Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis

doi:10.21203/rs.3.rs-4862816/v1

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Research Article

Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis

https://doi.org/10.21203/rs.3.rs-4862816/v1

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Background

Dysbiosis of the nasal microbiome is considered to be related to the acute exacerbation of chronic rhinosinusitis (AECRS). The microbiota in the nasal cavity of AECRS patients and its association with disease severity has rarely been studied. This study aimed to characterize nasal dysbiosis in a prospective cohort of patients with AECRS.

Methods

We performed a cross-sectional study of 28 patients with AECRS, 20 patients with chronic rhinosinusitis (CRS) without acute exacerbation (AE), and 29 healthy controls using 16S rRNA gene sequencing. Subjective and objective assessments of CRS disease severity during AE were also collected.

Results

Compared to healthy controls and patients with CRS without AE, AECRS presented with a substantial decrease of the Corynebacterium_1 and a significant increase of Ralstonia and Acinetobacter at the genus level (LDA score > 2.0 [P < 0.05]). Furthermore, 29 genera with a substantial alteration in AECRS were rare constituents of the microbiome, of which 18 rare genera were highly associated with subjective and objective disease severity. Moreover, a combination of 15 genera could differentiate patients with AECRS with an area under the curve of 0.870 (95% CI = 0.784–0.955). Prediction of microbial functional pathways involved significantly enhanced lipopolysaccharide biosynthesis pathways and significantly decreased folate biosynthesis, sulfur relay system, and cysteine and methionine metabolism pathways in patients with AECRS.

Conclusions

The rare nasal microbiota correlated with disease status and disease severity in patients with AECRS. The knowledge about the pattern of the nasal microbiome and its metabolomic pathway may contribute to the fundamental understanding of AECRS pathophysiology.

Chronic rhinosinusitis

acute exacerbation

nasal microbiome

dysbiosis

disease severity

prediction

Chronic rhinosinusitis (CRS) is an inflammatory sinonasal disease with high heterogeneity and different endotypes¹. Except for chronic baseline symptoms, patients with CRS tend to have acute exacerbation (AE), which is generally defined based on the deterioration in pre-existing symptoms with a return to baseline symptoms after treatment². Acute exacerbation of CRS (AECRS) was a major, independent driver of diminished quality of life and morbidity^{3, 4}. Therefore, it is necessary to explore the pathogenesis of AE among patients with CRS.

The precise etiology of AE in patients with CRS is still unclear. The significant alternation of the nasal microbiome has been regarded as a critical factor for the onset of AE in patients with CRS^5–7. It has been proposed that CRS is characterized by nasal dysbiosis in the form of an altered balance of the mucosal microbiota rather than a single pathogen^{5, 8}. And the nasal dysbiosis may elicit a host inflammatory response, triggering the onset of AE. Furthermore, a series of studies have focused on the risk factors and local immunological characterization of patients with CRS during the AE^9–13. Risk factors for AE among patients with CRS included asthma, nasal polyps, allergic rhinitis, and eosinophil count ≥ 150/µL^{9, 12}, indicating a critical role of type 2 inflammation in the pathogenesis of AECRS. How these risk factors are associated with dysbiosis of the mucosal microbiota during the AE has not been explored.

Previous research on the microbiology of AECRS was mainly based on bacterial culture or automated VITEK® device^6,7,14,15. The above methods were weaker than 16S rRNA in terms of sensitivity and accuracy in identifying pathogens. With traditional culture technology, a study by Brook et al. found that the microbiological profile of patients with AECRS was similar to that of patients with stable CRS¹⁶, making it hard to identify unique and essential patterns of altered nasal microbiome during AE in patients with CRS. How the microbiota varies based on 16S rRNA among patients with AECRS remains unknown.

Therefore, we utilized 16S rRNA sequencing to explore the patterns of nasal dysbiosis, investigate the association of microbiota characteristics of AE with disease severity, and thus analyze the role and likelihood of microbiota characteristics in predicting disease exacerbation among patients with CRS.

