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Metagenomic Analysis of Human Nasal Microbiome in Chronic Rhinosinusitis Using Nasal Secretion
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A1282 - Metagenomic Analysis of Human Nasal Microbiome in Chronic Rhinosinusitis Using Nasal Secretion
Author Block: Y. Kim1, M. Lee1, D. Kim2, Y. Kim3, J. Mo4, H. Shin5; 1Department of Pharmacology, Seoul National University, College of Medicine, Seoul, Korea, Republic of, 2Department of Otorhinolaryngology, Boramae Medical Center, Seoul, Korea, Republic of, 3Department of Otorhinolaryngology, Chungnam University Hospital, Daejeon, Korea, Republic of, 4Department of Otorhinolaryngology, Dankook University Hospital, Chonan, Korea, Republic of, 5Department of Pharmacology, Seoul National University, Seoul, Korea, Republic of.
Rationale
Chronic rhinosinusitis (CRS) is common chronic disease in nasal mucosa and sinus. CRS is subdivided into CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP). But, dichotomization of phenotypes of CRS can’t explain various etiologies of CRS. We aimed to perform metagenomic analysis using nasal secretion for clustering microbiota composition in CRS patients.MethodsNasal secretions were obtained from 8 non-CRS controls, 8 patients with CRSsNP and 23 patients with CRSwNP. To collect nasal secretions, a filter paper was placed around the uncinate process for 10 minutes and eluted sterile distilled water. 16S rDNA was extracted using PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA) from the nasal secretion. For Miseq (Illumina, USA) sequencing, V3-4 region primer were used to make DNA amplicons. Sequence analysis, prediction of bacterial metagenomes analysis and bacterial features analysis were performed using QIIME version 1.9.1, PICRUSt version 1.1.1 and LDA effect size (LEfSe), respectively. ResultsBased on Dirichlet multinomial mixtures (DMM) modeling and Laplace approximation, bacterial composition of 39 patients was divided into 2 distinct clusters. Subgroup1 consisted of 19 subjects (5 control, 6 CRS, 8 CRSwNP) and was dominated by Veillonellaceae. On the other hands, Subgroup2 consisted of 20 subjects (3 control, 2 CRS, 15 CRSwNP) and was dominated by Staphylococcaceae. Nasal polyp cases were more enriched in subgroup2 (chi-square test, p=0.037). Subgroup2 was significantly lower chao1, shannon, simpson indices and the number of observed OTUs compared to Subgroup1. Clear separation was observed in PCoA plot and there was significant difference between Subgroup1 and 2(PERMANOVA p=0.001). Compared to Subgroup1 microbiota, Subgroup2 was observed lower functionally diverse in KEGG pathways and depleted of biosynthesis of other secondary metabolites, cell communication, energy metabolism and metabolic diseases. ConclusionsOur data demonstrate clusters with microbiota composition was highly associated with occurrence of nasal polyp and provided novel insights on the endotypes of CRS. Thus, metagenomics profiles of nasal secretion from CRS patients may help to discover novel biomarker for precise therapeutic decisions.
Author Block: Y. Kim1, M. Lee1, D. Kim2, Y. Kim3, J. Mo4, H. Shin5; 1Department of Pharmacology, Seoul National University, College of Medicine, Seoul, Korea, Republic of, 2Department of Otorhinolaryngology, Boramae Medical Center, Seoul, Korea, Republic of, 3Department of Otorhinolaryngology, Chungnam University Hospital, Daejeon, Korea, Republic of, 4Department of Otorhinolaryngology, Dankook University Hospital, Chonan, Korea, Republic of, 5Department of Pharmacology, Seoul National University, Seoul, Korea, Republic of.
Rationale
Chronic rhinosinusitis (CRS) is common chronic disease in nasal mucosa and sinus. CRS is subdivided into CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP). But, dichotomization of phenotypes of CRS can’t explain various etiologies of CRS. We aimed to perform metagenomic analysis using nasal secretion for clustering microbiota composition in CRS patients.MethodsNasal secretions were obtained from 8 non-CRS controls, 8 patients with CRSsNP and 23 patients with CRSwNP. To collect nasal secretions, a filter paper was placed around the uncinate process for 10 minutes and eluted sterile distilled water. 16S rDNA was extracted using PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA) from the nasal secretion. For Miseq (Illumina, USA) sequencing, V3-4 region primer were used to make DNA amplicons. Sequence analysis, prediction of bacterial metagenomes analysis and bacterial features analysis were performed using QIIME version 1.9.1, PICRUSt version 1.1.1 and LDA effect size (LEfSe), respectively. ResultsBased on Dirichlet multinomial mixtures (DMM) modeling and Laplace approximation, bacterial composition of 39 patients was divided into 2 distinct clusters. Subgroup1 consisted of 19 subjects (5 control, 6 CRS, 8 CRSwNP) and was dominated by Veillonellaceae. On the other hands, Subgroup2 consisted of 20 subjects (3 control, 2 CRS, 15 CRSwNP) and was dominated by Staphylococcaceae. Nasal polyp cases were more enriched in subgroup2 (chi-square test, p=0.037). Subgroup2 was significantly lower chao1, shannon, simpson indices and the number of observed OTUs compared to Subgroup1. Clear separation was observed in PCoA plot and there was significant difference between Subgroup1 and 2(PERMANOVA p=0.001). Compared to Subgroup1 microbiota, Subgroup2 was observed lower functionally diverse in KEGG pathways and depleted of biosynthesis of other secondary metabolites, cell communication, energy metabolism and metabolic diseases. ConclusionsOur data demonstrate clusters with microbiota composition was highly associated with occurrence of nasal polyp and provided novel insights on the endotypes of CRS. Thus, metagenomics profiles of nasal secretion from CRS patients may help to discover novel biomarker for precise therapeutic decisions.
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