Bronchoalveolar Lavage Proteomics in Acute Exacerbation of Bronchiectasis

Bronchoalveolar Lavage Proteomics in Acute Exacerbation of Bronchiectasis | 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 Bronchoalveolar Lavage Proteomics in Acute Exacerbation of Bronchiectasis Ju Yeon Lee, Jiyoul Yang, Jin Young Kim, Yeji Do, Min-Sik Kim, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5322072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The molecular pathophysiology underlying the development of bronchiectasis with acute exacerbation at the proteomic level has not been clarified using bronchoalveolar lavage fluid samples. This study aimed to evaluate the bronchoalveolar lavage fluid inflammatory profiles associated with acute exacerbation of bronchiectasis. Methods: We analyzed the bronchoalveolar lavage fluid specimens from 4 patients in the acute exacerbation status and 4 patients in a stable status using liquid chromatography-tandem mass spectrometry. Results: A total of 1,577 proteins were identified using proteomic analysis, with 127 differentially expressed proteins. Of 127 differentially expressed proteins, 23 proteins showed more than 2-fold differences between the acute exacerbation and stable status groups. The acute exacerbation status was associated with 18 upregulated proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2)and five downregulated proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC). Pathway analysis revealed that the neutrophil degranulation pathway (R-HSA-6798695) was the most enriched pathway in these proteins, followed by the C-type lectin receptor pathway (R-HSA-5621481). Conclusion: The bronchoalveolar lavage fluid protein expression in patients in the acute exacerbation status of bronchiectasis was significantly different from that in patients in the stable status, indicating that neutrophil degranulation and C-type lectin receptor pathways are the most enriched pathways during acute exacerbation. Bronchiectasis Bronchoalveolar lavage Proteomics Neutrophil degranulation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Bronchiectasis is a chronic respiratory disorder characterized by irreversible dilation and damage to the bronchial walls, leading to impaired mucus clearance and increased susceptibility to recurrent bronchial infections [ 1 ]. The prevalence and socioeconomic burden of bronchiectasis have increased worldwide [ 2 – 4 ]. The acute exacerbation (AE) of bronchiectasis, defined as a sustained worsening of the respiratory symptoms, is associated with poor treatment outcomes, and the prevention and/or appropriate management of AE is crucial for altering the natural course of this disease [ 5 , 6 ]. Accordingly, understanding the mechanisms underlying AE development is imperative for the development of novel treatments to reduce the occurrence of AE in bronchiectasis. Proteomics is a powerful and rapidly evolving field of study that offers an innovative lens for examining the intricate molecular signatures and protein dynamics [ 7 ]. Unlike genomics, which provides information about the genetic blueprint of an individual, proteomics enables the direct analysis of protein expression, modifications, and interactions within a biological system [ 8 , 9 ]. Thus, proteomic investigations can yield invaluable insights into the functional alterations in cellular pathways, which are crucial to understanding the pathogenesis of bronchiectasis. Previous studies evaluating the role of proteomics in the development of AE in bronchiectasis were performed using sputum samples [ 10 – 12 ]. Although these studies have revealed important novel findings, sputum samples have limitations in terms of contamination and these may not reflect true local inflammation (in the active parenchymal area of bronchiectasis) [ 11 ]. Therefore, to overcome these limitations, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS), a high-throughput proteomic screening method, to bronchoalveolar lavage (BAL) fluid samples obtained from patients with bronchiectasis. Our study aimed to determine whether a high-throughput proteomic screening method could reveal differentially expressed proteins associated with the AE of bronchiectasis. Methods Study population This prospective study evaluated 8 patients with bronchiectasis between January 2021 and January 2022 at Chungbuk National University Hospital, a tertiary referral hospital in Cheongju, Republic of Korea. Bronchiectasis was diagnosed by two pulmonologists (BHY and SHK) based on respiratory symptoms and computed tomography findings, according to published guidelines [ 13 , 14 ] During the study period, 4 patients in a stable status and 4 patients in an AE status were enrolled