Serving over three billion passengers annually, air travel serves as a conduit for infectious disease spread, including emerging infections and pandemics. Over two dozen cases of in-flight transmissions have been documented. To understand these risks, a characterization of the airplane cabin microbiome is necessary. Our study team collected 229 environmental samples on ten transcontinental US flights with subsequent 16S rRNA sequencing. We found that bacterial communities were largely derived from human skin and oral commensals, as well as environmental generalist bacteria. We identified clear signatures for air versus touch surface microbiome, but not for individual types of touch surfaces. We also found large flight-to-flight beta diversity variations with no distinguishing signa- tures of individual flights, rather a high between-flight diversity for all touch surfaces and particularly for air samples. There was no systematic pattern of microbial community change from pre- to post-flight. Our findings are similar to those of other recent studies of the microbiome of built environments. In summary, the airplane cabin microbiome has immense airplane to airplane variability. The vast majority of airplane-associated microbes are human commensals or non-pathogenic, and the results provide a baseline for non-crisis-level airplane microbiome conditions. . . . . Keywords Commercial airplanes Microbiome Bacteria Pandemic Respiratory infection Howard Weiss and Vicki Stover Hertzberg contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00248-018-1191-3) contains supplementary material, which is available to authorized users. * Howard Weiss School of Mathematics, The Georgia Institute of Technology, 686 email@example.com Cherry St. NW, Atlanta, GA 30313, USA Vicki Stover Hertzberg Nell Hodgson Woodruff School of Nursing, Emory University, 1520 firstname.lastname@example.org Clifton Rd. NE, Atlanta, GA 30322, USA Chris Dupont J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA 92037, email@example.com USA Josh L. Espinoza firstname.lastname@example.org 4 HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL 35806, USA Shawn Levy email@example.com J. Craig Venter Institute, 9714 Medical Center Drive, Karen Nelson Rockville, MD 20850, USA firstname.lastname@example.org Sharon Norris Boeing Health Services, The Boeing Company, 3156 160th Ave. NE, email@example.com Bellevue, WA 98008-2245, USA Weiss H. et al. Introduction airplane cabin microbiome might differ considerably from those of other built environments. Another key difference is With over three billion airline passengers annually, the risk of that in an airplane cabin, it is difficult to avoid a mobile sick in-flight transmission of infectious disease is a vital global person, or one sitting in close proximity. health concern [1, 2]. Over two dozen cases of in-flight trans- In another publication , we describe behaviors and mission have been documented, including influenza [3–7], close contacts of all passengers and flight attendants in the measles [8, 9], meningococcal infections , norovirus economy cabin on ten flights of duration 4 hours or more, , SARS [12, 13], shigellosis , cholera , and the FlyHealthy™ Study. FlyHealthy™ has provided first de- multi-drug resistant tuberculosis [1, 16–18]. Studies of tailed understanding of infectious disease transmission oppor- SARS [12, 13] and pandemic influenza (H1N1p) trans- tunities in an airplane cabin. In addition to quantifying the mission on airplanes indicate that air travel can serve as a opportunities, we wanted to understand the infectious agents conduit for the rapid spread of newly emerging infections present in an airplane cabin that might be transmitted during and pandemics. Further, some of these studies suggest that these opportunities. the movements of passengers and crew (and their close con- To this end, we identified the microbiota present on these tacts) may be an important factor in disease transmission. In flights, allowing characterization of the airplane cabin 2014, a passenger infected with Ebola flew on Frontier airlines microbiome. We hypothesized that the airplane cabin the night before being admitted to a hospital . Luckily, she microbiome differs from that of other built environments did not infect anybody during that trip. due to the above-stated reasons. Since the majority of flights Despite many sensational media stories and