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

Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis Yunfan Zhang, Fan Yuan, Zheng Liu, Xiaoxi Huang, Junsheng Hong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4862816/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Inflammation Research → Version 1 posted 7 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic rhinosinusitis (CRS) is an inflammatory sinonasal disease with high heterogeneity and different endotypes 1 . 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 treatment 2 . 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 CRS 16 , 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. Materials and Methods 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 EPOS2020 5 , 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 criteria 5 . AECRS was defined as an acute worsening of sinonasal symptoms in the last four weeks in patients with underlying CRS 5 . 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 scale 17 . 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 sinuses 18 . 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 study 19 . 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 protocol 20 , 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 0.19.6 22 and merged by FLASH version 1.2.7 23 . 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 algorithm 25 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. Results 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 (10 9 /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 (10 9 /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 (10 9 /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. 1 A). 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. 1 B). 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. 1 C). 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 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%), 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. 2 A). 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. 2 A). 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. 2 B). 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. 3 A), and ROC curves were performed to evaluate the predictive power (Fig. 3 B). 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. 4 A. 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. 4 B). 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. 4 B, 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. Discussion 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 CRS 28 . 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 microbiota 29 . 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 CRS 5 . 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 dysbiosis 34 . 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 environment 35 . Xanthomonas has been reported in AECRS 8 and was associated with impaired respiratory function in pulmonary emphysema patients 36 . 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 Sphingomonadaceae 39 due to the activation of iNKT cells by glycosphingolipids. Faecalibacterium was reported to be dominant in allergic rhinitis 40 and was enriched in children with severe asthma 41 . 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 hypersecretion 42 . And LPS regulated the eosinophilic airway inflammation via the COX-2/PGE (2) axis in the mechanism of CRS 43 . 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 DNA 46 . 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 pathology 47 . 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 symptoms 9 . 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 CRSwNP 48 . 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. Conclusions 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. Declarations 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. References Orlandi RR, Kingdom TT, Smith TL, et al. International consensus statement on allergy and rhinology: rhinosinusitis 2021. Int Forum Allergy Rhinol. 2021 Mar;11(3):213-739. Dawei W, Benjamin SB, Yongxiang W. Definition and characteristics of acute exacerbation in adult patients with chronic rhinosinusitis: a systematic review. Journal of otolaryngology-head and neck surgery. 2020 Aug 18;49(1):62. Phillips KM, Hoehle LP, Bergmark RW, Caradonna DS, Gray ST, Sedaghat AR. Acute Exacerbations Mediate Quality of Life Impairment in Chronic Rhinosinusitis. J Allergy Clin Immunol Pract. 2017;5(2):422-6. 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The difference in nasal bacterial microbiome diversity between chronic rhinosinusitis patients with polyps and a control population. Int Forum Allergy Rhinol. 2019;9(6):582-92. Liu C, Zhao D, Ma W, Guo Y, Wang A, Wang Q, et al. Denitrifying sulfide removal process on high-salinity wastewaters in the presence of Halomonas sp. Appl Microbiol Biotechnol. 2016;100(3):1421-6. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i90. Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957-63. Vandelaar LJ, Hanson B, Marino M, Yao WC, Luong AU, Arias CA, et al. Analysis of Sinonasal Microbiota in Exacerbations of Chronic Rhinosinusitis Subgroups. OTO open. 2019;3(3):2473974X19875100. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. 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Lena TB, Martin D, Markus E, Marion E, Susanne K, Christoph B, et al. Environmental and mucosal microbiota and their role in childhood asthma. Allergy. 2017 Jan;72(1):109-119. Watanabe K, Aritomi T, Toyoshima H, Senju S, Yoshida M. [Pathogenic bacteria isolated from the sputum of the patients with pulmonary emphysema]. Kansenshogaku Zasshi. 1995;69(11):1251-9. Lieberman D, Lieberman D, Printz S, Ben-Yaakov M, Lazarovich Z, Ohana B, et al. Atypical pathogen infection in adults with acute exacerbation of bronchial asthma. Am J Respir Crit Care Med. 2003;167(3):406-10. Lieberman D, Lieberman D, Shmarkov O, Gelfer Y, Ben-Yaakov M, Lazarovich Z, et al. Serological evidence of Legionella species infection in acute exacerbation of COPD. Eur Respir J. 2002;19(3):392-7. Huang YJ, Nelson CE, Brodie EL, Desantis TZ, Baek MS, Liu J, et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J Allergy Clin Immunol. 2011;127(2):372-81.e1-3. Darren R, Ahmed B, Elizabeth E, Zhixin L, Paramasivan S, Raquel A, et al. The Association Between Disease Severity and Microbiome in Chronic Rhinosinusitis. Laryngoscope. 2019 Jun;129(6):1265-1273. Goldman DL, Chen Z, Shankar V, Tyberg M, Vicencio A, Burk R. Lower airway microbiota and mycobiota in children with severe asthma. J Allergy Clin Immunol. 2018;141(2):808-11.e7. Headland SE, Dengler HS, Xu D, Teng G, Everett C, Ratsimandresy RA, et al. Oncostatin M expression induced by bacterial triggers drives airway inflammatory and mucus secretion in severe asthma. Sci Transl Med. 2022;14(627):eabf8188. Rodríguez D, Keller AC, Faquim-Mauro EL, de Macedo MS, Cunha FQ, Lefort J, et al. Bacterial lipopolysaccharide signaling through Toll-like receptor 4 suppresses asthma-like responses via nitric oxide synthase 2 activity. J Immunol. 2003;171(2):1001-8. Hoffman S, Nolin J, McMillan D, Wouters E, Janssen-Heininger Y, Reynaert N. Thiol redox chemistry: role of protein cysteine oxidation and altered redox homeostasis in allergic inflammation and asthma. Journal of cellular biochemistry. 2015;116(6):884-92. Stephenson ST, Brown LA, Helms MN, Qu H, Brown SD, Brown MR, et al. Cysteine oxidation impairs systemic glucocorticoid responsiveness in children with difficult-to-treat asthma. J Allergy Clin Immunol. 2015;136(2):454-61.e9. Ezraty B, Gennaris A, Barras F, Collet JF. Oxidative stress, protein damage and repair in bacteria. Nat Rev Microbiol. 2017;15(7):385-96. Zhu Z, Camargo CA, Jr., Raita Y, Freishtat RJ, Fujiogi M, Hahn A, et al. Nasopharyngeal airway dual-transcriptome of infants with severe bronchiolitis and risk of childhood asthma: A multicenter prospective study. J Allergy Clin Immunol. 2022;150(4):806-16. Zhao Y, Chen J, Hao Y, Wang B, Wang Y, Liu Q, et al. Predicting the recurrence of chronic rhinosinusitis with nasal polyps using nasal microbiota. Allergy. 2022 Feb;77(2):540-549. Additional Declarations No competing interests reported. Supplementary Files 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. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Inflammation Research → Version 1 posted Editorial decision: Revision requested 16 Dec, 2024 Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers invited by journal 21 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 07 Aug, 2024 First submitted to journal 05 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4862816","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351058915,"identity":"38bf257f-b0c7-4090-b93b-5aa503338dc3","order_by":0,"name":"Yunfan Zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunfan","middleName":"","lastName":"Zhang","suffix":""},{"id":351058917,"identity":"e1c1e2c3-42a6-46f6-8b33-d82bfeae98f1","order_by":1,"name":"Fan Yuan","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yuan","suffix":""},{"id":351058918,"identity":"4d24210c-4e30-46bc-b296-20e47d873938","order_by":2,"name":"Zheng Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Liu","suffix":""},{"id":351058919,"identity":"b654fc79-27ee-4c5f-82ed-a499d9fedf1e","order_by":3,"name":"Xiaoxi Huang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxi","middleName":"","lastName":"Huang","suffix":""},{"id":351058920,"identity":"a6f5442f-d061-48f4-ac0d-8ab90fdc2f36","order_by":4,"name":"Junsheng Hong","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junsheng","middleName":"","lastName":"Hong","suffix":""},{"id":351058921,"identity":"183828b5-2b9b-43d5-9628-24a675c38eb3","order_by":5,"name":"Feifan Chang","email":"","orcid":"","institution":"Beijing Institute of Heart Lung and Blood Vessel