Study design and patients

Patients with CRS (N = 48) over the age of 18 were recruited in rhinology clinic from December 1, 2020, through December 1, 2021. CRS was defined according to the diagnostic described in criteria EPOS2020⁵, which included persisting sinonasal symptoms for more than 12 weeks and sinonasal inflammation confirmed by computed tomography. A total of 29 healthy controls were also recruited from the health examination center. Inclusion criteria included a diagnosis of AECRS (N = 28) based on the EPOS2020 criteria⁵. AECRS was defined as an acute worsening of sinonasal symptoms in the last four weeks in patients with underlying CRS⁵. CRS without AE (N = 20) were also included. Exclusion criteria included patients with cystic fibrosis, ciliary dysfunction, autoimmune disease, or immunodeficiency. Patients receiving antibiotics orally or topically in the last four weeks were also excluded. Demographic data were recorded for each patient, and allergies were diagnosed with a positive skin test result or history. All patients signed the informed consent, and this study was approved by the Ethics Committee.

Subjective and objective assessment of CRS disease severity

The 22-item Sino-Nasal Outcome Test (SNOT-22), total nasal symptom scores (TNSS), sinus visual analog scale (VAS) for sinonasal symptoms, and Lebel scale were used to evaluate patient-reported symptom severity. A sinus computed tomography (CT) scan was obtained from every participant. CT radiographic severity scores were calculated according to the Lund-Mackay (LM) scoring scale¹⁷. All the patients received an endoscopy examination by a senior rhinologist, and the Lund-Kennedy (LK) endoscopy scoring system grades visual pathologic states within the nose and paranasal sinuses¹⁸.

Measurement of nasal mucus eosinophil-derived neurotoxin levels and serum eosinophils

A centrifugal extraction device with a polyvinyl alcohol sponge (Medtronic, Minneapolis, MN) was utilized to collect nasal mucus according to our previous study¹⁹. A small piece of sponge of the same size (12×5×3 mm) was inserted into the nasal cavity and kept in place for 5 minutes. The sponge was transferred into a centrifugal extraction device and was centrifuged at 1500 g for 15 min at 4°C to recover the fluid. Aliquots of 80 ul each were prepared and stored at -80°C for further analysis. The level of mucus eosinophil-derived neurotoxin (EDN) was quantified using commercial Human EDN ELISA kits, with a detection range of 78 ng/ml-5000 ng/ml (CSB-E17923h, Cosmo Bio Co., LTD. CA, USA), following the manufacturer’s instructions. A total of 5 mL of peripheral venous blood was collected from each participant before surgery for complete blood cell percentage by an automated analyzer (Beckman Coulter, Miami, Florida, USA).

Swab sample collection, DNA extraction, and sequence process

Swab samples for DNA extraction were collected from patients and controls by an endoscope guided to the middle meatus and rotated at least five times. The collected samples were placed into 2-mL sterile tubes without enzyme, placed on ice immediately after collection, and stored at -80 ℃ within 2 hours until DNA extraction. According to the manufacturer's instructions and previous study protocol²⁰, total microbial genomic DNA was extracted from swab samples using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA)²¹. All samples were amplified in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer's instructions and quantified using Quantus™ Fluorometer (Promega, USA). Purified amplicons were pooled in equimolar amounts and paired-end sequenced on the Illumina MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols by Sinotech Genome Technology Co. (Shanghai, China).

Bioinformatic processing of sequence data

Raw FASTQ files were de-multiplexed using an in-house perl script, and then quality-filtered by fastp version 0.19.6²² and merged by FLASH version 1.2.7²³. Sequences with more than 97% similarity were assigned to the same operational taxonomic units (OTUs)²⁴. UPARSE 7.0 was used to perform cluster analysis on the OTUs. Species annotation for OTU representative sequences used the RDP classifier Bayesian algorithm²⁵ against the SILVA (SSU123) 16S rRNA gene database. The unweighted Unifrac distance matrices were calculated by the Quantitative Insights into Microbial Ecology (QIIME) pipeline. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt)²⁶ was used to predict function profiles of microbial communities, and Statistical Analysis of Metagenomic Profiles (STAMP)²⁷ analyzed statistically significant differences.