anecdotes, e.g., were during the seasonal flu epidemic in either the originating BFlying The Filthy Skies^ or BThe Gross Truth About city or the destination city, we were interested to determine if Germs and Airplanes^ , the true risks of in-flight trans- we could detect influenza virus in our samples. Since the mission are unknown. An essential component of risk assess- transmission opportunities we characterized in the first part ment and public health guidance is characterizing the back- of the FlyHealthy™ study were those that would allow trans- ground microbial communities present, in particular those in mission by large droplets, we were interested in sampling air the air and on common touch surfaces. Next-generation se- as well as touch surfaces (fomites). Key questions related to quencing has the potential to identify all bacteria present via differences between types of samples (air versus touch sur- their genomes, commonly called the microbiome. There have faces), pre- to post-flight changes, and changes from flight- been a few previous studies of the bacterial community in to-flight in the Bcore^ airplane cabin microbiome. cabin air [23–26], but none, to our knowledge, on airplane touch surfaces. These studies estimated total bacterial burden of culturable cells present, and applied early forms of 16S Results rRNA sequencing and bioinformatics, claiming species-level resolution. At the time of these studies, there were far fewer Airplane Cabin Bacterial Communities in the Air reference genomes with which to align. Although these were and on Touch Surfaces at the vanguard of research of the microbiome of built envi- ronments, 10 years later, current methods and protocols are Skin commensals in the family Propionibacteriaceae domi- significantly more rigorous. nate both air (~ 20% post-filtered reads) and touch surfaces The microbiome of the built environment is an active re- (~ 27% post-filtered reads). There is substantial overlap of the search area. Using a wide range of methods, authors have top 20 families in air and touch surface samples (Fig. 1). The studied the microbiomes of classrooms [27–29], homes top ten families in both air and fomites additionally contain [30–32], offices [33, 34], hospitals , museums , nurs- Enterobacteriaceae, Staphylococcaceae, Streptococcaceae, ing homes , stores , and subways [39–41]. Several of Corynebacteriaceae,and Burkholderiaceae.The environ- these studies, particularly those of classrooms and offices, mental bacteria Sphingomonadaceae is quite prevalent in the identified significant quantities of Lactobacillus on seats. air, but much less so on touch surfaces. Note that Bunclassified With the exception of the hospital microbiome, all of these family^ aggregates different families from different higher studies indicate that the main microbiome constituents, at the level taxa. The top OTUs are shown in SM Fig. 1. family level, are human commensal and environmental bacte- OTUs within the genera Propionibacterium and ria. What else could they be? Burkholderia were present in every sample and two OTUs, Airplane environments are unique to the examples listed annotated as genus Staphylococcus and Streptococcus above. Special features include very dry air, periodic high (oralis), were present in all but one sample. These four occupant densities, exposure to the microbiota of the high OTUs are contained in three phyla: Actinobacteria, atmosphere, and long periods during which occupants have Proteobacteria,and Firmicutes,and comprise the Bcore^ extremely limited mobility. Thus, one might expect that the airplane cabin microbiome. The Airplane Cabin Microbiome Fig. 1 Most prevalent families in air (left) and touch surface samples (right) by relative abundance (proportion of families) Air and Touch Surface Communities Have Discernible with the variance explained by PC1 (Fig. 2b), this indicates a Signatures, but There Are No Discernible Signatures clear signature of the air community. The complement is the of Touch Surface Types signature of the touch surface community. There is a potpourri of touch surface types in the figure, again indicating the lack of Figure 2 shows the results of the principal component analysis clear signature of individual touch surface type. There are no (PCA) on a log-scale of families of all samples over all ten statistically significant differences of alpha diversity between flights. The associated