Study of the impact of long-duration space missions at the International Space Station on the astronaut microbiome

Alexander A Voorhies, C Mark Ott, Satish Mehta, Duane L Pierson, Brian E Crucian, Alan Feiveson, Cherie M Oubre, Manolito Torralba, Kelvin Moncera, Yun Zhang, Eduardo Zurek, Hernan A Lorenzi, Alexander A Voorhies, C Mark Ott, Satish Mehta, Duane L Pierson, Brian E Crucian, Alan Feiveson, Cherie M Oubre, Manolito Torralba, Kelvin Moncera, Yun Zhang, Eduardo Zurek, Hernan A Lorenzi

Abstract

Over the course of a mission to the International Space Station (ISS) crew members are exposed to a number of stressors that can potentially alter the composition of their microbiomes and may have a negative impact on astronauts' health. Here we investigated the impact of long-term space exploration on the microbiome of nine astronauts that spent six to twelve months in the ISS. We present evidence showing that the microbial communities of the gastrointestinal tract, skin, nose and tongue change during the space mission. The composition of the intestinal microbiota became more similar across astronauts in space, mostly due to a drop in the abundance of a few bacterial taxa, some of which were also correlated with changes in the cytokine profile of crewmembers. Alterations in the skin microbiome that might contribute to the high frequency of skin rashes/hypersensitivity episodes experienced by astronauts in space were also observed. The results from this study demonstrate that the composition of the astronauts' microbiome is altered during space travel. The impact of those changes on crew health warrants further investigation before humans embark on long-duration voyages into outer space.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Study experimental design. (A) Schematic representation of study experimental design. Sample collection time points are indicated on top of the scheme. Number prefixes indicate days before launch (L−), during flight in the ISS (FD) and before (R−) and after (R+) the return to Earth. Colored circles depict time points used for collection of samples specified on the left of the figure. Data types generated from samples are shown on the right. (B) Diagram depicting space missions scheduled during the study. Relative mission time points, in red, indicate the order in which inflight time points FD7 and FD180/FD360 were collected along the study. Circles denote mission start time for each astronaut that participated in this study. Yellow and grey boxes represent the six-month periods astronauts stayed in the ISS. Black numbers indicate the time in months a mission at the ISS started and ended since the beginning of the first ISS mission that contributed to the study.
Figure 2
Figure 2
Principal coordinate plots of weighted Bray-Curtis beta diversity distances across all samples collected in the study. (A) Samples colored by body or ISS surface site; The permanova p-value and R2 using body site as the independent variable is shown. (B) Human microbiome samples colored by astronaut identifier. Each microbiome is represented with a different symbol. Permanova p-value and R2 using astronaut identifier as the independent variable and stratifying by body site are indicated.
Figure 3
Figure 3
Changes in alpha diversity and richness of the astronauts’ microbiome. Boxplots depicting changes in Shannon alpha diversity (A) and Richness (B) of the five human microbiomes surveyed in this study during a mission to the ISS. Significant linear mixed-effects model p-values are depicted using mission stage Preflight as baseline.
Figure 4
Figure 4
Changes in beta diversity of the astronauts’ microbiome. Boxplots showing differences in weighted (A) and unweighted (B) Bray-Curtis beta diversity distances among microbiome samples within and between mission stages. Within_pre_in, within preflight and within inflight distances; between_pre_in, distances between preflight and inflight samples; between_pre_post, distances between preflight and postflight samples; within_pre_post, distances within preflight and within inflight samples. Linear mixed-effects model p-values ≦ 0.06 are depicted using within_pre_in or within_pre_post as baselines.
Figure 5
Figure 5
Changes in the relative abundance of bacterial genera of the astronauts’ microbiome. Changes in the microbiota of the forearm skin (A), GI (B) and nose (C) during a mission at the ISS and after the return to Earth. Colors represent different phyla; horizontal axis, logarithm of the fold change in relative abundance between preflight and inflight or postflight samples; vertical axis, bacterial genera. Black circles indicate genera belonging to the corresponding preflight core microbiome.
Figure 6
Figure 6
Variation of plasma cytokine concentrations during spaceflight and after the return to Earth. Significance values are with respect to preflight L-180 time point. op-value < 0.1; *p-value < 0.05; **p-value < 0.01; ***p-value < 0.001.
