Ozone Disinfection for Elimination of Bacteria and Degradation of SARS-CoV2 RNA for Medical Environments

Pathogenic bacteria and viruses in medical environments can lead to treatment complications and hospital-acquired infections. Current disinfection protocols do not address hard-to-access areas or may be beyond line-of-sight treatment, such as with ultraviolet radiation. The COVID-19 pandemic further underscores the demand for reliable and effective disinfection methods to sterilize a wide array of surfaces and to keep up with the supply of personal protective equipment (PPE). We tested the efficacy of Sani Sport ozone devices to treat hospital equipment and surfaces for killing Escherichia coli, Enterococcus faecalis, Bacillus subtilis, and Deinococcus radiodurans by assessing Colony Forming Units (CFUs) after 30 min, 1 h, and 2 h of ozone treatment. Further gene expression analysis was conducted on live E. coli K12 immediately post treatment to understand the oxidative damage stress response transcriptome profile. Ozone treatment was also used to degrade synthetic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA as assessed by qPCR CT values. We observed significant and rapid killing of medically relevant and environmental bacteria across four surfaces (blankets, catheter, remotes, and syringes) within 30 min, and up to a 99% reduction in viable bacteria at the end of 2 h treatment cycles. RNA-seq analysis of E. coli K12 revealed 447 differentially expressed genes in response to ozone treatment and an enrichment for oxidative stress response and related pathways. RNA degradation of synthetic SARS-CoV-2 RNA was seen an hour into ozone treatment as compared to non-treated controls, and a non-replicative form of the virus was shown to have significant RNA degradation at 30 min. These results show the strong promise of ozone treatment of surfaces for reducing the risk of hospital-acquired infections and as a method for degradation of SARS-CoV-2 RNA

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.