Identification of microbial community in the urban environment: The concordance between conventional culture and nanopore 16S rRNA sequencing

IntroductionMicrobes in the built environment have been implicated as a source of infectious diseases. Bacterial culture is the standard method for assessing the risk of exposure to pathogens in urban environments, but this method only accounts for <1% of the diversity of bacteria. Recently, full-length 16S rRNA gene analysis using nanopore sequencing has been applied for microbial evaluations, resulting in a rise in the development of long-read taxonomic tools for species-level classification. Regarding their comparative performance, there is, however, a lack of information.MethodsHere, we aim to analyze the concordance of the microbial community in the urban environment inferred by multiple taxonomic classifiers, including ARGpore2, Emu, Kraken2/Bracken and NanoCLUST, using our 16S-nanopore dataset generated by MegaBLAST, as well as assess their abilities to identify culturable species based on the conventional culture results.ResultsAccording to our results, NanoCLUST was preferred for 16S microbial profiling because it had a high concordance of dominant species and a similar microbial profile to MegaBLAST, whereas Kraken2/Bracken, which had similar clustering results as NanoCLUST, was also desirable. Second, for culturable species identification, Emu with the highest accuracy (81.2%) and F1 score (29%) for the detection of culturable species was suggested.DiscussionIn addition to generating datasets in complex communities for future benchmarking studies, our comprehensive evaluation of the taxonomic classifiers offers recommendations for ongoing microbial community research, particularly for complex communities using nanopore 16S rRNA sequencing

Genetic mechanisms of co-emergence of INH-resistant Mycobacterium tuberculosis strains during the standard course of antituberculosis therapy

ABSTRACTThe incidence of isoniazid (INH) resistant Mycobacterium tuberculosis is increasing globally. This study aimed to identify the molecular mechanisms behind the development of INH resistance in M. tuberculosis strains collected from the same patients during the standard course of treatment. Three M. tuberculosis strains were collected from a patient before and during antituberculosis (anti-TB) therapy. The strains were characterized using phenotypic drug susceptibility tests, Mycobacterial Interspersed Repeated Unit-Variable-Number Tandem Repeats (MIRU-VNTR), and whole-genome sequencing (WGS) to identify mutations associated with INH resistance. To validate the role of the novel mutations in INH resistance, the mutated katG genes were electroporated into a KatG-deleted M. tuberculosis strain (GA03). Three-dimensional structures of mutated KatG were modeled to predict their impact on INH binding. The pre-treatment strain was susceptible to INH. However, two INH-resistant strains were isolated from the patient after anti-TB therapy. MIRU-VNTR and WGS revealed that the three strains were clonally identical. A missense mutation (P232L) and a nonsense mutation (Q461Stop) were identified in the katG of the two post-treatment strains, respectively. Transformation experiments showed that katG of the pre-treatment strain restored INH susceptibility in GA03, whereas the mutated katG genes from the post-treatment strains rendered negative catalase activity and INH resistance. The protein model indicated that P232L reduced INH-KatG binding affinity while Q461Stop truncated gene transcription. Our results showed that the two katG mutations, P232L and Q461Stop, accounted for the co-emergence of INH-resistant clones during anti-TB therapy. The inclusion of these mutations in the design of molecular assays could increase the diagnostic performance.IMPORTANCEThe evolution of drug-resistant strains of Mycobacterium tuberculosis within the lung lesions of a patient has a detrimental impact on treatment outcomes. This is particularly concerning for isoniazid (INH), which is the most potent first-line antimycobacterial drug. However, the precise genetic factors responsible for drug resistance in patients have not been fully elucidated, with approximately 15% of INH-resistant strains harboring unknown genetic factors. This raises concerns about the emergence of drug-resistant clones within patients, further contributing to the global epidemic of resistance. In this study, we revealed the presence of two novel katG mutations, which emerged independently due to the stress exerted by antituberculosis (anti-TB) treatment on a parental strain. Importantly, we experimentally demonstrated the functional significance of both mutations in conferring resistance to INH. Overall, this research sheds light on the genetic mechanisms underlying the evolution of INH resistance within patients and provides valuable insights for improving diagnostic performance by targeting specific mutations

Table_1_Identification of microbial community in the urban environment: The concordance between conventional culture and nanopore 16S rRNA sequencing.xlsx

IntroductionMicrobes in the built environment have been implicated as a source of infectious diseases. Bacterial culture is the standard method for assessing the risk of exposure to pathogens in urban environments, but this method only accounts for MethodsHere, we aim to analyze the concordance of the microbial community in the urban environment inferred by multiple taxonomic classifiers, including ARGpore2, Emu, Kraken2/Bracken and NanoCLUST, using our 16S-nanopore dataset generated by MegaBLAST, as well as assess their abilities to identify culturable species based on the conventional culture results.ResultsAccording to our results, NanoCLUST was preferred for 16S microbial profiling because it had a high concordance of dominant species and a similar microbial profile to MegaBLAST, whereas Kraken2/Bracken, which had similar clustering results as NanoCLUST, was also desirable. Second, for culturable species identification, Emu with the highest accuracy (81.2%) and F1 score (29%) for the detection of culturable species was suggested.DiscussionIn addition to generating datasets in complex communities for future benchmarking studies, our comprehensive evaluation of the taxonomic classifiers offers recommendations for ongoing microbial community research, particularly for complex communities using nanopore 16S rRNA sequencing.</p

Table_6_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.XLSX

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_4_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.xlsx

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_3_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.xlsx

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_5_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.XLSX

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_2_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.xlsx

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_7_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.XLSX

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p

Table_1_The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.XLSX

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.</p