Figure 7
Figure 7
Comparative beta diversity analysis between the astronauts’ skin microbiota and environmental bacteria at the ISS. (A,B) Principal coordinate analyses of weighted (A) and unweighted (B) Bray-Curtis beta diversity of FD180/FD360 forehead and forearm skin samples (triangles) and ISS samples (circles) collected during early and late relative mission time points. Ellipses represent 95% confidence intervals. Permanova p-values and R2 values are shown. (C,D) comparative analysis of the difference in mean weighted (C) and unweighted (D) Bray-Curtis beta diversity distances between skin and ISS samples collected at FD7 (C) or FD180 (D). Welch two samples t-test p-values for each comparison are shown.
Figure 8
Figure 8
Changes in alpha diversity and richness of astronauts’ skin microbiota and environmental bacteria at the ISS during the entire duration of the study. Changes in Shannon alpha diversity (A) and Richness (B) of the ISS microbiota over time and their association with Shannon alpha diversity and Richness of forehead and forearm skin samples collected at FD7, FD180 and FD360. Pearson correlation coefficients between skin and ISS samples and correlation p-values are shown.

References

    1. Ohnishi K, Ohnishi T. The biological effects of space radiation during long stays in space. Biol Sci Space. 2004;18:201–205. doi: 10.2187/bss.18.201.
    1. Mallis MM, DeRoshia CW. Circadian rhythms, sleep, and performance in space. Aviat Space Environ Med. 2005;76:B94–107.
    1. Crucian BE, et al. Immune System Dysregulation During Spaceflight: Potential Countermeasures for Deep Space Exploration Missions. Front Immunol. 2018;9:1437. doi: 10.3389/fimmu.2018.01437.
    1. Hackney KJ, et al. The Astronaut-Athlete: Optimizing Human Performance in Space. J Strength Cond Res. 2015;29:3531–3545. doi: 10.1519/JSC.0000000000001191.
    1. Wu B, et al. On-orbit sleep problems of astronauts and countermeasures. Mil Med Res. 2018;5:17. doi: 10.1186/s40779-018-0165-6.
    1. Zhang LF, Hargens AR. Spaceflight-Induced Intracranial Hypertension and Visual Impairment: Pathophysiology and Countermeasures. Physiol Rev. 2018;98:59–87. doi: 10.1152/physrev.00017.2016.
    1. NASA Human Research Roadmap, (2019).
    1. Howe Alexis, Kiffer Frederico, Alexander Tyler C., Sridharan Vijayalakshmi, Wang Jing, Ntagwabira Fabio, Rodriguez Analiz, Boerma Marjan, Allen Antiño R. Long-Term Changes in Cognition and Physiology after Low-Dose 16O Irradiation. International Journal of Molecular Sciences. 2019;20(1):188. doi: 10.3390/ijms20010188.
    1. Bigley AB, et al. NK cell function is impaired during long-duration spaceflight. J Appl Physiol (1985) 2019;126:842–853. doi: 10.1152/japplphysiol.00761.2018.
    1. Crucian B, et al. Incidence of clinical symptoms during long-duration orbital spaceflight. Int J Gen Med. 2016;9:383–391. doi: 10.2147/IJGM.S114188.
    1. Antonsen, E. Risk of Adverse Health Outcomes & Decrements in Performance due to Inflight Medical Conditions, (2017).
    1. Mehta SK, et al. Multiple latent viruses reactivate in astronauts during Space Shuttle missions. Brain Behav Immun. 2014;41:210–217. doi: 10.1016/j.bbi.2014.05.014.
    1. Pierson DL, Stowe RP, Phillips TM, Lugg DJ, Mehta SK. Epstein-Barr virus shedding by astronauts during space flight. Brain Behav Immun. 2005;19:235–242. doi: 10.1016/j.bbi.2004.08.001.
    1. Crucian B, et al. Alterations in adaptive immunity persist during long-duration spaceflight. NPJ Microgravity. 2015;1:15013. doi: 10.1038/npjmgrav.2015.13.
    1. Crucian BE, Stowe RP, Pierson DL, Sams CF. Immune system dysregulation following short- vs long-duration spaceflight. Aviat Space Environ Med. 2008;79:835–843. doi: 10.3357/ASEM.2276.2008.
    1. Kaur I, Simons ER, Castro VA, Mark Ott C, Pierson DL. Changes in neutrophil functions in astronauts. Brain Behav Immun. 2004;18:443–450. doi: 10.1016/j.bbi.2003.10.005.