Diseases","correspondingAuthor":false,"prefix":"","firstName":"Feifan","middleName":"","lastName":"Chang","suffix":""},{"id":351058922,"identity":"3f8dde2f-bfec-40ab-a5dd-493d6b6ddd4a","order_by":6,"name":"Dawei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDCCA0DEA2KwMx848OEHSVqY2RIPzuwhUgsDRAuP8WEONiJ08B0/Y3jgbdthOfNmng+HgZrl+cUO4NcieSbH4ODctsPGMod5NxwusGAwnDk7Ab8WgwNpCYd5tx1OnMEM1DKDhyHB4DYhLeefwbTwPDjMw0aMlhvJB2BaGIjTInnj8YGDc/+lG0swsxkAA1mCsF/4zic2f3hzxlpOgr358YcPP2zk+aUJaEEHEqQpHwWjYBSMglGAHQAAF7pKJoJ8PkMAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-08-05 14:45:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4862816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4862816/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00011-025-01995-9","type":"published","date":"2025-01-11T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66330455,"identity":"4d730535-7286-496a-ab96-bf532f17812b","added_by":"auto","created_at":"2024-10-10 13:27:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3329057,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences of microbiota in nasal cavity composition in AE. (A) Mean relative abundance of major phyla in healthy control, AE, and NAE; (B) Mean relative abundance of major genus in healthy control, AE, and NAE; (C)Taxonomic cladogram generated by LDA effect size analysis from phylum to genus level. LEfSe analysis revealed that the absolute abundance of 77 genera was significantly different among the three groups (LDA\u0026gt;2, P\u0026lt;0.05). Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/b7118f89e06b3d8871cc1872.jpg"},{"id":66329563,"identity":"74226014-8844-4bce-8b95-2e19a9ba88cf","added_by":"auto","created_at":"2024-10-10 13:19:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254331,"visible":true,"origin":"","legend":"\u003cp\u003eRelations among the nasal microbiome, objective measurements and the disease status of acute exacerbation. (A) Correlation among the relative abundance of disease-associated microbial communities and related clinical indicators based on the Spearman correlation analysis. Analysis was based on patients from the AE and NAE groups. (B) ROC curve of the binary logistic regression model based on the EDN level; *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001. Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/79c68f1ee063f30061f3a225.jpg"},{"id":66329264,"identity":"2d16fe50-d5a9-4d05-b8ac-e0f8345e9869","added_by":"auto","created_at":"2024-10-10 13:11:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":981351,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive diagnosis model based on the microbiome signature of the nasal cavity. (A)The Genera importance ranking chart was determined with the mean decrease in accuracy based on a random forest model. From top to bottom, higher values indicated more importance. The analysis is based on 77 patients (29 healthy controls, 28 AE, and 20 NAE); (B)ROC curve of the multivariable logistic regression model using 15 disease-associated genera was used to evaluate the classification performance. Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/5b1200136a17e0f0801d8314.jpg"},{"id":66329268,"identity":"6b347c47-0968-4a76-8796-fc6cc6a8a0e7","added_by":"auto","created_at":"2024-10-10 13:11:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1373674,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted metabolic profile of the nasal microbiome. The 16S rRNA data were further analyzed as indicated by PICRUSt. (A)PCA score 2D plots of the KEGG pathways; (B)Statistical significance differences were assessed by using Kruskal-Wallis test with with Bonferroni corrections among three groups and Mann-Whitney U test between two groups (p\u0026lt;0.05). The analysis is based on 77 patients in total (29 HCs, 28 AEs, and 20 NAEs). *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001. Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/0561c7049ced169a6334da04.jpg"},{"id":66329564,"identity":"162fced6-5f89-4c1d-95a8-91520b000e5d","added_by":"auto","created_at":"2024-10-10 13:19:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":935039,"visible":true,"origin":"","legend":"\u003cp\u003eFolate biosynthesis(A), Sulfur relay system(B) and Cysteine and methionine metabolism(C) were significantly different among three groups. (D)Pairwise comparison showed that these pathways were significantly decreased in subjects with AEs and HCs, as well as in subjects with NAEs and HCs. The analysis is based on 77 patients in total (29 HCs, 28 AEs, and 20 NAEs). *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001. Abbreviations: HC, healthy control; AE, chronic rhinosinusitis with acute exacerbation; NAE, chronic rhinosinusitis without acute exacerbation.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/37792d6a6c71813c9a726f63.jpg"},{"id":73694192,"identity":"971f49b7-82a4-417c-b99d-13a782dcdcb1","added_by":"auto","created_at":"2025-01-13 16:12:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7873344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/1b7df41d-950a-43a3-93e4-d4ad53b21446.pdf"},{"id":66329266,"identity":"df1e736f-2af5-4173-bfc9-a8f0d37f1aa7","added_by":"auto","created_at":"2024-10-10 13:11:29","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":743573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e 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.