Andrea Hahn1, Amit Sanyal2, Geovanny F Perez3, Anamaris M Colberg-Poley4, Joseph Campos5, Mary C Rose6, Marcos Pérez-Losada7. 1. Division of Infectious Diseases, Children's National Health System (CNHS), Washington, DC, USA; Research Center for Genetic Medicine, CNHS, Washington, DC, USA; Department of Pediatrics, George Washington University (GWU), Washington, DC, USA. Electronic address: alhahn@childrensnational.org. 2. GWU School of Medicine, Washington, DC, USA. 3. Department of Pediatrics, George Washington University (GWU), Washington, DC, USA; Division of Pulmonary and Sleep Medicine, CNHS, Washington, DC, USA. 4. Research Center for Genetic Medicine, CNHS, Washington, DC, USA; Department of Pediatrics, George Washington University (GWU), Washington, DC, USA; Department of Integrative Systems Biology, GWU, Washington, DC, USA; Department of Biochemistry & Molecular Medicine, GWU, Washington, DC, USA; Department of Microbiology, Immunology & Tropical Medicine, GWU, Washington, DC, USA. 5. Department of Pediatrics, George Washington University (GWU), Washington, DC, USA; Department of Microbiology, Immunology & Tropical Medicine, GWU, Washington, DC, USA; Division of Laboratory Medicine, CNHS, Washington, DC, USA; Department of Pathology, GWU, Washington, DC, USA. 6. Research Center for Genetic Medicine, CNHS, Washington, DC, USA; Department of Pediatrics, George Washington University (GWU), Washington, DC, USA; Department of Integrative Systems Biology, GWU, Washington, DC, USA. 7. Research Center for Genetic Medicine, CNHS, Washington, DC, USA; Computational Biology Institute, GWU, Ashburn, VA, USA; CIBIO-InBIO, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal.
Abstract
BACKGROUND: Cystic fibrosis (CF) is an autosomal recessive disease characterized by recurrent lung infections. Studies of the lung microbiome have shown an association between decreasing diversity and progressive disease. 454 pyrosequencing has frequently been used to study the lung microbiome in CF, but will no longer be supported. We sought to identify the benefits and drawbacks of using two state-of-the-art next generation sequencing (NGS) platforms, MiSeq and PacBio RSII, to characterize the CF lung microbiome. Each has its advantages and limitations. METHODS: Twelve samples of extracted bacterial DNA were sequenced on both MiSeq and PacBio NGS platforms. DNA was amplified for the V4 region of the 16S rRNA gene and libraries were sequenced on the MiSeq sequencing platform, while the full 16S rRNA gene was sequenced on the PacBio RSII sequencing platform. Raw FASTQ files generated by the MiSeq and PacBio platforms were processed in mothur v1.35.1. RESULTS: There was extreme discordance in alpha-diversity of the CF lung microbiome when using the two platforms. Because of its depth of coverage, sequencing of the 16S rRNA V4 gene region using MiSeq allowed for the observation of many more operational taxonomic units (OTUs) and higher Chao1 and Shannon indices than the PacBio RSII. Interestingly, several patients in our cohort had Escherichia, an unusual pathogen in CF. Also, likely because of its coverage of the complete 16S rRNA gene, only PacBio RSII was able to identify Burkholderia, an important CF pathogen. CONCLUSION: When comparing microbiome diversity in clinical samples from CF patients using 16S sequences, MiSeq and PacBio NGS platforms may generate different results in microbial community composition and structure. It may be necessary to use different platforms when trying to correctly identify dominant pathogens versus measuring alpha-diversity estimates, and it would be important to use the same platform for comparisons to minimize errors in interpretation.
BACKGROUND:Cystic fibrosis (CF) is an autosomal recessive disease characterized by recurrent lung infections. Studies of the lung microbiome have shown an association between decreasing diversity and progressive disease. 454 pyrosequencing has frequently been used to study the lung microbiome in CF, but will no longer be supported. We sought to identify the benefits and drawbacks of using two state-of-the-art next generation sequencing (NGS) platforms, MiSeq and PacBio RSII, to characterize the CF lung microbiome. Each has its advantages and limitations. METHODS: Twelve samples of extracted bacterial DNA were sequenced on both MiSeq and PacBio NGS platforms. DNA was amplified for the V4 region of the 16S rRNA gene and libraries were sequenced on the MiSeq sequencing platform, while the full 16S rRNA gene was sequenced on the PacBio RSII sequencing platform. Raw FASTQ files generated by the MiSeq and PacBio platforms were processed in mothur v1.35.1. RESULTS: There was extreme discordance in alpha-diversity of the CF lung microbiome when using the two platforms. Because of its depth of coverage, sequencing of the 16S rRNA V4 gene region using MiSeq allowed for the observation of many more operational taxonomic units (OTUs) and higher Chao1 and Shannon indices than the PacBio RSII. Interestingly, several patients in our cohort had Escherichia, an unusual pathogen in CF. Also, likely because of its coverage of the complete 16S rRNA gene, only PacBio RSII was able to identify Burkholderia, an important CF pathogen. CONCLUSION: When comparing microbiome diversity in clinical samples from CF patients using 16S sequences, MiSeq and PacBio NGS platforms may generate different results in microbial community composition and structure. It may be necessary to use different platforms when trying to correctly identify dominant pathogens versus measuring alpha-diversity estimates, and it would be important to use the same platform for comparisons to minimize errors in interpretation.
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