Statistical analysis

Continuous variables are presented as mean ± standard deviation, median (with interquartile range), or n (%) according to the data distribution. The 1-sample Kolmogorov–Smirnov test was applied to test whether variables were normally distributed. Significant differences among multiple groups were determined using a Kruskal-Wallis test. Bonferroni corrections were conducted. The receiver operating characteristic (ROC curves), and the area under the curve (AUC) were calculated to assess the predictive diagnostic performance. Spearman correlation analysis was used to determine the correlation between microbiome and disease status. All tests were two-tailed, and a p-value < 0.05 was considered significant. Statistical analysis was performed using the SPSS (Version 26.0; IBM Corp), and graphical outputs were performed using Prism GraphPad version 8 software program (GraphPad Software, San Diego, California) and OmicStudio tools.

Clinical characteristics of the enrolled patients

The study cohort included patients with AECRS (N = 28), CRS without AE (N = 20), and healthy controls (N = 29). The characteristics of the total cohort are described in Table 1. No significant differences were found among the three groups concerning age, gender, BMI, smoking status, and alcoholic status. Regarding the SNOT-22, TNSS, VAS for sinonasal symptoms and Lebel scale, AECRS had significantly higher scores than CRS without AE and healthy controls. As for the objective disease severity of CRS, no significant differences were found in the LM scores between AECRS and CRS without AE. However, the LK scores were significantly higher among AECRS than CRS without AE and healthy controls. There were no differences in white blood cell counts, lymphocyte counts, eosinophil counts, or C-reactive protein among the three groups. The levels of nasal mucus EDN were significantly different among the three groups, and AECRS had the significantly highest levels of nasal mucus EDN.

Table 1

Demographic characteristics of the cohorts
Characteristic	Healthy controls (N = 29, 38%)	AECRS (N = 28, 36%)	CRS without AE (N = 20, 26%)	P value
Age (year)	40.67 ± 14.14	42.56 ± 11.47	48.45 ± 13.82	0.267
Male, n (%)	17 (58.62)	21 (75.00)	16 (80.00)	0.214
BMI, mean ± SD	23.44 ± 2.59	24.18 ± 4.31	25.94 ± 2.75	0.171
Smoker, n (%)	7 (24.14)	9 (32.14)	8 (40.00)	0.495
Drinker, n (%)	2 (6.90)	7 (25.00)	6 (30.00)	0.087
CRSwNP, n (%)	NA	12 (63.16)	7 (36.84)	0.698
Allergy, n (%)	2 (33.33)	4 (66.67)	0(0.00)	0.186
SNOT-22 score, mean ± SD	13.10 ± 2.39	50.19 ± 8.27	20.20 ± 9.81	< 0.001
TNSS, mean ± SD	2.27 ± 3.24	6.38 ± 3.77	2.18 ± 2.36	0.005
VAS for sinonasal symptom, mean ± SD	1.87 ± 2.67	7.31 ± 2.47	4.09 ± 2.98	< 0.001
Lebel scale, mean ± SD	2.40 ± 2.85	5.38 ± 3.22	1.27 ± 1.74	0.003
LM, median (IQR)	NA	8.50(5.25–15.50)	6.00(2.00–9.00)	0.131
LK, median (IQR)	0.00(0.00–1.00)	3.00(1.25-4.00)	1.00(0.00–2.00)	< 0.001
White blood cell counts (10⁹/L), median (IQR)	6.40(5.14–6.83)	6.775(5.52–8.17)	6.15(4.84–7.15)	0.378
Lymphocyte counts (10⁹/L), median (IQR)	1.72(1.43–2.23)	2.11(1.62–2.70)	1.86(1.48–2.42)	0.190
Eosinophil counts (10⁹/L), median (IQR)	0.07(0.03–0.21)	0.18(0.92 − 0.55)	0.10(0.07–0.20)	0.086
C-reactive protein (mg/L), median (IQR)	0.83(0.36–1.14)	0.56(0.27–1.51)	1.02(0.76–4.43)	0.190
EDN (ng/ml), median (IQR)	262.45(211.44-324.35)	488.65(340.00-954.59)	398.95(308.15-450.07)	< 0.001
AECRS, acute exacerbation of chronic rhinosinusitis; CRS, chronic rhinosinusitis; AE,acute exacerbation; SD, standard deviation; IQR, interquartile range; CRSwNP, CRS with nasal polyps; SNOT-22, Sino-Nasal Outcome test 22; TNSS, total nasal symptom scores; VAS, sinus visual analog scale symptom scoring; LM, Lund-Mackay scoring system; LK, Lund-Kennedy scoring system; CT, computed tomography; EDN, eosinophil-derived neurotoxin; NA, not available.

