Impact of RNA extraction on respiratory microbiome analysis using third-generation sequencing

The respiratory microbiome, which comprises bacteria, fungi, and viruses, plays a crucial role in respiratory health and disease. However, its study is limited by the low microbial biomass in respiratory samples and the dominance of host RNA. Metatranscriptomics offers comprehensive insights into active microbial communities and their interactions with the host but requires optimized RNA extraction protocols for robust and unbiased analysis. This study evaluated two RNA extraction kits—one employing chemical lysis (CL) and another combining chemical and mechanical lysis (CML)—to determine their effectiveness for metatranscriptomic analysis of respiratory samples. The CML protocol significantly increased double-stranded DNA (dsDNA) library yields, leading to higher sequencing read counts for both sample types (p < 0.0001). The read length was unaffected by the lysis protocol for the BAL and NPS samples. Taxonomic profiling revealed that CML enhanced the detection of robust microorganisms, such as gram-positive bacteria and fungi, without compromising viral detection. The CML protocol demonstrated superior recovery of genetic material, particularly for fungi and gram-positive bacteria, making it better suited for comprehensive metatranscriptomic analyses. These findings underscore the need for tailored RNA extraction strategies on the basis of sample type and research objectives. Optimized metatranscriptomic protocols are pivotal for advancing our understanding of the respiratory microbiome and its role in health and disease.

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Publication Details

Journal
BMC Genomics
Published
2025-10-10
DOI
https://doi.org/10.1186/s12864-025-12028-4
Citations
2
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
1.61
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article

Impact of RNA extraction on respiratory microbiome analysis using third-generation sequencing

Nicolas Nesi, Alice Michel, Chervin Hassel, Jean‐Christophe Plantier et al.
2 citations
BMC Genomics
Gut microbiota and health
1.61
article

Impact of RNA extraction on respiratory microbiome analysis using third-generation sequencing

Nicolas Nesi, Alice Michel, Chervin Hassel, Jean‐Christophe Plantier, Meriadeg Ar Gouilh, Hortense Petat, Marie Leoz, Christophe Marguet, Camille Charbonnier Le Clezio
article en
2 citations

Abstract

The respiratory microbiome, which comprises bacteria, fungi, and viruses, plays a crucial role in respiratory health and disease. However, its study is limited by the low microbial biomass in respiratory samples and the dominance of host RNA. Metatranscriptomics offers comprehensive insights into active microbial communities and their interactions with the host but requires optimized RNA extraction protocols for robust and unbiased analysis. This study evaluated two RNA extraction kits—one employing chemical lysis (CL) and another combining chemical and mechanical lysis (CML)—to determine their effectiveness for metatranscriptomic analysis of respiratory samples. The CML protocol significantly increased double-stranded DNA (dsDNA) library yields, leading to higher sequencing read counts for both sample types (p < 0.0001). The read length was unaffected by the lysis protocol for the BAL and NPS samples. Taxonomic profiling revealed that CML enhanced the detection of robust microorganisms, such as gram-positive bacteria and fungi, without compromising viral detection. The CML protocol demonstrated superior recovery of genetic material, particularly for fungi and gram-positive bacteria, making it better suited for comprehensive metatranscriptomic analyses. These findings underscore the need for tailored RNA extraction strategies on the basis of sample type and research objectives. Optimized metatranscriptomic protocols are pivotal for advancing our understanding of the respiratory microbiome and its role in health and disease.

BMC GenomicsVol. 26(1)
Inserm (FR), Normandie Université (FR), Centre Hospitalier Universitaire de Caen Normandie (FR), Université de Rouen Normandie (FR), Université de Caen Normandie (FR)
Openalex Percentile: Top 14%
Gut microbiota and health
1.61
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