in the study. No attempt to match for age, sex, or body mass index (BMI) was made, as prior studies on both chronic obstructive pulmonary disease (COPD) and bronchiectasis found no relationship between the sputum proteome and these parameters [ 10 , 15 ]. The AE of bronchiectasis is defined as the worsening of three or more major symptoms for 48 h or more, necessitating a change in treatment [ 16 ]. Major symptoms include coughing, sputum volume/viscosity, sputum suppuration, dyspnea, exercise ability, fatigue, malaise, and hemoptysis. Moreover, we define a stable status as a condition in which a patient does not require treatment changes, including antibiotic or corticosteroid use, for 4 weeks or longer [ 16 , 17 ]. The study protocol was approved by the Institutional Review Board of the Chungbuk National University Hospital (application no. 2021-10-007). Primary outcomes The primary outcome of this study was to identify differences in BAL proteomics and related pathways associated with the AE status of bronchiectasis. Measurements of the clinical variables The baseline demographic characteristics included the age, sex, BMI (weight in kilograms divided by the height in meters squared), and smoking status (never, former, or current smokers). Dyspnea was evaluated using the modified Medical Round Council scale [ 18 ]. The presence of patient-reported physician-diagnosed comorbidities was assessed at baseline. The white blood cell count, neutrophil count, and high-sensitivity C-reactive protein levels were measured in all patients. Pre- and post-bronchodilator spirometry were performed according to the recommendations of the American Thoracic Society/European Respiratory Society [ 19 ]. The absolute values of forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) were recorded; additionally, the percentages of the predicted values for FEV 1 (FEV 1 %pred) and FVC (FVC %pred) were calculated using an automatic calculator by using a reference equation obtained from a representative Korean sample [ 20 ]. Microbiological analyses of specimens were performed using standard methods [ 21 ]. Conventional semi-quantitative bacterial and fungal cultures were also performed. All samples underwent initial Gram staining before culturing the BAL fluid if they met the Murray and Washington criteria [ 22 ]. Bronchoalveolar lavage BAL fluid and microbiological analyses were conducted for all patients. BAL procedures were performed by two pulmonologists (BHY and SHK) according to the American Thoracic Society recommendations [ 23 ]. Bronchoscopy was performed using a standard flexible bronchoscope (BF-1TQ260; Olympus, Tokyo, Japan). During the BAL, the scope was placed in a wedge position within the selected segmental bronchi. A total volume of normal saline between 100 and 300 ml, divided into three to five aliquots, was instilled through the channel of the bronchoscope. After the instillation of each aliquot, the instilled saline was gently retrieved using a negative suction pressure of less than 100 mmHg to avoid visible airway collapse. Comprehensive proteomic analysis of the bronchoalveolar lavage samples of patients with bronchiectasis Protein analysis of the BAL samples was conducted as illustrated in Fig. 1 . The detailed process is described in the next sections. Materials The High-Select™ Top14 abundant protein depletion resin (catalog number A36370), which was used for removing the 14 abundant proteins from the BAL samples, and the Tandem Mass Tag (TMT) Pro 16-plex isobaric label reagent set were purchased from Thermo Fisher Scientific (Rockford, USA). An Amicon Ultra centrifugal filter (3 kDa MWCO) and S-Trap mini spin columns were obtained from Merck Millipore (Billerica, USA) and ProFit (New York, USA), respectively. Sodium dodecyl sulfate, 1,4-dithiothreitho (DTT), iodoacetamide (IAA), ammonium bicarbonate (ABC), ammonium acetate, and triethylammonium bicarbonate (TEAB) buffers were purchased from Sigma-Aldrich. Trypsin Gold (mass spectrometry grade) from Promega (Madison, WI, USA) was used. Immunoaffinity depletion Fourteen abundant proteins in the BAL samples (albumin, IgA, IgD, IgE, IgG, IgG light chains, IgM, alpha-1-acid glycoprotein, alpha-1-antitrypsin, alpha-2-macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, and transferrin) were removed using the High-Select™ Top14 resin following the manufacturer's protocol. Briefly, the depletion spin columns were equilibrated to room temperature, and 10 µl of samples were directly added to the resin slurry in the column. The samples were mixed by inverting the column several times until the resin was completely homogenous. The mixture in the column was incubated under gentle end-over-end mixing for 10 min at