    1. Stowe RP, Sams CF, Pierson DL. Effects of mission duration on neuroimmune responses in astronauts. Aviat Space Environ Med. 2003;74:1281–1284.
    1. Crucian B, et al. A case of persistent skin rash and rhinitis with immune system dysregulation onboard the International Space Station. J Allergy Clin Immunol Pract. 2016;4:759–762 e758. doi: 10.1016/j.jaip.2015.12.021.
    1. Li P, et al. Simulated microgravity disrupts intestinal homeostasis and increases colitis susceptibility. FASEB J. 2015;29:3263–3273. doi: 10.1096/fj.15-271700.
    1. Foster JS, Khodadad CL, Ahrendt SR, Parrish ML. Impact of simulated microgravity on the normal developmental time line of an animal-bacteria symbiosis. Sci Rep. 2013;3:1340. doi: 10.1038/srep01340.
    1. Nickerson CA, Ott CM, Wilson JW, Ramamurthy R, Pierson DL. Microbial responses to microgravity and other low-shear environments. Microbiol Mol Biol Rev. 2004;68:345–361. doi: 10.1128/MMBR.68.2.345-361.2004.
    1. Pierson DL, et al. Epidemiology of Staphylococcus aureus during space flight. FEMS Immunol Med Microbiol. 1996;16:273–281. doi: 10.1111/j.1574-695X.1996.tb00146.x.
    1. Johnston, R. S. & Dietlein, L. F. Skylab Environmental and Crew Microbiology Studies. (NASA, 1977).
    1. Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–214. doi: 10.1038/nature11234.
    1. Costello EK, et al. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694–1697. doi: 10.1126/science.1177486.
    1. Shao D, et al. Simulated microgravity affects some biological characteristics of Lactobacillus acidophilus. Appl Microbiol Biotechnol. 2017;101:3439–3449. doi: 10.1007/s00253-016-8059-6.
    1. Klaus DM, Howard HN. Antibiotic efficacy and microbial virulence during space flight. Trends Biotechnol. 2006;24:131–136. doi: 10.1016/j.tibtech.2006.01.008.
    1. Ciferri O, Tiboni O, Di Pasquale G, Orlandoni AM, Marchesi ML. Effects of microgravity on genetic recombination in Escherichia coli. Naturwissenschaften. 1986;73:418–421. doi: 10.1007/BF00367284.
    1. Benoit MR, et al. Microbial antibiotic production aboard the International Space Station. Appl Microbiol Biotechnol. 2006;70:403–411. doi: 10.1007/s00253-005-0098-3.
    1. Decelle JG, Taylor GR. Autoflora in the upper respiratory tract of Apollo astronauts. Appl Environ Microbiol. 1976;32:659–665.
    1. Lencner AA, et al. The quantitative composition of the intestinal lactoflora before and after space flights of different lengths. Nahrung. 1984;28:607–613. doi: 10.1002/food.19840280608.
    1. Brown LR, Fromme WJ, Handler SF, Wheatcroft MG, Johnston DA. Effect of Skylab missions on clinical and microbiologic aspects of oral health. J Am Dent Assoc. 1976;93:357–363. doi: 10.14219/jada.archive.1976.0502.
    1. Lizko NN, Silov VM, Syrych GD. Events in he development of dysbacteriosis of the intestines in man under extreme conditions. Nahrung. 1984;28:599–605. doi: 10.1002/food.19840280604.
    1. Nefedov YG, Shilov VM, Konstantinova IV, Zaloguyev SN. Microbiological and immunological aspects of extended manned space flights. Life Sci Space Res. 1971;9:11–16.
    1. Taylor PW, Sommer AP. Towards rational treatment of bacterial infections during extended space travel. Int J Antimicrob Agents. 2005;26:183–187. doi: 10.1016/j.ijantimicag.2005.06.002.
    1. Hales, N. W. et al. A countermeasure to ameliorate immune dysfunction in in vitro simulated microgravity environment: role of cellularnucleotide nutrition. In Vitro Cell Dev Biol Anim38, 213–217, doi:10.1290/1071-2690(2002)038<0213:ACTAID>;2 (2002).
    1. Mosca A, Leclerc M. & Hugot, J. P. Gut Microbiota Diversity and Human Diseases: Should We Reintroduce Key Predators in Our Ecosystem? Front Microbiol. 2016;7:455. doi: 10.3389/fmicb.2016.00455.