scree plot (SM Fig. 3)indicates that air and fomites as measured by any of six indices (SM Fig. 2). the vast majority (73%) of the variability is captured by first Use of an infinite Dirichlet–multinomial mixture (iDMM) principal component, about an order of magnitude more than model  identified four clusters (or ecostates), with ecostate that captured by PC2. We observe that the air samples are 4 containing the vast majority of air samples, though it also primarily positive on PC1 and, in fact, greater than 50, while includes many fomite samples as well (Fig. 3a). Figure 3b the touch surface samples are largely negative. When combined shows the diagnostic OTUs present in this air cluster and their Fig. 2 Scatterplot of the logs of the first two principal components, colored by sample source. a Families. b OTUs Weiss H. et al. Fig. 3 Results of iDMM analysis indicating two distinct ecostates. a Composition of the four ecostates identified in the iDMM analysis. b Most prevalent OTUs identified in the two ecostates associated with cabin air weights. Note that the weights are an essential component of within-flight (W) beta diversity, that is, each flight is already this characterization. starting with microbiomes that are likely different from other Another important question is whether bacterial communi- flights. ties change discernibly during flight? Again, Fig. 4 shows the admixture of pre- and post-flight communities in the touch surface samples. Note the linearity of these scatterplots of Discussion the logged average number of reads for OTUs from pre- to post-flight for each touch surface type. There is no discernible Toward the goal of characterizing the airplane cabin pattern of change of pre-flight to post-flight communities. microbiome, our study team flew on ten transcontinental A final key question is whether bacterial communities in US flights on which we collected 229 air and touch surface the cabin air change discernibly from flight to flight? For samples. We employed highly stringent quality control example, is there a difference between east-bound versus criteria during sampling, sample extraction, 16S rRNA west-bound flights? A principle component analysis at both gene sequencing, and the bioinformatics pipeline. The ob- the family and OTU levels shows a wide variation with no served microbial communities, when merged across sam- clustering by flight (Fig. 5). Furthermore, without exception, ples, are comprised of human commensals and common between-flight (B) beta diversity is statistically higher than environmental (water and soil) genera. We identified a The Airplane Cabin Microbiome Fig. 4 Logged average number of reads for OTUs from pre- to post-flight for each touch surface (fomite) type Bcore^ airplane cabin microbiome containing OTUs within Propionibacterium is a genus of the phylum the genera Propionibacterium, Burkholderia (glumae), Actinobacteria, comprised of commensal bacteria that live Staphylococcus,and Streptococcus (oralis). We identified on human skin and commonly implicated in acne. clear OTU signatures for the air microbiome, but not for Burkholderia glumae is a species of the phylum individual touch surface types. We found no meaningful Proteobacteria and is a soil bacterium. Staphylococcus is a differences between air and touch surfaces with respect to genus of the phylum Firmicutes that is found on the skin alpha diversity measures. Finally, we found no systematic and mucus membranes of humans. Most species of pattern of change from pre- to post-flight. Staphylococcus are harmless. Streptococcus oralis, a species We also found large flight-to-flight variations with no of the phylum Firmicutes, is normally found in the oral cav- distinguishing signatures of individual flights. This would ities of humans. These constituents of the core airplane cabin suggest that each flight starts with a different microbiome microbiome are usually harmless to humans unless an unusual from other flights, which would greatly hinder pre-and opportunity for infection is present, such as a weakened im- post-flight microbiome comparisons (e.g., Fig. 4)that ag- mune system, an altered gut microbiome, or a breach in the gregate samples between flights. A methodological impli- integumentary system. cation is that aggregating communities between flights for While airplane cabins are certainly examples of built statistical analyses is problematic. Instead, sample