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.

Fecal microbiota transplantation (FMT) involves the transfer of fecal microbiota from healthy donors to patients to rectify dysbiosis and restore the functionality of the gut microbiota to a healthy state. Donor selection is important to minimize the risk of FMT. Donor selection for FMT is primarily focused on screening for potential infections. A complete consensus on screening tests and checkpoints is lacking and controversial; nevertheless, most guidelines agree to rule out certain infections, such as syphilis; hepatitis A, B, and C; and HIV. In most guidelines, stool testing includes testing for Clostridioides difficile and other enteric pathogens. The Korean FMT guidelines for C. difficile infections were published in 2022. The guidelines recommend serological and stool testing for donor candidates, with recommendations for stool testing providing targets for screening using specific test methods. Donor screening by Microbiotix Inc., a fecal microbiota bank in Korea, between 2017 and 2023 showed that only 5% of potential FMT donors were eligible for repeat donation. The future of FMT remains uncertain, with possibilities ranging from continuation to restrictions, and the development of synthetic microbiota preparations. Legislative support is crucial for advancing this field and providing hope and a potential cure for previously incurable patients.

Background: Numerous studies have examined the prevalence of Helicobacter pylori infection and clarithromycin (CLA) resistance rate of H. pylori. However, in South Korea, there is a lack of research analyzing specimens from local clinics and hospitals using molecular methods. This study aimed to assess the prevalence of H. pylori infection and CLA resistance across sex and age groups, as well as to explore regional variations in CLA resistance and its characteristics. Methods: Data from a laboratory information system from 2015 to 2018 were retrospectively analyzed to determine the prevalence of H. pylori infection and CLA resistance rate. The 23S ribosomal RNA genes of H. pylori were analyzed using a dual priming oligonucleotide-based multiplex polymerase chain reaction method. Results: The overall prevalence of H. pylori infection was 50.5%(12,000/23,773), with a significantly higher prevalence among males (53.5%) than females (47.0%). The CLA resistance rate was 28.3%, with a significantly higher rate among females (34.9%) than males (23.8%). Age group analysis revealed that the highest prevalence of H. pylori infection was among individuals in their 40s, whereas the highest CLA resistance rate was observed among those in their 60s. The CLA resistance rate exhibited an upward trend and varied among patients based on their place of residence, and A2143G mutation was the most prevalent across all regions. Conclusion The prevalence of H. pylori infection and CLA resistance rate in Korea remain high and vary according to sex, age, and region. To effectively eradicate H. pylori, it is crucial to periodically monitor regional CLA resistance patterns and conduct CLA susceptibility testing before prescription.

Background: The epidemiology of pathogenic bacteria varies according to the socioeconomic status and antimicrobial resistance status. However, longitudinal epidemiological studies to evaluate the changes in species distribution and antimicrobial susceptibility of pathogenic bacteria nationwide are lacking. We retrospectively investigated the nationwide trends in species distribution and antimicrobial susceptibility of pathogenic bacteria over the last 20 years in Korea. Methods: From 1997 to 2016, annual cumulative antimicrobial susceptibility and species distribution data were collected from 12 university hospitals in five provinces and four metropolitan cities in South Korea. Results: The prevalence of Staphylococcus aureus was the highest (13.1%) until 2012 but decreased to 10.3% in 2016, consistent with the decrease in oxacillin resistance from 76.1% in 2008 to 62.5% in 2016. While the cefotaxime resistance of Escherichia coli increased from 9.0% in 1997 to 34.2% in 2016, E. coli became the most common species since 2013, accounting for 14.5% of all isolates in 2016. Pseudomonas aeruginosa and Acinetobacter baumannii rose to third and fifth places in 2008 and 2010, respectively, while imipenem resistance increased from 13.9% to 30.8% and 0.7% to 73.5% during the study period, respectively.Streptococcus agalactiae became the most common pathogenic streptococcal species in 2016, as the prevalence of Streptococcus pneumoniae decreased since 2010. During the same period, pneumococcal penicillin susceptibility decreased to 79.0%, and levofloxacin susceptibility of S. agalactiae decreased to 77.1% in 2016. Conclusion The epidemiology of pathogenic bacteria has changed significantly over the past 20 years according to trends in antimicrobial resistance in Korea. Efforts to confine antimicrobial resistance would change the epidemiology of pathogenic bacteria and, consequently, the diagnosis and treatment of infectious diseases.

Background: Streptococcus pneumoniae is a serious pathogen causing various infections in humans. We evaluated the serotype distribution and antimicrobial resistance of S. pneumoniae causing invasive pneumococcal disease (IPD) after introduction of pneumococcal conjugate vaccine (PCV)13 in Korea and investigated the epidemiological characteristics of multidrug-resistant (MDR) isolates. Methods: S. pneumoniae isolates causing IPD were collected from 16 hospitals in Korea between 2017 and 2019. Serotyping was performed using modified sequential multiplex PCR and the Quellung reaction. Antimicrobial susceptibility tests were performed using the broth microdilution method. Multilocus sequence typing was performed on MDR isolates for epidemiological investigations. Results: Among the 411 S. pneumoniae isolates analyzed, the most prevalent serotype was 3 (12.2%), followed by 10A (9.5%), 34 (7.3%), 19A (6.8%), 23A (6.3%), 22F (6.1%), 35B (5.8%), 11A (5.1%), and others (40.9%). The coverage rates of PCV7, PCV10, PCV13, and pneumococcal polysaccharide vaccine (PPSV)23 were 7.8%, 7.8%, 28.7%, and 59.4%, respectively. Resistance rates to penicillin, ceftriaxone, erythromycin, and levofloxacin were 13.1%, 9.2%, 80.3%, and 4.1%, respectively. MDR isolates accounted for 23.4% of all isolates. Serotypes 23A, 11A, 19A, and 15B accounted for the highest proportions of total isolates at 18.8%, 16.7%, 14.6%, and 8.3%, respectively. Sequence type (ST)166 (43.8%) and ST320 (12.5%) were common among MDR isolates. Conclusions Non-PCV13 serotypes are increasing among invasive S. pneumoniae strains causing IPD. Differences in antimicrobial resistance were found according to the specific serotype. Continuous monitoring of serotypes and antimicrobial resistance is necessary for the appropriate management of S. pneumoniae infections.