Bacteriophages (phages), or bacterial viruses, are very diverse and highly abundant worldwide, including as a part of the human microbiomes. Although a few metagenomic studies have focused on oral phages, they relied on short-read sequencing. Here, we conduct a long-read metagenomic study of human saliva using PromethION. Our analyses, which integrate both PromethION and HiSeq data of >30 Gb per sample with low human DNA contamination, identify hundreds of viral contigs; 0–43.8% and 12.5–56.3% of the confidently predicted phages and prophages, respectively, do not cluster with those reported previously. Our analyses demonstrate enhanced scaffolding, and the ability to place a prophage in its host genomic context and enable its taxonomic classification. Our analyses also identify a Streptococcus phage/prophage group and nine jumbo phages/prophages. 86% of the phage/prophage group and 67% of the jumbo phages/prophages contain remote homologs of antimicrobial resistance genes. Pan-genome analysis of the phages/prophages reveals remarkable diversity, identifying 0.3% and 86.4% of the genes as core and singletons, respectively. Furthermore, our study suggests that oral phages present in human saliva are under selective pressure to escape CRISPR immunity. Our study demonstrates the power of long-read metagenomics utilizing PromethION in uncovering bacteriophages and their interaction with host bacteria.

The Japanese Nosocomial Infections Surveillance (JANIS) is one of the largest national antimicrobial resistance (AMR) surveillance systems in the world. In particular, the JANIS Clinical Laboratory (CL) division collects comprehensive specimen-based data from diagnostic microbiology laboratories of participating hospitals to monitor the isolation rate of 11 major bacteria and specific AMR bacteria, and creating antibiograms of approximately 20 bacterial species. Data in the JANIS web-database system are also annually tabulated and provided to the WHO Global Antimicrobial Resistance Surveillance System (GLASS). To create a network of international AMR surveillance systems among Asian countries, Japan is developing an international web-database system named ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net) based on the JANIS system, but is open-source and available confidentially at almost no cost. JANIS further continues to evolve in multiple directions, some of which are selected and discussed in end of this review.

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Background The impact of the coronavirus disease (COVID-19) pandemic on antimicrobial resistance (AMR) is a major concern. Aim To compare the number of patients and isolation rate of antimicrobial-resistant bacteria before and after the beginning of the COVID-19 pandemic using the comprehensive national surveillance data. Methods We utilized comprehensive surveillance data, collected in the Japan Nosocomial Infections Surveillance program, which included a total of 16.7 million samples of 5.9 million tested patients from >1,300 hospitals. We compared the number of patients and isolation rate of five bacteria between 2019 and 2020, including antimicrobial-susceptible and -resistant bacteria of Staphylococcus aureus , Streptococcus pneumoniae , Escherichia coli, Klebsiella pneumoniae , and Pseudomonas aeruginosa . Findings The number of patients and isolation rate of S. aureus and methicillin-resistant S. aureus decreased slightly; those of S. pneumoniae and penicillin-resistant S. pneumoniae decreased by 60%; and those of third-generation cephalosporin-resistant K. pneumoniae increased. The isolation rate of the remaining bacteria apparently increased, although the number of patients decreased. This was due to a substantial decrease in the total number of tested patients (the denominator of the isolation rate), which was larger than that of the number of patients (the numerator of the isolation rate). Consistent results were obtained when the same data were re-aggregated using the procedure of the World Health Organization Global Antimicrobial Resistance Surveillance System, demonstrating the general importance of this problem. Conclusion Surveillance data during the COVID-19 pandemic must be carefully interpreted based on examination of the numerator, denominator and background factors that affect the denominator.