto, CA, USA.

Funding Information

DLR Space program: 50WB1535
WorldQuant Foundation
Starr Foundation: I9-A9-071
NIDDK: DP3DK094352
NIDDK: DP3DK094352

Notes

*
These authors contributed equally to this work.
§§
Corresponding author. Email: [email protected] (J.B.C); [email protected] (C.E.K.); [email protected] (G.B.I.S.); [email protected] (S.M.B.); [email protected] (M.B.); [email protected] (A.P.F.); [email protected] (S.M.C.L.); [email protected] (C.E.M.); [email protected] (E.M.); [email protected] (B.K.R.); [email protected] (S.M.S.); [email protected] (M.P.S.); [email protected] (F.W.T.)

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