    1. Ritchie LE, et al. Space Environmental Factor Impacts upon Murine Colon Microbiota and Mucosal Homeostasis. PLoS One. 2015;10:e0125792. doi: 10.1371/journal.pone.0125792.
    1. Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. doi: 10.1038/nature08821.
    1. Miquel S, et al. Faecalibacterium prausnitzii and human intestinal health. Curr Opin Microbiol. 2013;16:255–261. doi: 10.1016/j.mib.2013.06.003.
    1. Lopez-Siles M, Duncan SH, Garcia-Gil LJ, Martinez-Medina M. Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics. ISME J. 2017;11:841–852. doi: 10.1038/ismej.2016.176.
    1. Crucian BE, et al. Plasma cytokine concentrations indicate that in vivo hormonal regulation of immunity is altered during long-duration spaceflight. J Interferon Cytokine Res. 2014;34:778–786. doi: 10.1089/jir.2013.0129.
    1. Strewe C, et al. Effects of parabolic flight and spaceflight on the endocannabinoid system in humans. Rev Neurosci. 2012;23:673–680. doi: 10.1515/revneuro-2012-0057.
    1. Chen YJ, et al. Parasutterella, in association with irritable bowel syndrome and intestinal chronic inflammation. J Gastroenterol Hepatol. 2018;33:1844–1852. doi: 10.1111/jgh.14281.
    1. Takeshita K, et al. A Single Species of Clostridium Subcluster XIVa Decreased in Ulcerative Colitis Patients. Inflamm Bowel Dis. 2016;22:2802–2810. doi: 10.1097/MIB.0000000000000972.
    1. Henning SM, et al. Decaffeinated green and black tea polyphenols decrease weight gain and alter microbiome populations and function in diet-induced obese mice. Eur J Nutr. 2018;57:2759–2769. doi: 10.1007/s00394-017-1542-8.
    1. Bailey MT, et al. Exposure to a social stressor alters the structure of the intestinal microbiota: implications for stressor-induced immunomodulation. Brain Behav Immun. 2011;25:397–407. doi: 10.1016/j.bbi.2010.10.023.
    1. Gabay C. Interleukin-6 and chronic inflammation. Arthritis Res Ther. 2006;8(Suppl 2):S3. doi: 10.1186/ar1917.
    1. Li B, et al. IL-10 engages macrophages to shift Th17 cytokine dependency and pathogenicity during T-cell-mediated colitis. Nat Commun. 2015;6:6131. doi: 10.1038/ncomms7131.
    1. McGeachy MJ, Cua DJ. Th17 cell differentiation: the long and winding road. Immunity. 2008;28:445–453. doi: 10.1016/j.immuni.2008.03.001.
    1. Scher JU, et al. Decreased bacterial diversity characterizes the altered gut microbiota in patients with psoriatic arthritis, resembling dysbiosis in inflammatory bowel disease. Arthritis Rheumatol. 2015;67:128–139. doi: 10.1002/art.38892.
    1. Dao MC, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016;65:426–436. doi: 10.1136/gutjnl-2014-308778.
    1. Png CW, et al. Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol. 2010;105:2420–2428. doi: 10.1038/ajg.2010.281.
    1. Vigsnaes LK, Brynskov J, Steenholdt C, Wilcks A, Licht TR. Gram-negative bacteria account for main differences between faecal microbiota from patients with ulcerative colitis and healthy controls. Benef Microbes. 2012;3:287–297. doi: 10.3920/BM2012.0018.
    1. Rajilic-Stojanovic M, Shanahan F, Guarner F, de Vos WM. Phylogenetic analysis of dysbiosis in ulcerative colitis during remission. Inflamm Bowel Dis. 2013;19:481–488. doi: 10.1097/MIB.0b013e31827fec6d.
    1. Ottman, N. et al. Genome-Scale Model and Omics Analysis of Metabolic Capacities of Akkermansia muciniphila Reveal a Preferential Mucin-Degrading Lifestyle. Appl Environ Microbiol83, 10.1128/AEM.01014-17 (2017).
    1. Cani PD, et al. Endocannabinoids–at the crossroads between the gut microbiota and host metabolism. Nat Rev Endocrinol. 2016;12:133–143. doi: 10.1038/nrendo.2015.211.
    1. Ruokolainen L, et al. Green areas around homes reduce atopic sensitization in children. Allergy. 2015;70:195–202. doi: 10.1111/all.12545.