room temperature. After incubation, the contents of the mini column were added to a 1.5 ml collection tube and centrifuged at 1,000 × g for 2 min. The 14 most abundant proteins were removed from the collected samples. The samples were filtered using an Amicon Ultra centrifugal filter (3 kDa MWCO) for concentration and desalting. Proteome sample preparation Depleted and desalted BAL samples were digested using S-Trap mini spin columns following the manufacturer's protocol. The proteins were denatured in sodium dodecyl sulfate and TEAB, reduced using TCEP, alkylated with IAA in the dark, and quenched with phosphoric acid. After adding the binding/wash buffer, the solution was centrifuged in the S-Trap column, washed, and digested with trypsin at 37°C for 16 h. Peptides were eluted using three different buffers and centrifuged. The resulting peptide solutions were pooled, dried, and quantified using a BCA assay. These peptides were then labeled with TMT-16 plex reagents, and the reaction was quenched using a hydroxylamine solution. The labeled samples were combined and dried for further analyses. High pH reversed-phase liquid chromatography for peptide fractionation TMT-labeled samples were fractionated using an XBridge BEH Shield RP18 column on a Nexera XR high-performance liquid chromatography (HPLC) system, and 40 fractions were collected and concentrated into 10 fractions for LC-MS/MS analysis. Liquid chromatography-tandem mass spectrometry These ten fractions were analyzed using an Easy nLC 1200 system and an Orbitrap Fusion Lumos mass spectrometer equipped with a nanoelectrospray source. The samples were initially trapped in a C18 precolumn and then separated using a C18 analytical column at a flow rate of 250 nl/min. The mobile phase consisted of water (A) and an acetonitrile-water-formic acid mixture (B). The LC gradient varied from 5–95% for phase B over a specified time course. The mass spectrometer was operated in the data-dependent mode, alternating between MS and MS/MS scans. The MS spectra were collected at a resolution of 120,000, whereas the MS/MS spectra were collected at a resolution of 60,000 with specific settings for ion injection times, AGC target values, and collision energy. The system used a 30 s exclusion time for previously fragmented ions. Pre-processing and data processing Prior to the LC-MS/MS analysis, sample preparation involved protein trapping to remove the non-essential proteins and contaminants from the BAL samples, thus concentrating the necessary proteins for analysis. This process successfully eliminated 14 abundant plasma proteins, including albumin and immunoglobulins, with only the relevant proteins remaining. Subsequently, protein digestion was performed using trypsin. These peptides were labeled with isobaric TMT agents for detailed quantitative analysis. HPLC was used to fractionate the labeled samples into 10 parts, simplifying their complexity, before performing LC-MS/MS. This method enables the accurate measurement, quantification, and identification of peptides, thereby providing essential data on diagnostic markers and proteolytic processes. Protein identification and quantitation The Integrated Proteomics Pipeline with built-in search engines (IP2, version 6.5.5, Integrated Proteomics) was used for data analysis using the UniProt human protein database (September 2021, reviewed). Reverse sequences of all proteins were appended to the database to calculate the false discovery rate. ProLuCID [ 24 ] was used to identify the peptides with a precursor mass error of 5 ppm and a fragment ion mass error of 50 ppm. Trypsin was selected as the enzyme to use because of two potentially missed cleavages. The TMT modification (+ 304.2071) of the N-terminus and lysine residue using the labeling reagent and carbamidomethylation of cysteine were chosen as static modifications. Methionine oxidation was chosen as the variable modification. The reporter ions were extracted from small windows (approximately 20 ppm) around their expected m/z values in the HCD spectrum. The output data files were filtered and sorted to compose the protein list using DTASelect (The Scripps Research Institute, USA), with two or more peptide assignments for protein identification and a false positive rate of less than 0.01 [ 25 ]. Data processing Quantitative analysis was conducted using Census in the IP2 pipeline (Integrated Proteomics, USA) with only unique peptides. The intensity of the reporter ion channel for a protein was calculated as the sum of the intensities of the reporter ions from all constituent peptides of the identified protein [ 26 ]. The reverse and potentially contaminating proteins were removed. The Perseus platform (version 2.0.5.0) was