Nasal microbiome - syncom screening
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF
ORGANISM(S): Synthetic Community
SUBMITTER: 
Daniel Petras
PROVIDER: MSV000096776 | MassIVE | Sat Jan 04 05:42:00 GMT 2025
REPOSITORIES: MassIVE
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Project description:Microbial Synthetic Community resembling the Nasal human microbiome
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Project description:Staphylococcus aureus prefers the human anterior nares as its habitat, but nothing is known about the nutritional situation in this ecological niche. Analysis of nasal secretions showed a complex mixture of nutrients at low concentration. Based on these findings a synthetic nasal medium (SNM) was composed, mimicking nasal secretions. We used microarrays in order to investigate pathways and expression patterns important in a synthetic medium mimicking nasal secretions compared to standard laboratory complex medium Staphylococcus aureus USA300 was inoculated in complex medium (BM) or synthetic nasal medium (SNM) to an OD578nm of 0.005 and grown under aerobic conditions until OD578nm of 0.02. RNA was stabilized and extracted at this early growth phase and hybridization was made on Affymetrix microarrays. The aim was to identify genes which are important during growth under the limited conditions present during colonization of human nares
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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.
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