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Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria interactions associated with symptomatic respiratory infection [RNA-Seq]

ABSTRACT: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their interactions with the host remain unclear. In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. RNA sequencing reads from human nasal swab samples were pre-processed and aligned to the human genome for host gene expression counting, while unmapped reads were further aligned to microbiota genome. After taxonomic classification, microbial abundance matrix was derived at each taxonomic level for differential and host-microbiota interaction analysis. We applied this workflow to a local SARS-CoV-2 cohort with 76 infected patients, among whom 55 (72.37%) were symptomatic at enrollment. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 4.81% reads unmapped from the human genome across all 149 samples, among which around half (median 48.63%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment, and both groups showed decreased loads one week later. Compared with benchmarking 16S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genus. A total of 685 bacteria species were identified to show a relative abundance >= 0.01% in at least 10% samples. Differential abundance analysis identified 66 species (DASs) from 6 phyla with significantly decreased abundance in samples from the symptomatic group compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host’s gene expression using an expression quantitative trait bacteria (eQTB) model, we found 58 symptom-associated DASs identified at enrollment were significantly associated with one to 16 genes. GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to the activation of olfactory, keratinocyte differentiation, and DNA methylation. In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome interactions, offering insights into microbial contributions to respiratory diseases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE309130 | GEO | 2025/12/06

REPOSITORIES: GEO

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Project description:The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their interactions with the host remain unclear. In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. RNA sequencing reads from human nasal swab samples were pre-processed and aligned to the human genome for host gene expression counting, while unmapped reads were further aligned to microbiota genome. After taxonomic classification, microbial abundance matrix was derived at each taxonomic level for differential and host-microbiota interaction analysis. We applied this workflow to a local SARS-CoV-2 cohort with 76 infected patients, among whom 55 (72.37%) were symptomatic at enrollment. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 4.81% reads unmapped from the human genome across all 149 samples, among which around half (median 48.63%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment, and both groups showed decreased loads one week later. Compared with benchmarking 16S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genus. A total of 685 bacteria species were identified to show a relative abundance >= 0.01% in at least 10% samples. Differential abundance analysis identified 66 species (DASs) from 6 phyla with significantly decreased abundance in samples from the symptomatic group compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host’s gene expression using an expression quantitative trait bacteria (eQTB) model, we found 58 symptom-associated DASs identified at enrollment were significantly associated with one to 16 genes. GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to the activation of olfactory, keratinocyte differentiation, and DNA methylation. In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome interactions, offering insights into microbial contributions to respiratory diseases.

Project description:Background: While the luminal microbiome composition in the human cervicovaginal tract has been defined, the presence and impact of tissue-adherent ectocervical microbiota remain incompletely understood. Studies of luminal and tissue-associated bacteria in the gastrointestinal tract suggest that they may have distinct roles in health and disease. Here, we performed a multi-omics characterization of paired luminal and tissue samples collected from a clinically well-characterized cohort of Kenyan women. Results: We identified a tissue-adherent bacterial microbiome, with a higher alpha diversity than the luminal microbiome, in which dominant genera overall included Gardnerella and Lactobacillus, followed by Prevotella, Atopobium, and Sneathia. About half of the L. iners dominated luminal samples had a corresponding Gardnerella dominated tissue microbiome. Broadly, the tissue-adherent microbiome was associated with fewer differentially expressed host genes than the luminal microbiome. Gene set enrichment analysis revealed that L. crispatus-dominated tissue-adherent communities were associated with protein translation and antimicrobial activity, whereas a highly diverse microbiome was associated with epithelial remodeling and pro-inflammatory pathways. Communities dominated by L. iners and Gardnerella were associated with low host transcriptional activity. Tissue-adherent microbiomes dominated by Lactobacillus and Gardnerella correlated with host protein profiles associated with epithelial barrier stability, and with a more pro-inflammatory profile for the Gardnerella-dominated microbiome group. Tissue samples with a highly diverse composition had a protein profile representing cell proliferation and pro-inflammatory activity. Conclusion: We identified ectocervical tissue-adherent bacterial communities in all study participants. These communities were distinct from cervicovaginal luminal microbiota in a significant proportion of individuals. This difference could possibly explain that L. iners dominant luminal communities have a high probability of transitioning to high diverse bacterial communities including high abundance of Gardnerella. By performing integrative multi-omics analyses we further revealed that bacterial communities at both sites correlated with distinct host gene expression and protein levels. The tissue-adherent bacterial community is similar to vaginal biofilms that significantly impact women’s reproductive and sexual health.

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Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria interactions associated with symptomatic respiratory infection [RNA-Seq]

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