used for data processing. The intensity of the protein was input into Perseus, log 2 -transformed, and normalized to the median of the column for protein quantification. Student’s t-test was performed to select the significant proteins with a p-value between the two groups below 0.05 to identify the significant proteins. Moreover, pathway analysis of the significantly differentially expressed proteins was performed to determine the functional proteins whose expression differed between the two groups. The Perseus software was used to perform unsupervised hierarchical clustering and principal component analysis (PCA), and to generate volcano plots (Fig. 2 ). Gene ontology (GO) pathway enrichment analysis The proteins that remained altered following resuscitation in each tissue sample were subjected to GO pathway analysis using Metascape ( http://metascape.org ). Protein names were converted into Entrez gene IDs and redundant identifiers were merged into a single Entrez gene ID. Pathway and process enrichment analyses utilized various ontology sources, including GO biological processes, KEGG pathway, reactome gene sets, CORUM, PanGenBase, WikiPathways, and the PANTHER pathway. Significant terms (p 1.5) were clustered based on membership similarities and the most significant term within each cluster represented the respective cluster. P-values were calculated based on an accumulative hypergeometric distribution. Statistical analysis of the clinical variables Data are presented as the mean and standard deviation (SD) for the continuous variables and as frequency (percentage) for the categorical variables. Continuous variables were compared using the Mann–Whitney U test, and the Pearson chi-square test or Fisher’s exact test was used to compare the categorical variables, as appropriate. All tests were two-sided, and p-values < 0.05 indicated statistical significance. All statistical analyses were performed using the IBM SPSS Statistics for Windows (version 27.0; IBM Corp., Armonk, NY, USA). Results Baseline characteristics The baseline characteristics of the study population are summarized in Table 1 . Among the 8 patients, 4 patients were in the AE group and eight were in the stable group. No significant differences in the baseline characteristics were found between the AE and stable groups (all P > 0.05). Table 1 Baseline characteristics Total (n = 8) AE status (n = 4) Stable status (n = 4) P-value Age, years 61.6 (10.6) 58 (13.1) 65.3 (7.5) 0.374 Sex, male 2 (25.0) 2 (50.0) 0 (87.5) 0.429 BMI (kg/m 2 ) 20.0 (4.1) 21.2 (5.7) 18.8 (1.5) 0.434 Smoking history Current or ex-smoker 0 0 0 N/A Previous history of TB 0 0 0 N/A Comorbidities COPD 0 0 0 N/A Asthma 2 (25.0) 1 (25.0) 1 (25.0) 1.000 Cardiovascular disease 0 0 0 N/A Diabetes mellitus 1 (12.5) 1 (25.0) 0 1.000 Chronic kidney disease 0 0 0 N/A Neurologic disease 0 0 0 N/A Autoimmune disease 1 (14.3) 1 (33.3) 0 0.429 Malignancy 1 (12.5) 0 1 (25.0) 1.000 NTM-PD 3 (37.5) 1 (25.0) 2 (50.0) 1.000 Spirometry FVC, L 2.03 (0.7) 2.03 (1.0) 2.03 (0.2) 0.996 FVC, % predicted 66 (19.3) 60.4 (28.4) 71 (5.1) 0.539 FEV1, L 1.58 (0.6) 1.50 (0.9) 1.65 (0.1) 0.803 FEV1, % predicted 65 (20.6) 56.1 (28.1) 74.0 (5.4) 0.339 FEV1/FVC, % 77 (5.6) 73.3 (5.5) 81.3 (0.6) 0.067 Microbiology ‡ P. aeruginosa 1 (12.5) 1 (25.0) 0 1.000 mMRC 0.7 (0.8) 1 (1.0) 0.5 (0.6) 0.437 Lab findings WBC count 8265 (3781.0) 10433 (3,7) 6098 (2.7) 0.106 Neutrophil count, % 68 (6.5) 67.7 (8.4) 68.4 (5.2) 0.893 Eosinophil count, % 2 (1.2) 1.7 (0.5) 1.8 (1.8) 0.899 hs-CRP, mg/dl 1.72 (3.6) 2.94 (4.5) 0.51 (0.7) 0.406 Data are presented as the mean (standard deviation) or number (%). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; TB, tuberculosis; FVC, forced vital capacity; FEV 1 , forced expiratory volume in 1 second; NTM, nontuberculous mycobacteria; P. aeruginosa , Pseudomonas aeruginosa ; H. influenzae , Haemophilus influenzae ; S. aureus , Staphylococcus aureus ; K. pneumoniae , Klebsiella pneumoniae ; A. baumannii , Acinetobacter baumannii ; mMRC Modified Medical Research Council; WBC count, white blood cells count; hs-CRP, high-sensitivity C-reactive protein ‡ Klebsiella pneumonia and Staphylococcus aure us were identified in one patient. Proteome analysis Normalization, correlation, PCA, and protein variation In total, a total of 1,577 proteins were identified and quantified in stable and AE BAL samples using LC-MS/MS analysis, coupled with TMT labeling and HPLC fractionation into ten fractions ( Supplemental Table 1 ). The intensity of the TMT reporter ions was extracted from the TMT labelled peptides with an accuracy of 25 ppm. Figure 2 shows the sequential steps of the statistical analysis. The protein intensity summed by the TMT reporter