Dataset Information

Research on the microbiota and metabolic characteristics of type 2 chronic rhinosinusitis based on the analysis of 16S amplicon sequencing combined with metabolomics

ABSTRACT:

Objective:

To investigate the diversity of nasal microbiota and metabolic characteristics of patients with Type

2 chronic rhinosinusitis, as well as the interactions and potential regulatory relationships between

the nasal microbiota and their metabolites by conducting a 16S amplicon sequencing and

metabolomics analysis on nasal secretions from patients with Type 2 chronic rhinosinusitis.

Methods:

28 patients with Type 2 Chronic Rhinosinusitis (T2-CRS) were selected from the Guangzhou Red

Cross Hospital Affiliated to Jinan University. These patients underwent endoscopic sinus surgery

and were diagnosed with T2-CRS between July 2023 and October 2024, based on the presence of

eosinophilic infiltration (Eos10/high-power field) in the nasal polyp pathological tissues after

surgery. 12 healthy individuals without nasal disease was enrolled as the control group. For each

participant, two samples of nasal secretions were collected. One sample was processed using 16S

amplicon sequencing technology on the Agilent 2100 Bioanalyzer platform, targeting the V3-V4

hypervariable regions of the 16S rDNA to sequence all bacterial taxa present in the sample. The

sequencing results were subjected to Operational Taxonomic Unit (OTU) clustering, followed by

bioinformatics analyses (including α-diversity and β-diversity analyses, LEfSe analysis, and

differential abundance analysis) and statistical processing to characterize and compare the nasal

microbiota profiles and differences between the T2-CRS group and the control group. The other

tube of nasal secretion sample was subjected to extraction and analyzed using Ultra-Performance

Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS). The mass spectrometry

data were interpreted by integrating information from the BGI Metabolome database, the mzCloud

database, and the ChemSpider online database. This approach facilitated the identification and

comparison of the metabolic profiles and differences in nasal microbiota metabolites between the

T2-CRS group and the control group. Finally, the identified differentially abundant microbiota and metabolites were analyzed through an integrative multi-omics approach. This reveals the

metabolites closely related to the distribution of microbial communities and the dominant species

that induce metabolic changes, and explores the intrinsic regulatory pathways of the organism

involving nasal microbiota and metabolites.

Results:

The sequencing results showed that both T2-CRS patients and healthy control group had a rich

and diverse bacterial community in the nasal cavity. Compared with the healthy control group, the

T2-CRS group exhibited a decrease in α-diversity, with statistically significant differences (P <

0.05) observed in the α-diversity-related indices, including the Chao 1 index, Sobs index, Coverage

index, and Shannon index. Principal Coordinates Analysis (PCoA) revealed a significant

difference in β-diversity between the two groups (P < 0.05). At the phylum level, the nasal

microbiota of the T2-CRS group had higher relative abundances of Fusobacteriota,

Acidobacteriota, and Campylobacterota, with significant differences (P < 0.05). In contrast, the

nasal microbiota of the control group had higher relative abundances of Pseudomonadota,

Actinomycetota, Bacteroidota, and Gemmatimonadota, but these differences were not statistically

significant (P > 0.05). At the genus level, the nasal microbiota of the T2-CRS group exhibited

higher relative abundances of Haemophilus, Pseudomonas, and Burkholderia, with significant

differences (P < 0.05). In the control group, the relative abundances of Sphingomonas,

Bradyrhizobium, Cutibacterium, Methylorubrum, and Lawsonella were higher and showed

significant differences (P < 0.05). At the species level, Haemophilus species, Pseudomonas

aeruginosa, and Burkholderia cepacia exhibited higher relative abundances in the T2-CRS group

with significant differences (P < 0.05). In contrast, the control group had higher relative

abundances of Sphingomonas azotifigens, Cutibacterium acnes, Methylorubrum extorquens, and

Lawsonella clevelandensis, with significant differences observed (P < 0.05).

In terms of metabolomics, there were significant differences in the metabolic profiles between the

T2-CRS group and the control group. A total of 46 differential metabolites were identified using

fold change (FC), P-value, and variable importance in projection (VIP) values. In the T2-CRS

group, the upregulated metabolites mainly included 10Z - nonadecenoic acid, 4 -

hydroxyphenylpyruvic acid, pimelic acid, indoleacetic acid, and others, predominantly fatty acids.

In the T2-CRS group, the downregulated metabolites mainly included Leucine, L-aspartic acid, Asparagine, D-ornithine, L-glutathione oxidized etc., predominantly amino acids. These

differential metabolites were primarily annotated and enriched in metabolic pathways like

tryptophan metabolism, glycerophospholipid metabolism, glutathione metabolism, and arginine

and proline metabolism. Subsequently, 10 potential biomarkers were selected based on their

biological significance and the receiver operating characteristic (ROC) curve analysis, including

10Z - nonadecenoic acid, 4 - hydroxyphenylpyruvic acid, indoleacetic acid etc.

After conducting an integrative multi omics analysis of the differential microbial communities and

metabolites, it was revealed that certain differential microbes, such as Haemophilus species,

Staphylococcus aureus, and Streptococcus pneumoniae, were significantly positively correlated

with specific metabolites, including 10Z - nonadecenoic acid, 4 - hydroxyphenylpyruvic acid, L -

kynurenine, and xanthine (P < 0.05). Differential microbes such as Lawsonella clevelandensis,

Pseudomonas aeruginosa, and Sphingomonas azotifigens exhibited significant negative

correlations with metabolites including L(+) - ornithine, guanidoacetic acid, and leucine (P < 0.05).

Conclusions:

This study elucidated the potential pathogenesis of T2-CRS from the perspectives of microbiomics,

metabolomics, and their integrative analysis, with the aim of providing new directions for the

diagnosis and targeted treatment of T2-CRS.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

PROVIDER: MTBLS13192 | MetaboLights | 2025-10-21

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	a_MTBLS13192_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS13192_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS13192_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular
	m_MTBLS13192_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

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Research on the microbiota and metabolic characteristics of type 2 chronic rhinosinusitis based on the analysis of 16S amplicon sequencing combined with metabolomics

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