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4862816/v1/8686142124f37975678aada2.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic rhinosinusitis (CRS) is an inflammatory sinonasal disease with high heterogeneity and different endotypes\u003csup\u003e1\u003c/sup\u003e. 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 treatment\u003csup\u003e2\u003c/sup\u003e. Acute exacerbation of CRS (AECRS) was a major, independent driver of diminished quality of life and morbidity\u003csup\u003e3, 4\u003c/sup\u003e. Therefore, it is necessary to explore the pathogenesis of AE among patients with CRS.\u003c/p\u003e \u003cp\u003eThe 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\u003csup\u003e5\u0026ndash;7\u003c/sup\u003e. 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\u003csup\u003e5, 8\u003c/sup\u003e. 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\u003csup\u003e9\u0026ndash;13\u003c/sup\u003e. Risk factors for AE among patients with CRS included asthma, nasal polyps, allergic rhinitis, and eosinophil count\u0026thinsp;\u0026ge;\u0026thinsp;150/\u0026micro;L\u003csup\u003e9, 12\u003c/sup\u003e, 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.\u003c/p\u003e \u003cp\u003ePrevious research on the microbiology of AECRS was mainly based on bacterial culture or automated VITEK\u0026reg; device\u003csup\u003e6,7,14,15\u003c/sup\u003e. 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 CRS\u003csup\u003e16\u003c/sup\u003e, 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.\u003c/p\u003e \u003cp\u003eTherefore, 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.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003ePatients with CRS (N\u0026thinsp;=\u0026thinsp;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 EPOS2020\u003csup\u003e5\u003c/sup\u003e, 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\u0026thinsp;=\u0026thinsp;28) based on the EPOS2020 criteria\u003csup\u003e5\u003c/sup\u003e. AECRS was defined as an acute worsening of sinonasal symptoms in the last four weeks in patients with underlying CRS\u003csup\u003e5\u003c/sup\u003e. CRS without AE (N\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSubjective and objective assessment of CRS disease severity\u003c/h2\u003e \u003cp\u003eThe 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 scale\u003csup\u003e17\u003c/sup\u003e. 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 sinuses\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of nasal mucus eosinophil-derived neurotoxin levels and serum eosinophils\u003c/h2\u003e \u003cp\u003eA centrifugal extraction device with a polyvinyl alcohol sponge (Medtronic, Minneapolis, MN) was utilized to collect nasal mucus according to our previous study\u003csup\u003e19\u003c/sup\u003e. A small piece of sponge of the same size (12\u0026times;5\u0026times;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\u0026deg;C to recover the fluid. Aliquots of 80 ul each were prepared and stored at -80\u0026deg;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\u0026rsquo;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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSwab sample collection, DNA extraction, and sequence process\u003c/h2\u003e \u003cp\u003eSwab 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 protocol\u003csup\u003e20\u003c/sup\u003e, total microbial genomic DNA was extracted from swab samples using the E.Z.N.A.\u0026reg; 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\u0026rsquo;-ACTCCTACGGGAGGCAGCAG-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;) by an ABI GeneAmp\u0026reg; 9700 PCR thermocycler (ABI, CA, USA)\u003csup\u003e21\u003c/sup\u003e. 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\u0026trade; 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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic processing of sequence data\u003c/h2\u003e \u003cp\u003eRaw FASTQ files were de-multiplexed using an in-house perl script, and then quality-filtered by fastp version 0.19.6\u003csup\u003e22\u003c/sup\u003e and merged by FLASH version 1.2.7\u003csup\u003e23\u003c/sup\u003e. Sequences with more than 97% similarity were assigned to the same operational taxonomic units (OTUs)\u003csup\u003e24\u003c/sup\u003e. UPARSE 7.0 was used to perform cluster analysis on the OTUs. Species annotation for OTU representative sequences used the RDP classifier Bayesian algorithm\u003csup\u003e25\u003c/sup\u003e 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)\u003csup\u003e26\u003c/sup\u003e was used to predict function profiles of microbial communities, and Statistical Analysis of Metagenomic Profiles (STAMP)\u003csup\u003e27\u003c/sup\u003e analyzed statistically significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (with interquartile range), or n (%) according to the data