Microbiota differences among healthy control, AECRS, and CRS without AE

The main components of the nasal microbiota were first analyzed at the phylum level. The nasal microbiome of all subjects was represented primarily by Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes. Firmicutes was the most dominant phyla in all three groups, followed by Actinobacteria, Proteobacteria, and Bacterioidetes, as measured by mean relative abundance (Fig. 1A). The characteristics and alterations in the community structure of the microbiota in the nasal cavity were further analyzed at the genus level. For the healthy controls and AECRS, Staphylococcus was the highest in both groups (40.16% in healthy controls and 25.51% in AECRS), and Corynebacterium_1 ranked second (35.09% in healthy controls and 19.87% in AECRS). However, in patients with CRS without AE, it was the opposite (33.52% for Corynebacterium_1 and 25.82% for Staphylococcus). The third abundant genus was Moraxella in healthy controls (3.21%) and Dolosigranulum in AECRS (3.38%) and CRS without AE (1.77%) (Fig. 1B).

To identify differentially abundant taxa, we performed a linear discriminant analysis (LDA) effect size (LEfSe) analysis on the nasal microbiota composition of the three groups (Fig. 1C). Overall, the taxonomic bacteria belonging to Proteobacteria, Bacteroidetes, Patescibacteria, and Thermotogae were significantly higher in AECRS compared to both healthy controls and CRS without AE (Linear discriminant analysis = LDA score > 2.0 [P < 0.05]). 77 bacterial taxa showed distinct relative abundances among healthy controls, AECRS, CRS, and without AE (LDA score > 2.0 [P < 0.05]). The genera were defined as rare constituents when the mean relative abundance (MRA) was less than 1%. At the genus level, 32 genera were identified and significantly altered among three groups, among which 29 genera were rare constituents of the microbiome (MRA < 1%), and most of them were increased dramatically in AECRS compared to healthy controls and CRS without AE. As for the three genera (MRA > 1%), significant decreases in Corynebacterium_1 and significant increases in Ralstonia and Acinetobacter were observed in AECRS compared to both healthy controls and CRS without AE (LDA score > 2.0 [P < 0.05]).

The nasal microbiome is associated with AE in patients with CRS

The disease-associated genera were significantly and positively correlated with the SNOT-22 score, TNSS score, Lebel score, eosinophil counts, and basophil counts (Fig. 2A). A total of 18 genera were found to be highly associated with severity measurements, and all of them belong to the rare constituents (MRA < 1%) (Fig. 2A). Surprisingly, our results showed that increased diversity measured by the Shannon index was significantly related to TNSS (R = 0.450, P < 0.001), eosinophil count (R = 0.485, P < 0.001) and C-reactive protein (R = 0.381, P = 0.008). Our study showed EDN may be a potential marker for diagnosing AE with AUC value of 0.820 (95%CI = 0.705–0.936) (Fig. 2B). Besides, EDN level was positively correlated with SNOT-22 score (R = 0.319, P = 0.008) and eosinophil counts (R = 0.425, P < 0.001) in patients with CRS. Xanthomonas (R = 0.354, P = 0.027) and Legionella (R = 0.391, P = 0.014) were significantly positively correlated with the level of nasal mucus EDN.

