Antibiotic resistance associated with air quality and transferred by airborne particulate matter

Abstract

Background: Environmental spread of antibiotic resistance has become a public health problem, but the relationship between air quality and antibiotic resistance has not been fully investigated, the effect and mechanism of air pollutants on horizontal genes transfer of antibiotic resistance are rarely mentioned. Results: In the present study, significant correlation ( P < 0.05) between resistance rates of 10 clinical antibiotic-resistant pathogens and number of days when air quality index was below 100 were observed in an extensive survey from 2014 to 2017 in China. In addition, characterization of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacterial communities in airborne particulate matter (PM), dust, and human airway sputum samples were profiled in a hospital. PM 2.5 and PM 10 were shown to contain ARGs and MGEs high relative abundance and diversity index. Importantly, transferable multi-resistant plasmids were identified from air, e.g., pTAir-3 with 26 MGEs and 10 ARGs, using conjugative mating assays and nanopore sequencing . Furthermore, PM 2.5 and PM 10 significantly enhanced the conjugative transfer frequencies between bacteria, via inducing increased levels of reactive oxygen species and cell membrane permeability; upregulated gene expression levels were confirmed by genome-wide RNA sequencing. Conclusions: These findings provide a new perspective on antibiotic resistance research and have profound implications for antibiotic resistance control in clinics and environments.

Genetic Identification of Superbugs from River Streams in State University of Malang

“…Superbug have also reported to be found in the air environment of hospitals, livestock sector, and sewage treatment plants (Zhou et al, 2020). In the aquatic environment, superbugs can also be found due to the fact that in many areas, water bodies become the estuaries of domestic waste (Puspita et al, 2016), hospital waste (Sarkar et al, 2019), agricultural waste (Habibi et al, 2014), mining waste (Mulyadi et al, 2020), and industrial wastewater (Rahardjo & Prasetyaningsih, 2021).…”