distribution. The 1-sample Kolmogorov\u0026ndash;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\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the enrolled patients\u003c/h2\u003e \u003cp\u003eThe study cohort included patients with AECRS (N\u0026thinsp;=\u0026thinsp;28), CRS without AE (N\u0026thinsp;=\u0026thinsp;20), and healthy controls (N\u0026thinsp;=\u0026thinsp;29). The characteristics of the total cohort are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;29, 38%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAECRS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;28, 36%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRS without AE\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;20, 26%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.45\u0026thinsp;\u0026plusmn;\u0026thinsp;13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (58.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (24.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRSwNP, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergy, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNOT-22 score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNSS, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS for sinonasal symptom, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLebel scale, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.50(5.25\u0026ndash;15.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00(2.00\u0026ndash;9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLK, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00(0.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(1.25-4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.00\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell counts (10\u003csup\u003e9\u003c/sup\u003e/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.40(5.14\u0026ndash;6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.775(5.52\u0026ndash;8.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.15(4.84\u0026ndash;7.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte counts (10\u003csup\u003e9\u003c/sup\u003e/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72(1.43\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11(1.62\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86(1.48\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophil counts (10\u003csup\u003e9\u003c/sup\u003e/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.03\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18(0.92\u0026thinsp;\u0026minus;\u0026thinsp;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10(0.07\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.36\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56(0.27\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.76\u0026ndash;4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDN (ng/ml), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262.45(211.44-324.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488.65(340.00-954.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398.95(308.15-450.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAECRS, 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.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiota differences among healthy control, AECRS, and CRS without AE\u003c/h2\u003e \u003cp\u003eThe main components of the nasal microbiota were first analyzed at the phylum level. The nasal microbiome of all subjects was represented primarily by \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eBacteroidetes\u003c/em\u003e. \u003cem\u003eFirmicutes\u003c/em\u003e was the most dominant phyla in all three groups, followed by \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eBacterioidetes\u003c/em\u003e, as measured by mean relative abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). 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, \u003cem\u003eStaphylococcus\u003c/em\u003e was the highest in both groups (40.16% in healthy controls and 25.51% in AECRS), and \u003cem\u003eCorynebacterium_1\u003c/em\u003e 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 \u003cem\u003eCorynebacterium_1\u003c/em\u003e and 25.82% for \u003cem\u003eStaphylococcus\u003c/em\u003e). The third abundant genus was \u003cem\u003eMoraxella\u003c/em\u003e in healthy controls (3.21%) and \u003cem\u003eDolosigranulum\u003c/em\u003e in AECRS (3.38%) and CRS without AE (1.77%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Overall, the taxonomic bacteria belonging to \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003ePatescibacteria\u003c/em\u003e, and \u003cem\u003eThermotogae\u003c/em\u003e were significantly higher in AECRS compared to both healthy controls and CRS without AE (Linear discriminant analysis\u0026thinsp;=\u0026thinsp;LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 [P\u0026thinsp;\u0026lt;\u0026thinsp;0.05]). 