Genotyping Oral Commensal Bacteria to Predict Social Contact and Structure.
Social network structure is a fundamental determinant of human health, from infectious to chronic diseases. However, quantitative and unbiased approaches to measuring social network structure are lacking. We hypothesized that genetic relatedness of oral commensal bacteria could be used to infer social contact between humans, just as genetic relatedness of pathogens can be used to determine transmission chains of pathogens. We used a traditional, questionnaire survey-based method to characterize the contact network of the School of Public Health at a large research university. We then collected saliva from a subset of individuals to analyze their oral microflora using a modified deep sequencing multilocus sequence typing (MLST) procedure. We examined micro-evolutionary changes in the S. viridans group to uncover transmission patterns reflecting social network structure. We amplified seven housekeeping gene loci from the Streptococcus viridans group, a group of ubiquitous commensal bacteria, and sequenced the PCR products using next-generation sequencing. By comparing the generated S. viridans reads between pairs of individuals, we reconstructed the social network of the sampled individuals and compared it to the network derived from the questionnaire survey-based method. The genetic relatedness significantly (p-value < 0.001) correlated with social distance in the questionnaire-based network, and the reconstructed network closely matched the network derived from the questionnaire survey-based method. Oral commensal bacterial are thus likely transmitted through routine physical contact or shared environment. Their genetic relatedness can be used to represent a combination of social contact and shared physical space, therefore reconstructing networks of contact. This study provides the first step in developing a method to measure direct social contact based on commensal organism genotyping, potentially capable of unmasking hidden social networks that contribute to pathogen transmission.
Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.|Division of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
Stephen Starko
SS
Francis
Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.|Department of Environmental Science and Policy Management, University of California, Berkeley, California, United States of America.
Mateusz M
MM
Plucinski
Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.
Amelia D
AD
Wallace
Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.|Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, California, United States of America.
Lee W
LW
Riley
D003141:Communicable Diseases / Q000235:genetics / Q000382:microbiology; D019058:Community Networks; D004269:DNA, Bacterial / Q000235:genetics; D005838:Genotype; D006801:Humans; D058885:Multilocus Sequence Typing / Q000379:methods; D016133:Polymerase Chain Reaction; D012463:Saliva / Q000382:microbiology*; D012919:Social Behavior; D013290:Streptococcal Infections / Q000235:genetics / Q000382:microbiology*; D034363:Viridans Streptococci / Q000235:genetics*
D004269:DNA, Bacterial
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