Predicting nasal microbiome-based signature for discriminating AE

Random forest was performed using the list of AE-associated genera to provide a 15-genera predictive model to identify AE. Significant genera were selected by Mean Decrease Accuracy (Fig. 3A), and ROC curves were performed to evaluate the predictive power (Fig. 3B). The AUC value of 0.870 (95%CI = 0.784–0.955) suggested that the nasal microbiota had the potential to diagnose AE. In the 15 genera, 7 belonged to the phylum Proteobacteria, 4 belonged to Firmicutes, 1 belonged to Actinobacteria, 1 belonged to Chlamydiae, 1 belonged to Thermotogae. They might be used as biomarkers for discriminating AE.

Microbial functional dysbiosis in patients with AECRS

Through functional prediction on all 77 samples (healthy controls, AECRS and CRS without AE) using PICRUSt, we identified a total of 328 KEGG pathways. PCA score 2D plots of the KEGG pathways were shown in Fig. 4A. A clear separation of the disease group (CRS with and without AE) and healthy control group could be observed. Samples from the healthy control group were tightly clustered, while the samples from the disease group were scattered, further indicating the excellent stability of the metabolomics system in healthy controls and dysbiosis in CRS. Seventy-five pathways (MRA > 0.1%) were tested to be differently abundant (P < 0.05) among healthy controls, AECRS, and CRS without AE, among which 17 pathways were more abundant in AECRS than healthy controls and CRS without AE, 7 pathways were less abundant in AECRS than healthy controls and CRS without AE (Fig. 4B).

To be noted, when MRA > 0.1% and p-value < 0.5 in both AECRS & healthy controls and AECRS & CRS without AE (Mann-Whitney U test) were controlled, seven metabolic pathways were more abundant in AECRS than in healthy controls and CRS without AE, including bacterial chemotaxis, lipopolysaccharide (LPS) biosynthesis and proteins, bacterial motility proteins, cell motility and secretion and pores ion channels (Fig. 4B, Table 2). To further identify metabolic pathways that might be involved in the pathogenesis of AE, STAMP was used to analyze statistically significant differences among the three groups. At a threshold effect size > 0.20, three pathways were differed significantly (P < 0.05) after correction for multiple testing at false discovery rate (FDR) < 0.10 (Fig. 5). Folate biosynthesis (P < 0.001), sulfur relay system (P < 0.001), and cysteine and methionine metabolism (P = 0.011) are significantly different among the three groups. Patients with AECRS presented with the significantly lowest levels among the three groups. These data suggest that microbial metabolites might be involved in the pathogenesis of the AECRS.

Table 2

Seven pathways significantly enriched in acute exacerbation of chronic rhinosinusitis
Pathway	Healthy controls (N = 29, 38%) MRA, %	AECRS (N = 28, 36%)	CRS without AE (N = 20, 26%)	P value for AECRS and healthy controls	P value for AECRS and CRS without AE
Lipopolysaccharide biosynthesis	0.10	0.16	0.10	0.028	0.004
Lipopolysaccharide biosynthesis proteins	0.19	0.23	0.17	0.037	0.016
Bacterial motility proteins	0.21	0.48	0.29	0.037	< 0.001
Bacterial chemotaxis	0.08	0.16	0.11	0.049	< 0.001
Cell motility and secretion	0.09	0.12	0.09	0.031	0.001
Chromosome	1.31	1.29	1.20	0.037	< 0.001
Pores ion channels	0.29	0.32	0.27	0.043	0.017
AECRS, acute exacerbation of chronic rhinosinusitis; CRS, chronic rhinosinusitis; AE, acute exacerbation; MRA, mean relative abundance.

