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Background <p>The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear.</p> Results <p>In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. 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 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (<i>P</i> = 0.004), and both groups showed decreased loads one week later (symptomatic, <i>P</i> = 0.001; asymptomatic, <i>P</i> = 0.035). Compared with benchmarking 16&#xa0;S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, <i>P</i><sub><i>max</i></sub> &lt; 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log<sub>2</sub>(fold change or FC) &lt; -1 and adjusted <i>P</i> &lt; 0.05) 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 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted <i>P</i> &lt; 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation.</p> Conclusions <p>In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.</p>

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Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Xiangyu Ye,
  • Molin Yue,
  • Sojin Lee,
  • Andrew Li,
  • Anna F. Wang-Erickson,
  • Erick Forno,
  • Taylor Eddens,
  • Nader Shaikh,
  • Wei Chen

摘要

Background

The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear.

Results

In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. 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 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) 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 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation.

Conclusions

In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.