77 bacterial taxa showed distinct relative abundances among healthy controls, AECRS, CRS, and without AE (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 [P\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;1%), and most of them were increased dramatically in AECRS compared to healthy controls and CRS without AE. As for the three genera (MRA\u0026thinsp;\u0026gt;\u0026thinsp;1%), significant decreases in \u003cem\u003eCorynebacterium_1\u003c/em\u003e and significant increases in \u003cem\u003eRalstonia\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e were observed in AECRS compared to both healthy controls and CRS without AE (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 [P\u0026thinsp;\u0026lt;\u0026thinsp;0.05]).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe nasal microbiome is associated with AE in patients with CRS\u003c/h2\u003e \u003cp\u003eThe disease-associated genera were significantly and positively correlated with the SNOT-22 score, TNSS score, Lebel score, eosinophil counts, and basophil counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A total of 18 genera were found to be highly associated with severity measurements, and all of them belong to the rare constituents (MRA\u0026thinsp;\u0026lt;\u0026thinsp;1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Surprisingly, our results showed that increased diversity measured by the Shannon index was significantly related to TNSS (R\u0026thinsp;=\u0026thinsp;0.450, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), eosinophil count (R\u0026thinsp;=\u0026thinsp;0.485, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and C-reactive protein (R\u0026thinsp;=\u0026thinsp;0.381, P\u0026thinsp;=\u0026thinsp;0.008). Our study showed EDN may be a potential marker for diagnosing AE with AUC value of 0.820 (95%CI\u0026thinsp;=\u0026thinsp;0.705\u0026ndash;0.936) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Besides, EDN level was positively correlated with SNOT-22 score (R\u0026thinsp;=\u0026thinsp;0.319, P\u0026thinsp;=\u0026thinsp;0.008) and eosinophil counts (R\u0026thinsp;=\u0026thinsp;0.425, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in patients with CRS. \u003cem\u003eXanthomonas\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.354, P\u0026thinsp;=\u0026thinsp;0.027) and \u003cem\u003eLegionella\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.391, P\u0026thinsp;=\u0026thinsp;0.014) were significantly positively correlated with the level of nasal mucus EDN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredicting nasal microbiome-based signature for discriminating AE\u003c/h2\u003e \u003cp\u003eRandom 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and ROC curves were performed to evaluate the predictive power (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The AUC value of 0.870 (95%CI\u0026thinsp;=\u0026thinsp;0.784\u0026ndash;0.955) suggested that the nasal microbiota had the potential to diagnose AE. In the 15 genera, 7 belonged to the phylum \u003cem\u003eProteobacteria\u003c/em\u003e, 4 belonged to \u003cem\u003eFirmicutes\u003c/em\u003e, 1 belonged to \u003cem\u003eActinobacteria\u003c/em\u003e, 1 belonged to \u003cem\u003eChlamydiae\u003c/em\u003e, 1 belonged to \u003cem\u003eThermotogae\u003c/em\u003e. They might be used as biomarkers for discriminating AE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial functional dysbiosis in patients with AECRS\u003c/h2\u003e \u003cp\u003eThrough 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. 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\u0026thinsp;\u0026gt;\u0026thinsp;0.1%) were tested to be differently abundant (P\u0026thinsp;\u0026lt;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo be noted, when MRA\u0026thinsp;\u0026gt;\u0026thinsp;0.1% and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.5 in both AECRS \u0026amp; healthy controls and AECRS \u0026amp; 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e 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\u0026thinsp;\u0026gt;\u0026thinsp;0.20, three pathways were differed significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after correction for multiple testing at false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Folate biosynthesis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sulfur relay system (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cysteine and methionine metabolism (P\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeven pathways significantly enriched in acute exacerbation of chronic rhinosinusitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;29, 38%)\u003c/p\u003e \u003cp\u003eMRA, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAECRS\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;28, 36%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRS without AE (N\u0026thinsp;=\u0026thinsp;20, 26%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value for AECRS and healthy controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value for AECRS and CRS without AE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipopolysaccharide biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipopolysaccharide biosynthesis proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial motility proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell motility and secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePores ion channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAECRS, acute exacerbation of chronic rhinosinusitis; CRS, chronic rhinosinusitis; AE, acute exacerbation; MRA, mean relative abundance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite 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)\u003csup\u003e24\u003c/sup\u003e; meanwhile, Liang et al. found that nasal microbiome significantly altered in eosinophilic CRS as compared to non-eosinophilic CRS\u003csup\u003e28\u003c/sup\u003e. 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 microbiota\u003csup\u003e29\u003c/sup\u003e. Thus, exploring the patterns of nasal dysbiosis during AE via 16S rRNA sequencing is necessary, which might promote future precision treatment.\u003c/p\u003e \u003cp\u003eHere, 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 \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e. At the genus level, our finding was consistent with previous findings\u003csup\u003e6, 29\u003c/sup\u003e. Although \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eCorynebacterium_1\u003c/em\u003e 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 \u003cem\u003eRalstonia\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e. These genera did alter remarkably according to LEfSe analysis, which had been proved to be related to the development of CRS\u003csup\u003e8,9,16,22\u003c/sup\u003e. \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e were recognized as core sinus microbiome in CRS\u003csup\u003e5\u003c/sup\u003e. Evidence for an inverse relationship between \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e supported that \u003cem\u003eCorynebacterium\u003c/em\u003e protected patients from respiratory illness and exacerbation\u003csup\u003e30\u0026ndash;33\u003c/sup\u003e. Previous studies found that \u003cem\u003eStaphylococcus aureus\u003c/em\u003e was one of the predominant bacteria in the sinus microbiome in AECRS patients\u003csup\u003e8, 15, 29\u003c/sup\u003e. Therefore, the decreased \u003cem\u003eCorynebacterium\u003c/em\u003e in AE may increase the risk for AE by provoking \u003cem\u003eStaphylococcus aureus\u003c/em\u003e 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 \u003cem\u003eCorynebacterium\u003c/em\u003e, laid the foundation for other pathogenic bacteria\u0026rsquo;s invasion and colonization and finally led to dysbiosis\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAssociation between inflammation parameters, disease, and severity measurements were also analyzed. Interestingly, genera with MRA\u0026thinsp;\u0026gt;\u0026thinsp;0.1% barely correlated with disease activity and severity. However, the disease severity measurements have closer relations to rare constituents (MRA\u0026thinsp;\u0026lt;\u0026thinsp;1%), indicating that the heterogeneity and the development of AECRS may depend more on rare constituents. \u003cem\u003eFacklamia\u003c/em\u003e was isolated from milk and cow samples and reported in mattress dust to be relatively abundant in farming environment\u003csup\u003e35\u003c/sup\u003e. \u003cem\u003eXanthomonas\u003c/em\u003e has been reported in AECRS\u003csup\u003e8\u003c/sup\u003e and was associated with impaired respiratory function in pulmonary emphysema patients\u003csup\u003e36\u003c/sup\u003e. \u003cem\u003eLegionella\u003c/em\u003e was a typical pathogen for the AE of different airway disease\u003csup\u003e37, 38\u003c/sup\u003e. Our study found that \u003cem\u003eXanthomonas\u003c/em\u003e and \u003cem\u003eLegionella\u003c/em\u003e 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 \u003cem\u003eSphingomonadaceae\u003c/em\u003e\u003csup\u003e39\u003c/sup\u003e due to the activation of iNKT cells by glycosphingolipids. \u003cem\u003eFaecalibacterium\u003c/em\u003e was reported to be dominant in allergic rhinitis\u003csup\u003e40\u003c/sup\u003e and was enriched in children with severe asthma\u003csup\u003e41\u003c/sup\u003e. 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\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003cp\u003eOur 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 hypersecretion\u003csup\u003e42\u003c/sup\u003e. And LPS regulated the eosinophilic airway inflammation via the COX-2/PGE (2) axis in the mechanism of CRS\u003csup\u003e43\u003c/sup\u003e. 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\u003csup\u003e44, 45\u003c/sup\u003e. Methionine is a critical amino acid required for polyamine biosynthesis, essential for fast-growing bacteria, which must constantly synthesize polyamines to replicate their DNA\u003csup\u003e46\u003c/sup\u003e. 