Despite recent studies that have further discovered the microbiome in different phenotypic subgroups of CRS, there is still a lack of in-depth research on how the microbiome in the nasal cavity changes during AECRS. A recent study by Vandelaar et al. pointed out that no microbiology differences were noted during AE among the different CRS phenotypes (CRS with nasal polyps, CRS without nasal polyps, and allergic fungal rhinosinusitis)²⁴; meanwhile, Liang et al. found that nasal microbiome significantly altered in eosinophilic CRS as compared to non-eosinophilic CRS²⁸. These results suggested that the inflammation endotype rather than the clinical phenotype may be associated with the nasal microbiome. Due to the insufficient understanding of the microbiology of AE and a lack of objective diagnostic criteria, a short-term antibiotic was usually recommended only based on physician experience, which may promote antibiotic resistance, biofilm formation, and disruption of the natural microbiota²⁹. Thus, exploring the patterns of nasal dysbiosis during AE via 16S rRNA sequencing is necessary, which might promote future precision treatment.

Here, we first identified significant differences in microbiome composition among AECRS, healthy controls, and CRS without AE at the phylum level. The differences were mainly found in phyla Proteobacteria and Bacteroidetes. At the genus level, our finding was consistent with previous findings^{6, 29}. Although Staphylococcus and Corynebacterium_1 remained the core composition in nasal microbiome among the three groups, we found that the abundance of several important pathogenic bacteria in AECRS varied significantly, including the Ralstonia and Acinetobacter. These genera did alter remarkably according to LEfSe analysis, which had been proved to be related to the development of CRS^8,9,16,22. Corynebacterium and Staphylococcus were recognized as core sinus microbiome in CRS⁵. Evidence for an inverse relationship between Staphylococcus aureus and Corynebacterium supported that Corynebacterium protected patients from respiratory illness and exacerbation^30–33. Previous studies found that Staphylococcus aureus was one of the predominant bacteria in the sinus microbiome in AECRS patients^{8, 15, 29}. Therefore, the decreased Corynebacterium in AE may increase the risk for AE by provoking Staphylococcus aureus to switch from symbiotic to toxic, which needs further confirmation by more advanced sequencing methods and interpretation at the species level. The loss of dominant bacteria, especially Corynebacterium, laid the foundation for other pathogenic bacteria’s invasion and colonization and finally led to dysbiosis³⁴.

Association between inflammation parameters, disease, and severity measurements were also analyzed. Interestingly, genera with MRA > 0.1% barely correlated with disease activity and severity. However, the disease severity measurements have closer relations to rare constituents (MRA < 1%), indicating that the heterogeneity and the development of AECRS may depend more on rare constituents. Facklamia was isolated from milk and cow samples and reported in mattress dust to be relatively abundant in farming environment³⁵. Xanthomonas has been reported in AECRS⁸ and was associated with impaired respiratory function in pulmonary emphysema patients³⁶. Legionella was a typical pathogen for the AE of different airway disease^{37, 38}. Our study found that Xanthomonas and Legionella were positively related to eosinophil count and EDN level, indicating that they may play an essential role in developing eosinophil inflammation. Evidence has shown that bronchial hyperresponsiveness was associated with Sphingomonadaceae³⁹ due to the activation of iNKT cells by glycosphingolipids. Faecalibacterium was reported to be dominant in allergic rhinitis⁴⁰ and was enriched in children with severe asthma⁴¹. For the first time, our study proved that the above microbiota of rare constituents in the nasal cavity was correlated with disease severity among patients with AECRS. Considering that an increased Shannon index was found in AECRS and the different genera are mostly rare compositions with MRA < 1%, we further calculated the OTU amount among the three groups, and the results showed no difference, indicating there may be more related to the variation in microbiome composition.

Our research used PICUST to predict the variations in the metabolism pathways of dysbiosis. In previous research, bacterial-associated LPS was found to induce the expression of oncostatin M in severe asthma, leading to airway inflammation and mucus hypersecretion⁴². And LPS regulated the eosinophilic airway inflammation via the COX-2/PGE (2) axis in the mechanism of CRS⁴³. Therefore, our finding of enhanced LPS biosynthesis during AE compared to CRS without AE and healthy controls proved that the LPS was involved in the pathology of inflammation in AE. Previous studies reported that cysteine oxidations contributed to chronic inflammation and the development of asthma, and the level of cysteine significantly decreased in plasma of children with severe asthma^{44, 45}. Methionine is a critical amino acid required for polyamine biosynthesis, essential for fast-growing bacteria, which must constantly synthesize polyamines to replicate their DNA⁴⁶. Our study found that cysteine and methionine metabolism decreased significantly in patients with AECRS compared to healthy controls and CRS without AE. Fatty acid metabolism and glycolytic pathways were previously found to play essential roles in asthma pathology⁴⁷. Further studies are required to explore the molecular mechanism and extent of action of the above metabolism pathways in developing AECRS.

