Project description:We report the application of single cell RNA sequencing technology for high-throughput profiling of nasal microbiome Staphylococcus epidermidis in human nasal epithelial cells.
Project description:Chronic rhinitis (CR) is a frustrating clinical syndrome in dogs and our understanding of the disease pathogenesis in is limited. Increasingly, host-microbe interactions are considered key drivers of clinical disease in sites of persistent mucosal inflammation such as the nasal and oral cavities. Therefore, we applied next generation sequencing tools to interrogate abnormalities present in the nose of dogs with CR and compared immune and microbiome profiles to those of healthy dogs. Host nasal cell transcriptomes were evaluated by RNA sequencing, while microbial communities were assessed by 16S rRNA sequencing. Correlation analysis was then used to identify significant interactions between nasal cell transcriptomes and the nasal microbiome and how these interactions were altered in animals with CR. Notably, we observed significant downregulation of multiple genes associated with ciliary function in dogs with CR, suggesting a previously undetected role for ciliary dysfunction in this syndrome. We also found significant upregulation of immune genes related to the TNF-a and interferon pathways. The nasal microbiome was also significantly altered in CR dogs, with overrepresentation of several potential pathobionts. Interactome analysis revealed significant correlations between bacteria in the genus Porphyromonas and the upregulated host inflammatory responses in dogs with CR, as well as defective ciliary function which was correlated with Streptococcus abundance. These findings provide new insights into host-microbe interactions in a canine model of CR and indicate the presence of potentially causal relationships between nasal pathobionts and the development of nasal inflammation and ciliary dysfunction.
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: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:Antibiotics are commonly prescribed to treat chronic rhinosinusitis (CRS). However, the effects of antibiotics on the microbiome and secreted proteome remain unknown in regard to CRS.We analyzed the effects of antibiotics on the nasal secreted proteome inthe context of CRS using data-independent acqusition proteomics approach.