Additional file 1 of Characterisation of human astrovirus in a diarrhoea outbreak using nanopore and Sanger sequencing protocols

Supplementary Material

Table_1_Topography of respiratory tract and gut microbiota in mice with influenza A virus infection.XLSX

IntroductionInfluenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied.MethodsWe characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice.ResultsThe oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia, Streptococcus, and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice.DiscussionThese findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.</p

Table_2_Topography of respiratory tract and gut microbiota in mice with influenza A virus infection.XLSX

IntroductionInfluenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied.MethodsWe characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice.ResultsThe oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia, Streptococcus, and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice.DiscussionThese findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.</p

Data_Sheet_1_Topography of respiratory tract and gut microbiota in mice with influenza A virus infection.PDF

IntroductionInfluenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied.MethodsWe characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice.ResultsThe oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia, Streptococcus, and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice.DiscussionThese findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.</p

Table_1_Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection.pdf

IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.</p

Alignment of <i>phsA</i> sequences in 21 <i>S</i>. Choleraesuis.

<p>The deletion of G at position 760 resulted in a frame-shift mutation. The first sequence is H₂S-positive <i>S</i>. Choleraesuis strain SC-B67 (NC_006905.1).</p

Table_3_Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection.pdf

IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.</p

Table_5_Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection.xlsx

IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.</p

Image_2_Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection.pdf

IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.</p

Table_4_Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection.xlsx

IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.</p