A microbial survey of the Boston subway system suggests that the profiling of microbial ridership by surface type—seats, hanging grips, ticket machine touchscreens—could allow public health professionals to anticipate disease outbreaks or sanitation events. This kind of information could also be used to improve the design of healthy transit environments.

 


The ridership patterns for mass transit systems need to be more closely scrutinized—not the comings and goings of human passengers, but rather the aggregations and dispersals of microbial commuters. Otherwise, it might be peak hour for antibiotic resistance, and we would have no way of knowing.

Stepping lively across this knowledge gap are scientists based at the Harvard T.H. Chan School of Public Health. They entered the Boston subway system, where they proceeded to collect samples by swabbing seats, seat backs, walls, vertical and horizontal poles, and hanging grips inside train cars from three subway lines, as well as touchscreens and walls of indoor and outdoor ticketing machines at five subway stations.

The samples were then given an express ride to the laboratory, where metagenomic sequencing was performed. At the end of the line, the Harvard scientists declared that they had completed the first high-precision microbial survey of a variety of surfaces, ridership environments, and microbial functions (including tests for pathogenicity) in a mass transit environment.

The survey results suggest the value of characterizing microbial profiles for multiple transit systems. According to the scientists, such profiles will become increasingly important for biosurveillance of antibiotic resistance genes or pathogens, which can be early indicators for outbreak or sanitation events.

Details of the survey appeared June 28 in the journal mSystems, in an article entitled, “Urban Transit System Microbial Communities Differ by Surface Type and Interaction with Humans and the Environment.” The article described how 16S amplicon and shotgun sequencing were used to characterize the microbial community composition, gauge functional capacity, and assess pathogentic potential of the Boston mass transit system.

The researchers found that the type of surface—and how humans interact with it—was the greatest determinant of microbial community structure. Skin- and oral-associated microbes—transferred by touching and coughing or sneezing—were found on surfaces such as poles and hand grips. Vaginal microbes, which can be transferred through clothing, were found on seats. Greater amounts of nonhuman microbes, such as those seen in plants, were found on outdoor ticketing touchscreens. Little variation was observed between geographically distinct train lines and stations serving different demographics.

The findings are consistent with previous microbial DNA sequencing-based studies that have revealed that microbial communities in the built environment are greatly influenced by their human occupants. Further study of the separate influences of human contact, surface type, and surface material will help identify mechanisms through which microbial communities form and persist on surfaces within built environments.

“Understanding how human contact, materials, and the environment affect microbial profiles may eventually allow us to rationally design public spaces to sustain our health in the presence of microbial reservoirs,” the article’s authors added.

“We were surprised to find that the microbes that we collected on surfaces that people touch—and sometimes sneeze on—had low numbers of worrisome pathogens or antibiotic resistance genes. These environments have drastically lower virulence profiles, in fact, than are observed in a typical human gut,” said senior author Curtis Huttenhower, associate professor of computational biology and bioinformatics. “Our findings establish a baseline against which deviations can be used as an early warning system to monitor public health.”

“Our next steps are to find out which microbes are dead or alive and which can be transferred between people,” added first author Tiffany Hsu, a research assistant in the Department of Biostatistics.







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