repli- environments, there are unique features. These include cation must be derived from within a flight in order to very dry air, periodic high occupant densities, exposure determine how passengers alter the airplane cabin to the microbiota of the high atmosphere, long periods microbiome. Every plane being different in terms of its during which occupants have extremely limited mobility, microbiome suggests that each retains aspects of its his- and it is difficult to avoid a mobile sick person or one torical living microbiome, that is, the passengers. The de- sitting in close proximity. Half of the cabin air is recycled velopment of a cleaning routine that erases much of this after passing through a bank of HEPA filters, and the inherited microbiome could be a powerful preventative other half is taken from the outside. Furthermore, the air- measure against the spread of disease. line’s cabin cleaning policy is to disinfect all hard surfaces Weiss H. et al. ab Fig. 5 Beta diversity of samples. Scatterplot of the first two principal components of the beta diversity analysis, for a OTU-level and b family-level abundance, based on a Bray-Curtis distance. c Distributions of Bray-Curtis distances for different touch surface types, within and between flights whenever the plane Bovernights,^ and all touch surface species, Propionibacterium acnes, a common skin commen- samples were taken from hard surfaces. Different airlines sal, was excluded from discovery in the New York City sub- have different cabin disinfection protocols and supervise way microbiome study. their cabin cleaning staff in different ways. Although different primers and sequencing techniques Despite the uniqueness of the airplane cabin as a built en- were used, the core microbiome identified in the Boston sub- vironment, our findings are surprisingly consistent with other way system study has significant overlap with airplane cabins recent studies of the microbiome of built environments. This . Corynebacteriaceae, a skin commensal, appeared in consistency is reassuring in light of frequent sensationalistic nearly every subway sample, and while we do not include it media stories about dangerous germs found on airplanes. For in the airplane cabin core list, it was present in all but ten of this reason, there is no more risk from 4 to 5 hours spent in an our samples. A study of the microbiome of the International airplane cabin than 4–5 hours spent in an office, all other Space Station, the only other airborne built environment that exposures being the same. Our microbiome characterization has been studied, led to the same conclusion , as did two also provides a baseline for non-crisis level airplane studies of office spaces [33, 34]. microbiome conditions. A number of previous studies identified large amounts of It is not possible to make quantitative comparisons to other Lactobacillus,but Lactobacillaceae did not appear in our list studies which used different primers and different sequencing of 20 most prevalent families in our touch surface samples. methods and technologies. For example, the genus Lactobacillus is commonly found in vaginal microbiota, sug- Propionibacterium is a core component of the airplane cabin gesting that it should be found on surfaces where women sit. microbiome, but by choice of primers, the most common Many other studies of the built environment have sampled The Airplane Cabin Microbiome seats, and thus, it is not surprising to find Lactobacilli present Air Sampling Methods in those environments. We did not sample from the seat fabric where passengers sat; thus, the absence of Lactobacilli in the The two air sampling pumps used were model SKC 20 most prevalent families is to be expected. AirChek XR5000. These were located in a seat at the Airplanes fly through clouds. The narrow-body twin-en- back of the economy class cabin. Both pumps sampled gine models on which we flew use about 50% bleed at 3.5 liters per second, the NIOSH protocol for station- (outside) air to refresh the cabin air throughout the flight. ary sampling and approximately the normal breathing A study of the microbiome of clouds finds some members rate of adults. of the Propionibacterium and Burkholderia families in their Just prior to each sampling, each pump was calibrated core, as well as Streptococcus in some samples . A using a MesaLab Defender Calibrator. Air samples of 30- more recent study of