BioinformaticsMicrobiomeScientificDetection of Antimicrobial Resistance Genes on Mobile Phones in a Healthcare Setting Using the CosmosID-HUB Microbiome

6 July 2022by Dana Walsh0

Mobile phones have become essential to modern-day living; they are the main communication device for two-thirds of the human population (Kemp 2020). People handle their mobile phones up to 2000 times per day, which can total up to 3h and 37 minutes (Winnick 2016). Consequently, mobile phones have emerged as a fomite that could play a role in transmitting disease, as they could bypass “gold standard” hand hygiene practices and lead to pathogen cross-contamination transmission (Olsen et al. 2020). Thus, mobile phones may lead to breaches in sterility practices in health care settings and disseminate microbes, good and bad. To monitor the risk of pathogen transmission, Olsen et al. (2022) collected microbiome samples from 26 mobile phones of health care staff, sequenced the samples using whole genome shotgun sequencing, and uploaded the sample FASTQ files to the CosmosID-HUB Microbiome for taxonomic, antimicrobial resistance and virulence factor analyses. The key findings from the HUB are as follows:

  • All the 26 tested mobile phones harbored microbes that contained antibiotic resistance and virulence factors
  • The 26 mobile phones contained 11,163 organisms (5741, bacteria, 675 fungi, 93, protists, 228 viruses, 4453 bacteriophages)
  • The diversity of the antibiotic resistance and virulence factors represented 2096 different genes
  • Mobile phones appear to be serious vectors for microbes; contaminated mobile phones could pose a public health concern

Dana Walsh

Dana Walsh is a microbiome scientist whose career has consisted of a blend of wet bench and computational research. Currently, she is a Microbiome Scientist for CosmosID where she helps clients with custom microbiome analysis and interpretation as well as exploring new tools and methods for microbiome studies. She applies cutting-edge tools to integrate multi-omics data, including taxonomic, functional, meta-transcriptomics and metabolomics data, in order to help clients derive meaning from their results.

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