作者
Xiaole Yin,Xi Chen,Xiaotao Jiang,Yongqiang Yang,Bing Li,Marcus Ho-Hin Shum,Tommy Tsan‐Yuk Lam,GM Leung,Joan B. Rose,Concepcion Sanchez-Cid,Timothy M. Vogel,Fiona Walsh,Thomas U. Berendonk,Janet Midega,Chibuzor Uchea,Dominic Frigon,Gerard D. Wright,Cornelius Carlos Bezuidenhout,Renata Cristina Picão,Shaikh Ziauddin Ahammad,Per Halkjær Nielsen,Philip Hugenholtz,Nicholas J. Ashbolt,Gianluca Corno,Despo Fatta‐Kassinos,Helmut Bürgmann,Heike Schmitt,Chang‐Jun Cha,Amy Pruden,Kornelia Smalla,Eddie Cytryn,Yu Zhang,Min Yang,Yong‐Guan Zhu,Arnaud Dechesne,Barth F. Smets,David W. Graham,Michael R. Gillings,William H. Gaze,Célia M. Manaia,Mark C.M. van Loosdrecht,Pedro J. J. Alvarez,Martin J. Blaser,James M. Tiedje,Edward Topp,Tong Zhang
摘要
Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.