    1. Hanski I, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci USA. 2012;109:8334–8339. doi: 10.1073/pnas.1205624109.
    1. Fyhrquist N, et al. Acinetobacter species in the skin microbiota protect against allergic sensitization and inflammation. J Allergy Clin Immunol. 2014;134:1301–1309 e1311. doi: 10.1016/j.jaci.2014.07.059.
    1. Tronnier H, Wiebusch M, Heinrich U. Change in skin physiological parameters in space–report on and results of the first study on man. Skin Pharmacol Physiol. 2008;21:283–292. doi: 10.1159/000148045.
    1. Zeeuwen PL, et al. Microbiome dynamics of human epidermis following skin barrier disruption. Genome Biol. 2012;13:R101. doi: 10.1186/gb-2012-13-11-r101.
    1. Musher DM, et al. The current spectrum of Staphylococcus aureus infection in a tertiary care hospital. Medicine (Baltimore) 1994;73:186–208. doi: 10.1097/00005792-199407000-00002.
    1. Sharafutdinov IS, et al. Antimicrobial Effects of Sulfonyl Derivative of 2(5H)-Furanone against Planktonic and Biofilm Associated Methicillin-Resistant and -Susceptible Staphylococcus aureus. Front Microbiol. 2017;8:2246. doi: 10.3389/fmicb.2017.02246.
    1. Cannon JW, et al. An economic case for a vaccine to prevent group A streptococcus skin infections. Vaccine. 2018;36:6968–6978. doi: 10.1016/j.vaccine.2018.10.001.
    1. Feazel LM, Robertson CE, Ramakrishnan VR, Frank DN. Microbiome complexity and Staphylococcus aureus in chronic rhinosinusitis. Laryngoscope. 2012;122:467–472. doi: 10.1002/lary.22398.
    1. Ramakrishnan VR, Feazel LM, Abrass LJ, Frank DN. Prevalence and abundance of Staphylococcus aureus in the middle meatus of patients with chronic rhinosinusitis, nasal polyps, and asthma. Int Forum Allergy Rhinol. 2013;3:267–271. doi: 10.1002/alr.21101.
    1. Jervis Bardy J, Psaltis AJ. Next Generation Sequencing and the Microbiome of Chronic Rhinosinusitis: A Primer for Clinicians and Review of Current Research, Its Limitations, and Future Directions. Ann Otol Rhinol Laryngol. 2016;125:613–621. doi: 10.1177/0003489416641429.
    1. Muluk NB, Altin F, Cingi C. Role of Superantigens in Allergic Inflammation: Their Relationship to Allergic Rhinitis, Chronic Rhinosinusitis, Asthma, and Atopic Dermatitis. Am J Rhinol Allergy. 2018;32:502–517. doi: 10.1177/1945892418801083.
    1. Singh A, et al. Immune-modulatory effect of probiotic Bifidobacterium lactis NCC2818 in individuals suffering from seasonal allergic rhinitis to grass pollen: an exploratory, randomized, placebo-controlled clinical trial. Eur J Clin Nutr. 2013;67:161–167. doi: 10.1038/ejcn.2012.197.
    1. Ren J, et al. Immunomodulatory effect of Bifidobacterium breve on experimental allergic rhinitis in BALB/c mice. Exp Ther Med. 2018;16:3996–4004. doi: 10.3892/etm.2018.6704.
    1. Bailey MT. Influence of stressor-induced nervous system activation on the intestinal microbiota and the importance for immunomodulation. Adv Exp Med Biol. 2014;817:255–276. doi: 10.1007/978-1-4939-0897-4_12.
    1. Bailey MT, Engler H, Sheridan JF. Stress induces the translocation of cutaneous and gastrointestinal microflora to secondary lymphoid organs of C57BL/6 mice. J Neuroimmunol. 2006;171:29–37. doi: 10.1016/j.jneuroim.2005.09.008.
    1. De Palma G, et al. Microbiota and host determinants of behavioural phenotype in maternally separated mice. Nat Commun. 2015;6:7735. doi: 10.1038/ncomms8735.
    1. Rea K, Dinan TG, Cryan JF. The microbiome: A key regulator of stress and neuroinflammation. Neurobiol Stress. 2016;4:23–33. doi: 10.1016/j.ynstr.2016.03.001.
    1. Mehta SK, et al. Stress-induced subclinical reactivation of varicella zoster virus in astronauts. J Med Virol. 2004;72:174–179. doi: 10.1002/jmv.10555.