cloud water found Burkholderia, mindurationwere collected onboardtheaircraft during Staphylococcus,and Streptococcus in samples . five distinct sampling intervals. Once the pilot an- Interesting future research would be to ascertain the nounced the flight time, we calculated the quarter-way influence of the cloud microbiome on the airplane cabin point, halfway point, and three quart-way point. Thus, microbiome. the five sampling periods were pre-boarding and boarding, In conclusion, our study found that although the Q1 ± 15 min, Q2 ± 15 min, Q3 ± 15 min, and touchdown microbiome of airplane cabins has large flight-to-flight to end of deplaning. In addition, one sample was col- variations, it resembles the microbiome of many other lected throughout the whole flight from 10,000 ft on built environments. This work adds to the growing body ascent to 10,000 ft on descent. Flight 2 only has data of evidence characterizing the built environment. These for four time points. Following each sampling period, investigations form critical linkages between the categories the sampling cartridges were wrapped with Teflon tape, of environmental and human-associated microbial ecology, labeled, logged, and placed in a cooler with chemical and thus must meet the challenges of both areas. ice packs. Improvements in future studies should include incorpora- tion of rich metadata, such as architectural and other de- Fomite Sampling Methods sign features, human-surface contacts, and environmental exposures, as well as determination of microbe viability Prior to each flight, we prepared an ordered list of seven ran- and the mechanisms used to persist in the airplane cabin domly selected seats, of which the first two occupied seats, as environment. Identification of microbes that can be trans- confirmed by the gate agent prior to boarding, were sampled. ferred between passengers and specific fomites will be We also randomly chose a rear lavatory door (port or star- especially important in informing public health and trans- board) for sampling. portation policy. We hope to undertake an analogous study We swabbed the laboratory door handles using Bode on significantly longer, international flights, as well as at SecurSwab DNA Collector dual swabs, placing three drops key locations at departing and arriving airports. An im- of DNA- and RNA-free water on one of the two swabs, then, proved understanding of the airplane cabin microbiome swabbing in one direction within a 9 cm × 9 cm template, and and how it is affected by passengers and crew may lead finally swabbing in the perpendicular direction within the ultimately to construction of airplane cabins that maintain same template. Afterwards, we placed each swab into its se- human health. cure tube, labeled it, logged it, and placed it into a cooler on a chemical ice pack. We sampled three touch surfaces at each passenger seat— Materials and Methods the inside tray table, the outside tray table, and the seat belt buckle. Using the templates and the dual swabs, we sampled Selection of Flights the bottom corners of each side of the tray table as described above. We did not use the template to swab the seat belt Each of five round-trips, on non-stop flights, targeted a differ- buckle; rather, we swabbed the entire upper surface in one ent west coast destination to provide data representative of direction and then in the perpendicular direction. We placed transcontinental flights. We flew to San Diego, Los Angeles, each swab into its secure tube, labeled it, logged it, and placed San Francisco, and Portland, OR, between November 2012 it into a cooler on a chemical ice pack. and March 2013. We flew to Seattle, WA, in May 2013. We Material from the two swabs was combined in Tris Buffer flew on narrow-body twin-engine aircraft, with all but one and homogenized per kit instructions. The air filters were sim- flight on a specific model. Our movement data are represen- ilarly prepared. DNA isolations were performed using the tative of passenger and crew movements in a single aisle Power Soil kit (MoBio Laboratories, Carlsbad, CA) according B3+3^ economy cabin configuration. to the manufacturer’s directions with an elution volume of Weiss H. et al. 50 μl. The 16S rRNA gene was amplified for sequencing References using the 515F primer (5′ GTGCCAGCMGCCGCGGTAA 3′) and 806R primer (5′ GGACTACHVGGGTWTCTAAT 1. Mangili A, Gendreau MA (2005) Transmission of