Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: The 16S rRNA-targeted next-generation sequencing (NGS) has been widely used as the primary tool for microbiome analysis. However, whether the sequenced microbial diversity absolutely represents the original sample composition remains unclear. This study aimed to evaluate whether 16S rRNA gene-targeted NGS accurately captures bacterial community composition. Methods: Mock communities were constructed using equal amounts of DNA from 18 bacterial strains in three formats: genomic DNA, recombinant plasmids, and polymerase chain reaction (PCR) templates. The V3V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq. Results: Data regression analysis revealed that the recombinant plasmid produced more accurate and precise correlation curve than that by the gDNA and PCR products, with a slope closest to 1 (1.0082) and the highest R² value (0.9975). Despite the same input amount of bacterial DNA, the NGS read distribution varied across all three mock communities. Using multiple regression analysis, we found that the guanine-cytosine (GC) content of the V3V4 region, 16S rRNA gene, size of gDNA, and copy number of 16S rRNA were significantly associated with the NGS output of each bacterial species. Conclusion This study demonstrated that recombinant plasmids are the preferred option for quality control and that NGS output is biased owing to certain bacterial characteristics, such as %GC content, gDNA size, and 16S rRNA gene copy number. Further research is required to develop a system that compensates for NGS process biases using mock communities.

Background: Bacteroides fragilis group (BFG) isolates are the most frequently isolated gram-negative anaerobic bacteria and exhibit higher levels of antimicrobial resistance than other anaerobic bacteria. Reliable susceptibility testing is needed because of reports of resistance to the most active antibiotics. Recently, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) introduced disk zone diameter breakpoints. We evaluated the disk diffusion test (DDT) for susceptibility testing of BFG isolates compared with the agar dilution method. Methods: In total, 150 BFG isolates were collected from three institutes in Korea. The agar dilution method was conducted according to the CLSI guidelines. DDT was performed following the EUCAST guideline. Fastidious anaerobe agar supplemented with 5% defibrinated horse blood was used as the culture medium. Nine antimicrobials were evaluated:penicillin, cefoxitin, cefotetan, imipenem, meropenem, piperacillin-tazobactam, clindamycin, moxifloxacin, and metronidazole. Results: The categorical agreement (CA) between the two methods was > 90.0% for imipenem, meropenem, clindamycin, and metronidazole. However, the CA for piperacillintazobactam was low, at 83.2%. Major errors were found: 5.4% for imipenem, 7.4% for meropenem, and 12.8% for piperacillin-tazobactam. All minor errors were < 10%. We propose using the area of technical uncertainty (ATU) zone-overlapping area for susceptible and resistant strains to reduce errors in the DDT. Outside the ATU, the CAs of cefoxitin, cefotetan, and piperacillin-tazobactam were > 90.0%, whereas that of moxifloxacin was increased to 88.5%. Conclusions The DDT can be a useful alternative antimicrobial susceptibility test for BFG isolates when using the ATU zone to reduce errors.

Novel antimicrobial agents are continually developed to address the global threat of multidrug-resistant Neisseria gonorrhoeae. Promising candidates include zoliflodacin and, possibly, solithromycin. We evaluated their in-vitro activities against gonococcal isolates collected in Korea. In total, 250 N. gonorrhoeae isolates obtained across Korea between 2016 and 2018 were used to determine the minimum inhibitory concentrations (MICs) of 10 therapeutic agents using the CLSI agar dilution method. Most isolates (94.8%, 237/250) demonstrated non-susceptibility to penicillin G, tetracycline, and ciprofloxacin, and susceptibility to ceftriaxone and spectinomycin was substantially high. The half-maximal IC (MIC50) and 90% IC (MIC90) values for zoliflodacin were 0.03 and 0.06 µg/mL, respectively; 0.06 and 0.12 µg/mL, respectively, for solithromycin; and 0.03 and 0.12 µg/mL, respectively, for ceftriaxone. Notably, no cross-resistance was observed between zoliflodacin and ciprofloxacin, despite both targeting DNA topoisomerase II enzymes. Zoliflodacin and solithromycin demonstrated significant in-vitro activity against multidrug-resistant N.gonorrhoeae isolates, and zoliflodacin has shown non-inferiority to ceftriaxone/azithromycin dual therapy in a clinical phase 3 trial. Collectively, our findings highlight the potential of zoliflodacin as a novel therapeutic agent for gonococcal infections, particularly in the context of rising multidrug resistance, and highlight the need for continued surveillance and development of alternative antimicrobial strategies.

Background: There is difference in the incidence of multi-system inflammatory syndrome in children (MIS-C) in patients with different variants of severe acute respiratory syndrome coronavirus 2, however, little is known about the epidemiology in Asian countries. We investigated and compared the epidemiology of the MIS-C during omicron-dominant period with that of previous periods in South Korea. Methods: We obtained clinical, epidemiological and laboratory data on MIS-C cases from national MIS-C surveillance in South Korea. We defined pre-delta period as January 2020–May 2021; delta period as June 2021–December 2021; and omicron period as January 2022–April 2022. We describe the clinical characteristics and outcomes of MIS-C patients by period. Results: A total of 91 cases were assessed to be MIS-C cases. Number of MIS-C cases have increased from six cases during pre-delta period to 66 cases during omicron period, while the incidence rate (the number of MIS-C cases per 100,000 cases of reported coronavirus disease 2019) has decreased from 38.5 cases per 100,000 (95% confidence interval [CI], 14.1–83.9) during pre-delta period to 1.6 cases per 100,000 (95% CI, 1.2–2.0) during omicron periods. During pre-delta period, 66.7% and 100% had hypotension and gastrointestinal involvement, respectively; while during omicron period, 12.1% and 6.1% had such clinical manifestations. Fifty percent of pre-delta MIS-C patients were taken intensive care unit (ICU) cares, while 10.6% of patients during omicron periods were in ICUs. Conclusion Omicron period were associated with less severe clinical manifestation compared to pre-delta and delta periods. Although incidence rate of MIS-C was lower for the omicron period than pre-delta and delta periods, number of patients reported with MIS-C may pose a substantial clinical burden.

Background: In Korea, during the early phase of the coronavirus disease 2019 (COVID-19) pandemic, we responded to the uncertainty of treatments under various conditions, consistently playing catch up with the speed of evidence updates. Therefore, there was high demand for national-level evidence-based clinical practice guidelines for clinicians in a timely manner. We developed evidence-based and updated living recommendations for clinicians through a transparent development process and multidisciplinary expert collaboration. Methods: The National Evidence-based Healthcare Collaborating Agency (NECA) and the Korean Academy of Medical Sciences (KAMS) collaborated to develop trustworthy Korean living guidelines. The NECA-supported methodological sections and 8 professional medical societies of the KAMS worked with clinical experts, and 31 clinicians were involved annually. We developed a total of 35 clinical questions, including medications, respiratory/critical care, pediatric care, emergency care, diagnostic tests, and radiological examinations. Results: An evidence-based search for treatments began in March 2021 and monthly updates were performed. It was expanded to other areas, and the search interval was organized by a steering committee owing to priority changes. Evidence synthesis and recommendation review was performed by researchers, and living recommendations were updated within 3–4 months. Conclusion We provided timely recommendations on living schemes and disseminated them to the public, policymakers and various stakeholders using webpages and social media.Although the output was successful, there were some limitations. The rigor of development issues, urgent timelines for public dissemination, education for new developers, and spread of several new COVID-19 variants have worked as barriers. Therefore, we must prepare systematic processes and funding for future pandemics.