Current diagnosis criteria and indications for treatment for AECRS were based on the sudden worsening symptoms⁹. Our research confirmed the effectiveness of typical clinical tools in assessing the disease severity of AECRS, such as scales for symptoms, nasal endoscopy, and CT scans. Besides, our research found that EDN increased significantly during AE and presented a significantly positive association with disease severity. The ROC curve with the AUC value of 0.820 suggested that EDN may serve as a biomarker for AECRS. As the dysbiosis of nasal microbiota played a necessary role in AE, utilizing nasal microbial signatures to discriminate CRS disease status has great potential. The prediction model based on microbiota for disease state and diagnosis has been previously explored in the recurrence of CRSwNP⁴⁸. In our study, a model comprising of Klebsiella, Xanthomonas, Achromobacter, Sphingobium, Legionella, Cellvibrio, Pseudomonas, Flaviflexus, Enterococcus, Thermobrachium, Atopostipes, Vagococcus, Neochlamydia and Fervidobacterium was able to diagnose AE from CRS without AE and healthy controls. These findings suggested the possibility of noninvasive nasal sample profiles to diagnose AE among patients with CRS.

The innovations of our study included first utilizing 16S rRNA to identify the microbiome dysbiosis of AECRS, regardless of phenotypes, and aiming to find a universe variation during AE. Nevertheless, this study has several limitations. First, the sample size was relatively small and this was a single-center study. A multicenter study with large sample size will be needed. Second, further validation cohorts are needed to confirm that the nasal microbiome-based classifier can accurately distinguish patients with AECRS and can identify patients with different inflammation status.

In conclusion, our study first found that the rare nasal microbiota correlated with disease status and disease severity in patients with AECRS. The knowledge about the pattern of the nasal microbiome and its metabolomic pathway may contribute to the fundamental understanding of AECRS pathophysiology.

Funding information

Natural Science Foundation of China (82000954), Beijing Science and Technology Nova Program (Z201100006820086), Beijing Hospitals Authority Youth Program (QML20190617), Beijing Hospitals Authority Clinical Medicine Development of Special Funding (XMLX202136), and the Key clinical projects of Peking University Third Hospital (BYSYZD2023029).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

This study was approved by the Peking University Third Hospital Ethics Committee (number 2020100X).

Author Contribution

D W designed the study, analyzed the data, and wrote the manuscript. Y Z analyzed the data and drafted the manuscript. FY, ZL, JH, and FC collected the data. XH analyzed and revised the manuscript. All authors reviewed the manuscript.

Acknowledgement:

none.

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No competing interests reported.

SupplementaryFigure1.jpg
Supplementary Figure 1. Relative abundance of major genera in healthy control (n=29), AE (n=28), and NAE (n=20). Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.

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Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Study design and patients

Subjective and objective assessment of CRS disease severity

Measurement of nasal mucus eosinophil-derived neurotoxin levels and serum eosinophils

Swab sample collection, DNA extraction, and sequence process

Bioinformatic processing of sequence data

Statistical analysis

Results

Clinical characteristics of the enrolled patients

Microbiota differences among healthy control, AECRS, and CRS without AE

The nasal microbiome is associated with AE in patients with CRS

Predicting nasal microbiome-based signature for discriminating AE

Microbial functional dysbiosis in patients with AECRS

Discussion

Conclusions

Declarations

Funding information

Author Contribution

Acknowledgement:

References

Additional Declarations

Supplementary Files

Status:

Version 1