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 pathology\u003csup\u003e47\u003c/sup\u003e. Further studies are required to explore the molecular mechanism and extent of action of the above metabolism pathways in developing AECRS.\u003c/p\u003e \u003cp\u003eCurrent diagnosis criteria and indications for treatment for AECRS were based on the sudden worsening symptoms\u003csup\u003e9\u003c/sup\u003e. 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 CRSwNP\u003csup\u003e48\u003c/sup\u003e. In our study, a model comprising of \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eXanthomonas\u003c/em\u003e, \u003cem\u003eAchromobacter\u003c/em\u003e, \u003cem\u003eSphingobium\u003c/em\u003e, \u003cem\u003eLegionella\u003c/em\u003e, \u003cem\u003eCellvibrio\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eFlaviflexus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eThermobrachium\u003c/em\u003e, \u003cem\u003eAtopostipes\u003c/em\u003e, \u003cem\u003eVagococcus\u003c/em\u003e, \u003cem\u003eNeochlamydia\u003c/em\u003e and \u003cem\u003eFervidobacterium\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eNatural 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).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003cp\u003eThis study was approved by the Peking University Third Hospital Ethics Committee (number 2020100X).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD 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.\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003enone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrlandi RR, Kingdom TT, Smith TL, et al. International consensus statement on allergy and rhinology: rhinosinusitis 2021. Int Forum Allergy Rhinol. 2021 Mar;11(3):213-739.\u003c/li\u003e\n\u003cli\u003eDawei W, Benjamin SB, Yongxiang W. Definition and characteristics of acute exacerbation in adult patients with chronic rhinosinusitis: a systematic review. Journal of otolaryngology-head and neck surgery. 2020 Aug 18;49(1):62.\u003c/li\u003e\n\u003cli\u003ePhillips KM, Hoehle LP, Bergmark RW, Caradonna DS, Gray ST, Sedaghat AR. Acute Exacerbations Mediate Quality of Life Impairment in Chronic Rhinosinusitis. 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Allergy. 2022 Feb;77(2):540-549.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"inflammation-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inre","sideBox":"Learn more about [Inflammation Research](http://link.springer.com/journal/11)","snPcode":"11","submissionUrl":"https://submission.nature.com/new-submission/11/3","title":"Inflammation Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chronic rhinosinusitis, acute exacerbation, nasal microbiome, dysbiosis, disease severity, prediction","lastPublishedDoi":"10.21203/rs.3.rs-4862816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4862816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDysbiosis 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to healthy controls and patients with CRS without AE, AECRS presented with a substantial decrease of the \u003cem\u003eCorynebacterium_1\u003c/em\u003e and a significant increase of \u003cem\u003eRalstonia\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e at the genus level (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 [P\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.784\u0026ndash;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.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 13:11:24","doi":"10.21203/rs.3.rs-4862816/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-16T09:51:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T14:21:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25899507667043864882394710592448516430","date":"2024-09-08T09:29:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-21T10:34:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-07T21:39:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-07T21:39:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Inflammation Research","date":"2024-08-05T14:44:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"inflammation-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"inre","sideBox":"Learn more about [Inflammation Research](http://link.springer.com/journal/11)","snPcode":"11","submissionUrl":"https://submission.nature.com/new-submission/11/3","title":"Inflammation Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a991f992-2e2a-4425-9c9c-c63f5636e39c","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:06:29+00:00","versionOfRecord":{"articleIdentity":"rs-4862816","link":"https://doi.org/10.1007/s00011-025-01995-9","journal":{"identity":"inflammation-research","isVorOnly":false,"title":"Inflammation Research"},"publishedOn":"2025-01-11 15:57:33","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-10-10 13:11:24","video":"","vorDoi":"10.1007/s00011-025-01995-9","vorDoiUrl":"https://doi.org/10.1007/s00011-025-01995-9","workflowStages":[]},"version":"v1","identity":"rs-4862816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4862816","identity":"rs-4862816","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}