infectious diseases during commercial air travel. Lancet 365(9463):989– 3′). The 16S rRNA gene-specific primers were tailed with 996. https://doi.org/10.1016/s0140-6736(05)71089-8 Illumina adaptor sequences to allow a secondary PCR to add 2. Wilson ME (1995) Travel and the emergence of infectious diseases. indexing barcodes and full Illumina adaptor sequences to sup- Emerg Infect Dis 1(2):39–46 port paired-end sequencing. Libraries were pooled for se- 3. Baker MG, Thornley CN, Mills C, Roberts S, Perera S, Peters J, quencing in batch sizes of 48 samples per batch and se- Kelso A, Barr I, Wilson N (2010) Transmission of pandemic A/ H1N1 2009 influenza on passenger aircraft: retrospective cohort quenced on the Illumina MiSeq at HudsonAlpha study. Br Med J 340:c2424. https://doi.org/10.1136/bmj.c2424 Biosciences. Paired-end sequencing with a read length of 4. Foxwell AR, Roberts L, Lokuge K, Kelly PM (2011) Transmission 150 bases per read was used, providing a small overlap at of influenza on international flights, may 2009. Emerg Infect Dis the end of each read to facilitate assembly of the paired-end 17(7):1188–1194. https://doi.org/10.3201/eid1707.101135 sequencing reads to a single fragment of ~ 290 bp representing 5. Kim JH, Lee D-H, Shin S-S, Kang C, Kim JS, Jun BY, Lee J-K (2010) In-flight transmission of novel influenza A (H1N1). the V4 region of the 16S rRNA gene. In reality, the reverse Epidemiol Health read was of very low quality preventing assembly for forward 6. Ooi PL, Lai FYL, Low CL, Lin R, Wong C, Hibberd M, Tambyah and reverse reads. Therefore, only quality trimmed forward PA (2010) Clinical and molecular evidence for transmission of nov- reads were used for all downstream analyses. The 16S se- el influenza A(H1N1/2009) on a commercial airplane. Arch Intern Med 170(10):913–915 quence data have been deposited in the National Center for 7. Young N, Pebody R, Smith G, Olowokure B, Shankar G, Hoschler Biotechnology Information (NCBI) database on BioProject K, Galiano M, Green H, Wallensten A, Hogan A, Oliver I (2014) accession number: PRJNA420089 and at the Sequence Read International flight-related transmission of pandemic influenza Archive (SRA) under Accession IDs SRR6330835– A(H1N1)pdm09: an historical cohort study of the first identified cases in the United Kingdom. Influenza Other Respir Viruses SRR6330871. 8(1):66–73. https://doi.org/10.1111/irv.12181 Reads were de-multiplexed according to the barcodes and 8. Hoad VC, O'Connor BA, Langley AJ, Dowse GK (2013) trimmed of barcodes and adapters. Following the initial pro- Risk of measles transmission on aeroplanes: Australian ex- cessing of the sequence data, sequences were combined, perience 2007-2011. Med J Aust 198(6):320–323. https://doi. dereplicated, and aligned in mothur (version 1.36.1)  org/10.5694/mja12.11752 9. Slater P, Anis E, Bashary A (1995) An outbreak of measles using the SILVA template (SSURef_NR99_123) ; subse- associated with a New York/Tel Aviv flight. Travel. Med quently, sequences were organized into clusters of representa- Int 199(13):92–95 tive sequences based on taxonomy called operational taxo- 10. O'Connor BA, Chang KG, Binotto E, Maidment CA, Maywood P, nomic units (OTU) using the UPARSE pipeline . Initial McAnulty JM (2005) Meningococcal disease—probably transmis- sion during an international flight. Commun Dis Intell 29(3) filtering of the samples ensured discarding OTUs containing 11. Kirking HL, Cortes J, Burrer S, Hall AJ, Cohen NJ, Lipman H, Kim less than five sequences. Libraries were normalized using C, Daly ER, Fishbein DB (2010) Likely transmission of norovirus metagenomeSeq’s cumulative sum scaling method to on an airplane, October 2008. Clin Infect Dis 50(9):1216–1221. account for library size acting as a confounding factor https://doi.org/10.1086/651597 for the beta diversity analysis. Moreover, in addition to 12. Desenclos JC, van der Werf S, Bonmarin I, Levy-Bruhl D, Yazdanpanah Y, Hoen B, Emmanuelli J, Lesens O, Dupon M, discarding singletons, OTUs that were observed fewer Natali F, Michelet C, Reynes J, Guery B, Larsen C, Semaille C, than seven times in the count data were also filtered Mouton Y, Christmann D, Andre M, Escriou N, Burguiere A, out to avoid the inflation of any contaminants that might skew Manuguerra JC, Coignard B, Lepoutre A, Meffre C, Bitar D, the diversity estimates. Decludt B, Capek I, Antona D, Che D, Herida M, Infuso A, Saura C, Brucker G, Hubert B, LeGoff D, Scheidegger S (2004) Introduction of SARS in France, March-April, 2003. Emerg. Infect. Funding Information This work was supported by contract 2001-041-1 Dis. 10(2):195–200 between The Georgia Institute of Technology and The Boeing Company. 