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Visualization of the individual blood microbiome to study the etiology of sarcoidosis
INTRODUCTION: Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets i...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624578/ https://www.ncbi.nlm.nih.gov/pubmed/37928975 http://dx.doi.org/10.1016/j.csbj.2023.10.027 |
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author | Hodzhev, Yordan Tsafarova, Borislava Tolchkov, Vladimir Youroukova, Vania Ivanova, Silvia Kostadinov, Dimitar Yanev, Nikolay Zhelyazkova, Maya Tsonev, Stefan Kalfin, Reni Panaiotov, Stefan |
author_facet | Hodzhev, Yordan Tsafarova, Borislava Tolchkov, Vladimir Youroukova, Vania Ivanova, Silvia Kostadinov, Dimitar Yanev, Nikolay Zhelyazkova, Maya Tsonev, Stefan Kalfin, Reni Panaiotov, Stefan |
author_sort | Hodzhev, Yordan |
collection | PubMed |
description | INTRODUCTION: Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually. OBJECTIVE: To increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach. RESULTS: The 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus. CONCLUSIONS: Our approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics. |
format | Online Article Text |
id | pubmed-10624578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-106245782023-11-05 Visualization of the individual blood microbiome to study the etiology of sarcoidosis Hodzhev, Yordan Tsafarova, Borislava Tolchkov, Vladimir Youroukova, Vania Ivanova, Silvia Kostadinov, Dimitar Yanev, Nikolay Zhelyazkova, Maya Tsonev, Stefan Kalfin, Reni Panaiotov, Stefan Comput Struct Biotechnol J Research Article INTRODUCTION: Single microbial pathogens or host-microbiome dysbiosis are the causes of lung diseases with suspected infectious etiology. Metagenome sequencing provides an overview of the microbiome content. Due to the rarity of most granulomatous lung diseases collecting large systematic datasets is challenging. Thus, single-patient data often can only be summarized visually. OBJECTIVE: To increase the information gain from a single-case metagenome analysis we suggest a quantitative and qualitative approach. RESULTS: The 16S metagenomic results of 7 patients with pulmonary sarcoidosis were compared with those of 22 healthy individuals. From lysed blood, total microbial DNA was extracted and sequenced. Cleaned data reads were identified taxonomically using Kraken 2 software. Individual metagenomic data were visualized with a Sankey diagram, Krona chart, and a heat-map. We identified five genera that were exclusively present or significantly enhanced in patients with sarcoidosis - Veillonella, Prevotella, Cutibacterium, Corynebacterium, and Streptococcus. CONCLUSIONS: Our approach can characterize the blood microbiome composition and diversity in rare diseases at an individual level. Investigation of the blood microbiome in patients with granulomatous lung diseases of unknown etiology, such as sarcoidosis could enhance our comprehension of their origin and pathogenesis and potentially uncover novel personalized therapeutics. Research Network of Computational and Structural Biotechnology 2023-10-20 /pmc/articles/PMC10624578/ /pubmed/37928975 http://dx.doi.org/10.1016/j.csbj.2023.10.027 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Hodzhev, Yordan Tsafarova, Borislava Tolchkov, Vladimir Youroukova, Vania Ivanova, Silvia Kostadinov, Dimitar Yanev, Nikolay Zhelyazkova, Maya Tsonev, Stefan Kalfin, Reni Panaiotov, Stefan Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title | Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title_full | Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title_fullStr | Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title_full_unstemmed | Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title_short | Visualization of the individual blood microbiome to study the etiology of sarcoidosis |
title_sort | visualization of the individual blood microbiome to study the etiology of sarcoidosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624578/ https://www.ncbi.nlm.nih.gov/pubmed/37928975 http://dx.doi.org/10.1016/j.csbj.2023.10.027 |
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