Due to the increasing abuse of antibiotics globally, antibiotic resistance genes (ARGs) were widely identified from a variety of environment samples like water and soil etc. to different microbial species such as Escherichia coli and Klebsiella pneumoniae, etc. Fast and efficient identification, determination, and profiling of antibiotic resistance in environmental samples, microbial genomes, and metagenomic data would greatly facilitate our understanding of the molecular mechanisms, environmental transmissions, and dynamic changes of antibiotic resistance. So far, many methods have been proposed to predict antibiotic resistance based on techniques such as sequences alignment and machine-learning, etc. Artificial intelligence (AI) has also been reported to find applications in this field. However, with the incessant cumulation of massively sequenced data and continuously emerging antibiotic resistance, novel and effective methodologies for ARGs prediction should be keeping proposed and tools for dynamically analysing and visualizing antibiotic resistance profiles should be updated and developed. In addition, although hundreds of thousands of microbial genomes have been sequences, there rarely is a focus on the construction of database with culture-based antibiotic resistance profiling, not even mentioning an integrative database between computationally predicted and experimentally validated ARGs for microbial pathogens. It has been noticed that environment is a reservoir of ARGs where microbes could obtain new antibiotic resistance phenotypes. Thus, investigation into the spatial and temporal distributions of antibiotic resistance profiles in the environmental samples would greatly facilitate our understanding of how ARGs change dynamically, which will provide practical guidelines for preventing the transmission of ARGs. Computational prediction of novel ARGs would also provide the first-hand information for designing novel drugs to combat microbial pathogens, which could give human beings advantages during the kill-or-cure arm race with microbes. Finally, the evolution and fitness costs of antibiotic resistance are also important in understanding the origin of ARGs and its interactions with the corresponding microbial hosts, the investigation of which could help human beings fight bacterial pathogens from a long-term perspective and finally win the war. Taken together, the proposed research topic welcome investigators to contribute with Original Research articles and Reviews describing recent findings which use mainly computational methodologies in the research of microbial antibiotics resistance.
Topics of interest are in particular:
· Computational methodology for antibiotic resistance predictions
· Software development for antibiotic resistance predictions
· Database construction of culture-based antibiotic resistance profiling
· Spatial and temporal distributions of antibiotic resistance profiles
· Development of novel antibiotics via computational methods
· Evolution and fitness costs of antibiotic resistance in bacteria
Due to the increasing abuse of antibiotics globally, antibiotic resistance genes (ARGs) were widely identified from a variety of environment samples like water and soil etc. to different microbial species such as Escherichia coli and Klebsiella pneumoniae, etc. Fast and efficient identification, determination, and profiling of antibiotic resistance in environmental samples, microbial genomes, and metagenomic data would greatly facilitate our understanding of the molecular mechanisms, environmental transmissions, and dynamic changes of antibiotic resistance. So far, many methods have been proposed to predict antibiotic resistance based on techniques such as sequences alignment and machine-learning, etc. Artificial intelligence (AI) has also been reported to find applications in this field. However, with the incessant cumulation of massively sequenced data and continuously emerging antibiotic resistance, novel and effective methodologies for ARGs prediction should be keeping proposed and tools for dynamically analysing and visualizing antibiotic resistance profiles should be updated and developed. In addition, although hundreds of thousands of microbial genomes have been sequences, there rarely is a focus on the construction of database with culture-based antibiotic resistance profiling, not even mentioning an integrative database between computationally predicted and experimentally validated ARGs for microbial pathogens. It has been noticed that environment is a reservoir of ARGs where microbes could obtain new antibiotic resistance phenotypes. Thus, investigation into the spatial and temporal distributions of antibiotic resistance profiles in the environmental samples would greatly facilitate our understanding of how ARGs change dynamically, which will provide practical guidelines for preventing the transmission of ARGs. Computational prediction of novel ARGs would also provide the first-hand information for designing novel drugs to combat microbial pathogens, which could give human beings advantages during the kill-or-cure arm race with microbes. Finally, the evolution and fitness costs of antibiotic resistance are also important in understanding the origin of ARGs and its interactions with the corresponding microbial hosts, the investigation of which could help human beings fight bacterial pathogens from a long-term perspective and finally win the war. Taken together, the proposed research topic welcome investigators to contribute with Original Research articles and Reviews describing recent findings which use mainly computational methodologies in the research of microbial antibiotics resistance.
Topics of interest are in particular:
· Computational methodology for antibiotic resistance predictions
· Software development for antibiotic resistance predictions
· Database construction of culture-based antibiotic resistance profiling
· Spatial and temporal distributions of antibiotic resistance profiles
· Development of novel antibiotics via computational methods
· Evolution and fitness costs of antibiotic resistance in bacteria