13. Olsen SJ, Chang H, Cheung TY, Tang AF, Fisk TL, Ooi SP, Kuo H, Jiang DD, Chen K, Lando J, Hsu K, Chen T, Dowell SF (2003) Compliance with Ethical Standards Transmission of the severe acute respiratory syndrome on aircraft. N Engl J Med 349(25):2416–2422. https://doi.org/10.1056/ Conflict of Interest The authors declare that they have no conflict of NEJMoa031349 interest. 14. Hedberg CW, Levine WC, White KE, Carlson RH, Winsor DK, Cameron DN, Macdonald KL, Osterholm MT (1992) An inter- national foodborne outbreak of shigellosis associated with a Open Access This article is distributed under the terms of the Creative commercial airline. J Am Med Assoc 268(22):3208–3212. Commons Attribution 4.0 International License (http:// https://doi.org/10.1001/jama.268.22.3208 creativecommons.org/licenses/by/4.0/), which permits unrestricted use, 15. EberhartPhillips J, Besser RE, Tormey MP, Feikin D, Araneta MR, distribution, and reproduction in any medium, provided you give Wells J, Kilman L, Rutherford GW, Griffin PM, Baron R, Mascola appropriate credit to the original author(s) and the source, provide a link L (1996) An outbreak of cholera from food served on an interna- to the Creative Commons license, and indicate if changes were made. tional aircraft. Epidemiol Infect 116(1):9–13 The Airplane Cabin Microbiome 16. Kenyon TA, Valway SE, Ihle WW, Onorato IM, Castro KG (1996) 36. Gaüzère C, Moletta-Denat M, Blanquart H, Ferreira S, Moularat S, Godon JJ, Robine E (2014) Stability of airborne microbes in the Transmission of multidrug-resistant Mycobacterium tuberculosis during a long airplane flight. N Engl J Med 334(15):933–938. Louvre Museum over time. Indoor Air 24(1):29–40 https://doi.org/10.1056/nejm199604113341501 37. Rintala H, Pitkäranta M, Toivola M, Paulin L, Nevalainen A (2008) 17. Miller MA, Valway S, Onorato IM (1996) Tuberculosis risk Diversity and seasonal dynamics of bacterial community in indoor after exposure on airplanes. Tuber Lung Dis 77(5):414–419. environment. BMC Microbiol 8(1):56 https://doi.org/10.1016/s0962-8479(96)90113-6 38. Hoisington A, Maestre J, Kinney K, Siegel J (2015) Characterizing 18. Wang PD (2000) Two-step tuberculin testing of passengers and the bacterial communities in retail stores in the United States. crew on a commercial airplane. Am J Infect Control 28(3):233– Indoor Air 238. https://doi.org/10.1067/mic.2000.103555 39. Robertson CE, Baumgartner LK, Harris JK, Peterson KL, Stevens 19. Shankar AG, Janmohamed K, Olowokure B, Smith GE, Hogan MJ, Frank DN, Pace NR (2013) Culture-independent analysis of AH, De Souza V, Wallensten A, Oliver I, Blatchford O, Cleary P, aerosol microbiology in a metropolitan subway system. Appl. Ibbotson S (2014) Contact tracing for influenza A(H1N1)pdm09 Environ. Microbiol. 79(11):3485–3493 virus-infected passenger on international flight. Emerg Infect Dis 40. Afshinnekoo E, Meydan C, Chowdhury S, Jaroudi D, 20(1):118–120. https://doi.org/10.3201/eid2001.120101 Boyer C, Bernstein N, Maritz JM, Reeves D, Gandara J, 20. Regan JJ, Jungerman R, Montiel SH, Newsome K, Objio T, Chhangawala S (2015) Geospatial resolution of human and Washburn F, Roland E, Petersen E, Twentyman E, Olaiya O bacterial diversity with city-scale metagenomics. Cell Syst (2015) Public health response to commercial airline travel of a 1(1):72–87 person with Ebola virus infection—United States, 2014. MMWR 41. Hsu T, Joice R, Vallarino J, Abu-Ali G, Hartmann EM, Shafquat A, Morb Mortal Wkly Rep. 64(3):63–66 DuLong C, Baranowski C, Gevers D, Green JL (2016) Urban 21. Friedman S (2009) Flying the filthy skies. http://www.nbcdfw.com/ transit system microbial communities differ by surface type news/local/Flying-the-Filthy-Skies-72143422.html. Accessed 21 and interaction with humans and the environment. mSystems Feb 2017 1(3):e00018-00016 22. FOX NEWS Travel (2015) The gross truth about germs and air- 42. Hertzberg V, Weiss H, Elon L, Si W, Norris S (2018) Behaviors, planes. http://www.foxnews.com/travel/2015/06/02/gross-truth- movements, and transmission of droplet-mediated respiratory dis- about-germs-and-airplanes.html.Accessed2June 2017 eases during transcontinental airline flights. Proceedings of the 23. La Duc MT, Stuecker T, Venkateswaran K (2007) Molecular bac- National Academy of Sciences (e-print ahead of publication): terial diversity and bioburden of commercial airliner cabin air. Can. 201711611 J. Microbiol. 