Ruth M Hall1, Stefan Schwarz2. 1. School of Molecular and Microbial Biosciences, The University of Sydney, Sydney, 2006 New South Wales, Australia ruth.hall@sydney.edu.au. 2. Institute of Farm Animal Genetics, Friedrich-Loeffler-Institut, Höltystr. 10, 31535 Neustadt-Mariensee, Germany.
Abstract
In the genomic era, studying the epidemiology of individual antibiotic resistance genes and resistance gene discovery are open to all. However, the identification and naming of resistance genes is not currently understandable by all owing to a plethora of competing nomenclature systems, many of which do not comply with the basic rules of bacterial gene nomenclature. Change is needed urgently. Here, we make a case for the resistance research community to begin this process by agreeing on an answer to the question of when a new gene number should be assigned. This cut-off is of necessity arbitrary and we suggest a threshold value of ≥2% difference in the sequences of the DNA, predicted protein or both as a realistic boundary for assigning a new gene number. This proposal can be a starting point for agreement or debate followed by renumbering of the affected gene families.
In the genomic era, studying the epidemiology of individual antibiotic resistance genes and resistance gene discovery are open to all. However, the identification and naming of resistance genes is not currently understandable by all owing to a plethora of competing nomenclature systems, many of which do not comply with the basic rules of bacterial gene nomenclature. Change is needed urgently. Here, we make a case for the resistance research community to begin this process by agreeing on an answer to the question of when a new gene number should be assigned. This cut-off is of necessity arbitrary and we suggest a threshold value of ≥2% difference in the sequences of the DNA, predicted protein or both as a realistic boundary for assigning a new gene number. This proposal can be a starting point for agreement or debate followed by renumbering of the affected gene families.
Authors: Sally R Partridge; Vincenzo Di Pilato; Yohei Doi; Michael Feldgarden; Daniel H Haft; William Klimke; Samir Kumar-Singh; Jian-Hua Liu; Surbhi Malhotra-Kumar; Arjun Prasad; Gian Maria Rossolini; Stefan Schwarz; Jianzhong Shen; Timothy Walsh; Yang Wang; Basil Britto Xavier Journal: J Antimicrob Chemother Date: 2018-10-01 Impact factor: 5.790
Authors: George A Jacoby; Robert A Bonomo; Patricia A Bradford; Karen Bush; Yohei Doi; Michael Feldgarden; Daniel Haft; William Klimke; Patrice Nordmann; Timothy Palzkill; Laurent Poirel; Arjun Prasad; Gian Maria Rossolini; Timothy Walsh Journal: J Antimicrob Chemother Date: 2016-06-03 Impact factor: 5.790
Authors: Noelle R Noyes; Xiang Yang; Lyndsey M Linke; Roberta J Magnuson; Shaun R Cook; Rahat Zaheer; Hua Yang; Dale R Woerner; Ifigenia Geornaras; Jessica A McArt; Sheryl P Gow; Jaime Ruiz; Kenneth L Jones; Christina A Boucher; Tim A McAllister; Keith E Belk; Paul S Morley Journal: Sci Rep Date: 2016-04-20 Impact factor: 4.379
Authors: Noelle R Noyes; Xiang Yang; Lyndsey M Linke; Roberta J Magnuson; Adam Dettenwanger; Shaun Cook; Ifigenia Geornaras; Dale E Woerner; Sheryl P Gow; Tim A McAllister; Hua Yang; Jaime Ruiz; Kenneth L Jones; Christina A Boucher; Paul S Morley; Keith E Belk Journal: Elife Date: 2016-03-08 Impact factor: 8.140
Authors: Paul S Morley; Keith E Belk; Margaret D Weinroth; H Morgan Scott; Bo Norby; Guy H Loneragan; Noelle R Noyes; Pablo Rovira; Enrique Doster; Xiang Yang; Dale R Woerner Journal: Appl Environ Microbiol Date: 2018-06-18 Impact factor: 4.792
Authors: Enrique Doster; Pablo Rovira; Noelle R Noyes; Brandy A Burgess; Xiang Yang; Margaret D Weinroth; Steven M Lakin; Christopher J Dean; Lyndsey Linke; Roberta Magnuson; Kenneth I Jones; Christina Boucher; Jamie Ruiz; Keith E Belk; Paul S Morley Journal: Front Microbiol Date: 2018-07-30 Impact factor: 5.640