The Airplane Cabin Microbiome

Abstract

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 signatures 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.

Shotgun metagenomics of dust microbiome from flight deck and cabin in civil aviation aircraft

“…We collected settled dust in this study, differing from sampling strategies in other aircraft studies, including cabin air, swab, and HEPA filter. [20][21][22] Each sampling strategy has its strengths and limitations. Air sampling in aircraft represents short-term microbial exposure, and settled dust sampling represents long-term exposure for passengers and crew members.…”

“…The approach can only cultivate roughly 1% of bacterial species, 18,19 and the percentage could be even less for aerosolized Gram‐negative bacteria 20 . In recent years, culture‐free techniques, including microarray or 16S ribosomal amplicon sequencing, have been applied and greatly expand microbial detection limits in commercial aircraft 20–22 . In these studies, the most abundant bacteria were mainly commensal taxa mainly derived from human skin, oral, and outdoor environments, including water or soil habitat, and potential human pathogens were also identified.…”

Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research

“…The study of bioaerosols is an emerging and expanding research discipline [1], with several important study applications, including surveillance of clinically relevant microbes [2][3][4][5], air quality monitoring [6][7][8] and biodefense [9]. Bioaerosol research has traditionally relied on culture methods; however, not all microorganisms grow under standard laboratory conditions, resulting in underrepresentation of the true microbial diversity [10][11][12][13].…”

Applications of weighted association networks applied to compositional data in biology

“…transcripts, 16/18SS rRNA, marker genes) within a community by sampling from a pool of nucleic acid fragments. A typical NGS 16SrRNA experiment consists of (i) sequencing 16S rRNA amplicons at a specified sequencing depth from environmental samples (Caporaso et al ., 2011; Logares et al ., 2020), human tissues/biofluids (Goodrich et al ., 2016; Gomez et al ., 2017; Voorhies et al ., 2019) or built environments (Weiss et al ., 2018; Checinska Sielaff et al ., 2019); (ii) clustering highly similar sequences into operational taxonomic units (OTU) as a representative for closely related taxa (Sneath and Sokal, 1962; Schloss et al ., 2009; Edgar, 2013) or using amplicon sequencing variants (ASV) (Callahan et al ., 2016; Amir et al ., 2017); (iii) generating abundance tables by counting NGS reads mapped to ecological units (e.g. OTU or ASV); and (iv) analysing the resulting abundance tables such as alpha/beta‐diversity, differential abundance or network analysis.…”