53(11):1259–1271 43. O’Brien JD, Record N, Countway P (2016) The power and pit- 24. McKernan LT, Wallingford KM, Hein MJ, Burge H, Rogers CA, falls of Dirichlet-multinomial mixture models for ecological Herrick R (2008) Monitoring microbial populations on wide-body count data. bioRxiv commercial passenger aircraft. Ann Occup Hyg 52(2):139–149 44. Checinska A, Probst AJ, Vaishampayan P, White JR, Kumar D, 25. McManus C, Kelley S (2005) Molecular survey of aeroplane bac- Stepanov VG, Fox GE, Nilsson HR, Pierson DL, Perry J (2015) terial contamination. J Appl Microbiol 99(3):502–508 Microbiomes of the dust particles collected from the International 26. Osman S, La Duc MT, Dekas A, Newcombe D, Venkateswaran K Space Station and Spacecraft Assembly Facilities. Microbiome (2008) Microbial burden and diversity of commercial airline cabin 3(1):50 air during short and long durations of travel. ISME J 2(5):482–497 45. DeLeon-Rodriguez N, Lathem TL, Rodriguez-R LM, Barazesh JM, 27. Qian J, Hospodsky D, Yamamoto N, Nazaroff WW, Peccia J (2012) Anderson BE, Beyersdorf AJ, Ziemba LD, Bergin M, Nenes Size-resolved emission rates of airborne bacteria and fungi in an A, Konstantinidis KT (2013) Microbiome of the upper tro- occupied classroom. Indoor Air 22(4):339–351 posphere: species composition and prevalence, effects of 28. Kembel SW, Jones E, Kline J, Northcutt D, Stenson J, Womack tropical storms, and atmospheric implications. Proc Natl AM, Bohannan BJ, Brown G, Green JL (2012) Architectural design Acad Sci 110(7):2575–2580 influences the diversity and structure of the built environment 46. Amato P, Joly M, Besaury L, Oudart A, Taib N, Moné AI, microbiome. ISME J 6(8):1469–1479 Deguillaume L, Delort A-M, Debroas D (2017) Active microor- 29. Meadow JF, Altrichter AE, Kembel SW, Moriyama M, O’Connor ganisms thrive among extremely diverse communities in cloud wa- TK, Womack AM, Brown G, Green JL, Bohannan BJ (2014) ter. PLoS One 12(8):e0182869 Bacterial communities on classroom surfaces vary with human con- 47. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, tact. Microbiome 2(1):7 Fierer N, Owens SM, Betley J, Fraser L, Bauer M (2012) Ultra- 30. Jeon Y-S, Chun J, Kim B-S (2013) Identification of household high-throughput microbial community analysis on the Illumina bacterial community and analysis of species shared with human HiSeq and MiSeq platforms. ISME J 6(8):1621–1624 microbiome. Curr Microbiol 67(5):557–563 48. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, 31. Dunn RR, Fierer N, Henley JB, Leff JW, Menninger HL (2013) Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Home life: factors structuring the bacterial diversity found within Robinson CJ (2009) Introducing mothur: open-source, plat- and between homes. PLoS One 8(5):e64133 form-independent, community-supported software for de- 32. Flores GE, Bates ST, Caporaso JG, Lauber CL, Leff JW, Knight R, scribing and comparing microbial communities. Appl Fierer N (2013) Diversity, distribution and sources of bacteria in Environ Microbiol 75(23):7537–7541 residential kitchens. Environ Microbiol 15(2):588–596 49. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, 33. Chase J, Fouquier J, Zare M, Sonderegger DL, Knight R, Kelley Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribo- ST, Siegel J, Caporaso JG (2016) Geography and location are the somal RNA gene database project: improved data process- primary drivers of office microbiome composition. mSystems 1(2): ing and web-based tools. Nucleic Acids Res 41(D1): e00022-00016 D590–D596 34. Hewitt KM, Gerba CP, Maxwell SL, Kelley ST (2012) Office space 50. Edgar RC (2013) UPARSE: highly accurate OTU sequences from bacterial abundance and diversity in three metropolitan areas. PLoS microbial amplicon reads. Nat Methods 10(10):996–998 One 7(5):e37849 51. Paulson JN, Stine OC, Bravo HC, Pop M (2013) Robust methods 35. Kelley ST, Gilbert JA (2013) Studying the microbiology of the for differential abundance analysis in marker gene surveys. Nat indoor environment. Genome Biol 14(2):202 Methods 10(12):1200–1202
Microbial Ecology – Springer Journals
Published: Jun 6, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera