Project description: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 exhibitedhigher 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 liketryptophan 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.

Project description:Background: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by both motor and non-motor symptoms, including olfactory dysfunction, which often precedes motor symptoms by several years. However, the underlying mechanisms linking olfactory dysfunction in PD to biological changes remain unclear. Recent studies suggest a potential connection between PD's olfactory dysfunction and alterations in the nasal microbiome and metabolome, but comprehensive investigations are still lacking.This study aims to explore the interplay between nasal microbiota, metabolites, and olfactory function in PD, identify potential biomarkers for early diagnosis, and investigate therapeutic targets for olfactory dysfunction in PD. Methods: From October 1, 2023, to September 30, 2024, 66 potential participants were enrolled, including PD patients with varying degrees of olfactory dysfunction and healthy controls. Nasal samples were collected for 16S rRNA sequencing and metabolomic analysis using GC-MS. The study also used MPTP-induced PD mouse models to assess the protective effects of cholic acid on olfactory function and dopaminergic neurons. Results: The study found significant differences in nasal microbiota composition and metabolite profiles between PD patients and controls, with correlations to the severity of olfactory dysfunction. Specifically, the relative abundances of Corynebacterium, Dolosigranulum, Muribacter, and Moraxella were elevated in the PD group, while the abundances of Hydrogenophaga, Staphylococcus, Klebsiella, and Bacillus decreased. Metabolomic analysis revealed that cholic acid, octanal, hexadecanol, and phytol were significantly altered in PD patients compared to controls, with cholic acid showing potential as a diagnostic biomarker (AUC > 0.97). In MPTP-induced PD mouse models, cholic acid treatment significantly reduced latencies in detecting buried food pellets, and increased tyrosine hydroxylase (TH)-positive fluorescence intensity in the olfactory bulb and substantia nigra. However, cholic acid did not significantly ameliorate motor symptoms in PD mice, as assessed by the open field test, and did not prevent the loss of TH+ cells in the striatum. Conclusion: The findings highlight the interplay between the nasal microbiome, metabolome, and olfactory dysfunction in PD, suggesting that modulating metabolic pathways could be a promising therapeutic strategy. Future research should focus on larger sample sizes, longitudinal studies, and further validation of biomarkers and therapeutic targets to enhance the understanding and management of PD.

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:Pseudorabies virus infection causes intestinal flora disorder in animals, leading to diarrhea. To explore the effect of inactivated pseudorabies vaccine immunization on pseudorabies virus infection, this study sequenced the serum metabolome and intestinal microbiota of Hu sheep, analyzed their differential expression and conducted a combined analysis. The results showed that all the Hu sheep in the vaccine immunization group survived, while those in the non-immunization group died. The expressions of quercetin, 3, 7-dimethylquercetin, 5-Methoxyindoleacetate, L-methionine, L-glutamic acid, thymine, adenine and 20-HETE were upregulated in the serum of the vaccine immunization group (P<0.05). The expressions of amifostine, hemialdehyde succinate and trehalose were down-regulated (P<0.05). The microbiota structure and abundance in the vaccine immunization group changed significantly, and the beneficial bacteria Christensenellaceae_R-7_group, Bifidobacterium, Solibacillus, and Rikenellaceae_RC9_gut_group increased. The results of the combined omics analysis showed that the changes of metabolites such as quercetin, 3, 7-dimethylquercetin, 5-Methoxyindoleacetate and adenine were positively correlated with the abundance changes of Rikenellaceae_RC9_gut_group (P<0.05); L-methionine and 20-HETE were positively correlated with adenine and Solibacillus (P<0.05); Succinate semialdehyde was significantly negatively correlated with Rikenellaceae_RC9_gut_group. The above results indicate that after inactivated pseudorabies vaccine immunization of Hu sheep, it affects metabolites (quercetin, 3, 7-dimethylquercetin, 5-Methoxyindoleacetate adenine, L-methionine and 20-HETE) by maintaining beneficial intestinal bacteria (Rikenella aceae_RC9_group and Solibacillus). Metabolites jointly regulate the generation of inflammatory mediators, influence the function of immune cells and participate in the regulation of oxidative stress, thereby exerting an immune protective effect. This study provides an important theoretical basis for a deeper understanding of the immune protection mechanism of inactivated pseudorabies vaccine.

			Action	DRS
	MTBLS7584	Other
	FILES	Other
	a_MTBLS7584_GC-MS_positive__metabolite_profiling.txt	Txt
	a_MTBLS7584_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS7584_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt

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Disturbed Microbiota-Metabolites-Immune Interaction Network is Associated with Olfactory Dysfunction in Patients with Chronic Rhinosinusitis

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