    1. Petrakova L, et al. Psychosocial Stress Increases Salivary Alpha-Amylase Activity Independently from Plasma Noradrenaline Levels. PLoS One. 2015;10:e0134561. doi: 10.1371/journal.pone.0134561.
    1. Mandsager KT, Robertson D, Diedrich A. The function of the autonomic nervous system during spaceflight. Clin Auton Res. 2015;25:141–151. doi: 10.1007/s10286-015-0285-y.
    1. Morgan CA, 3rd, Rasmusson A, Pietrzak RH, Coric V, Southwick SM. Relationships among plasma dehydroepiandrosterone and dehydroepiandrosterone sulfate, cortisol, symptoms of dissociation, and objective performance in humans exposed to underwater navigation stress. Biol Psychiatry. 2009;66:334–340. doi: 10.1016/j.biopsych.2009.04.004.
    1. Loria RM, Inge TH, Cook SS, Szakal AK, Regelson W. Protection against acute lethal viral infections with the native steroid dehydroepiandrosterone (DHEA) J Med Virol. 1988;26:301–314. doi: 10.1002/jmv.1890260310.
    1. Loria RM, Padgett DA. Control of the immune response by DHEA and its metabolites. Rinsho Byori. 1998;46:505–517.
    1. Prall SP, Larson EE, Muehlenbein MP. The role of dehydroepiandrosterone on functional innate immune responses to acute stress. Stress Health. 2017;33:656–664. doi: 10.1002/smi.2752.
    1. Lax S, et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science. 2014;345:1048–1052. doi: 10.1126/science.1254529.
    1. Meadow JF, et al. Humans differ in their personal microbial cloud. PeerJ. 2015;3:e1258. doi: 10.7717/peerj.1258.
    1. Checinska A, et al. Microbiomes of the dust particles collected from the International Space Station and Spacecraft Assembly Facilities. Microbiome. 2015;3:50. doi: 10.1186/s40168-015-0116-3.
    1. Leung TN, Hon KL, Leung AK, Group A. Streptococcus disease in Hong Kong children: an overview. Hong Kong Med J. 2018 doi: 10.12809/hkmj187275.
    1. Rosa-Fraile M, Spellerberg B. Reliable Detection of Group B Streptococcus in the Clinical Laboratory. J Clin Microbiol. 2017;55:2590–2598. doi: 10.1128/JCM.00582-17.
    1. Paharik, A. E. & Horswill, A. R. The Staphylococcal Biofilm: Adhesins, Regulation, and Host Response. Microbiol Spectr4, 10.1128/microbiolspec.VMBF-0022-2015 (2016).
    1. Bernard K. The genus corynebacterium and other medically relevant coryneform-like bacteria. J Clin Microbiol. 2012;50:3152–3158. doi: 10.1128/JCM.00796-12.
    1. Mubaiwa, T. D. et al. The sweet side of the pathogenic Neisseria: the role of glycan interactions in colonisation and disease. Pathog Dis75, 10.1093/femspd/ftx063 (2017).
    1. Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507. doi: 10.1093/biomet/93.3.491.
    1. Mehta SK, et al. Latent virus reactivation in astronauts on the international space station. NPJ Microgravity. 2017;3:11. doi: 10.1038/s41526-017-0015-y.
    1. Jeraldo P, Chia N, Goldenfeld N. On the suitability of short reads of 16S rRNA for phylogeny-based analyses in environmental surveys. Environ Microbiol. 2011;13:3000–3009. doi: 10.1111/j.1462-2920.2011.02577.x.
    1. Bokulich NA, Mills DA. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl Environ Microbiol. 2013;79:2519–2526. doi: 10.1128/AEM.03870-12.
    1. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604.
    1. Schloss PD, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09.
    1. Quast C, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–596. doi: 10.1093/nar/gks1219.
    1. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–930. doi: 10.1111/j.1654-1103.2003.tb02228.x.
    1. Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using. J Stat Softw. 2015;67:lme4. doi: 10.18637/jss.v067.i01.
    1. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw82, 10.18637/jss.v082.i13 (2017).
    1. Robinson MD, McCarthy DJ, Smyth G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–140. doi: 10.1093/bioinformatics/btp616.
    1. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq. 2. Genome Biol. 2014;15:550. doi: 10.1186/s13059-014-0550-8.

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