ions from the peptide was normalized to the median of each sample after the log 2 transformation (Fig. 2 a). The intensity versus frequency histograms for all samples showed a Gaussian distribution (Fig. 2 b). The Pearson correlation values among all four stable and four AE samples were over 0.9 (Fig. 2 c). PCA was performed on all 127 significantly differentially expressed proteins after performing Student's t-test between the two groups (Fig. 2 d). It clearly discriminated two groups, with component 1 accounting for 72.9% of the variance and component 2 accounting for 9.3%. Hierarchical analysis (Fig. 2 e) showed that the expression of these 127 significant proteins increased (Fig. 2 f) and decreased (Fig. 2 g) in the AE groups. Detailed information on the 127 proteins was summarized in the Supplemental Table 2 . Of 127 differentially expressed proteins, 23 proteins showed more than 2-fold differences between the AE and stable status groups. Compared to the stable group, 18 proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2) were significantly upregulated by more than 2-fold in the AE group ( Supplementary Table 3 ). In contrast, 5 proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC) were significantly downregulated by more than 2-fold in the AE group compared to the stable group ( Supplementary Table 4 ). The volcano plot shows representative proteins whose expression was quantitatively increased (SPRR3, ORM1, S100A9, TPI1, and BPI) and decreased (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC) in the AE group compared to the stable group (Fig. 3 ; |log 2 value| ≥ 0.5). Overall, these differences indicate that increased neutrophilic inflammation and altered mucin and mucosal immunity are associated with the AE status of bronchiectasis. Functional analysis The 127 significant proteins obtained using the Student's t-test were analyzed via gene ontology (GO) using Metascape (Fig. 4 ). Twenty clusters enriched in all the 127 significant proteins are listed (accumulative hypergeometric p-values < 0.05). Neutrophil degranulation (R-HSA-6798695) was the most enriched pathway among those enriched in the 127 significant proteins, followed by C-type lection receptor (CLR) signaling (R-HSA-5621481). Discussion We conducted a proteomic analysis of BAL fluid using liquid chromatography-tandem mass spectrometry. Among the 1,577 proteins identified, 127 showed statistically significant differences between the patients with AE and those with stable bronchiectasis; we identified 18 proteins whose expression increased by more than 2-fold and 5 proteins whose expression decreased by more than 2-fold in the AE group compared to the stable group. GO analysis identified 20 functional clusters enriched in the significant proteins, with neutrophil degranulation being the most enriched pathway, followed by CLR signaling. While previous studies have employed sputum analysis for proteomics research in bronchiectasis [ 12 , 27 , 28 ], this study is the first to utilize BAL fluid samples to molecularly characterize the AE of bronchiectasis. Here, we found 127 proteins differentially expressed between the individuals in AE and stable status. Of these, 23 proteins showed marked differences demonstrating AE of bronchiectasis was associated with 18 upregulated proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2) and 5 downregulated proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC). Increased levels of CRP, S100A8, S100A9, BPI, and ORM1 suggest ongoing neutrophil inflammatory conditions, and are involved in the regulation of inflammatory processes and immune responses in chronic inflammatory lung disease [ 29 ]. The decreased levels of MUC5B and MUC5AC suggest that their crucial role in maintaining and protecting the mucus layer in the airways is interrupted in inflammatory lung disease, and the decreased IGHA1 levels indicate that the protective role of immunoglobulin A in mucosal immunity is impaired [ 30 , 31 ]. Overall, these results indicate that neutrophilic inflammation and an altered mucin and mucosal immunity are closely linked to the AE status of bronchiectasis, which is in line with a recent study that analyzed the sputum proteomics associated with the AE of bronchiectasis [ 12 ]. Collectively, the differences in these inflammatory proteins observed show that neutrophil degranulation (R-HSA-6798695) was the most enriched pathway, reinforcing its important role in exacerbating bronchiectasis. Neutrophilic inflammation is the main mechanism of chronic airway inflammation in bronchiectasis [ 32 ]. In addition, neutrophil degranulation, including the release of neutrophil elastase and myeloperoxidase, plays a significant role in the AE of bronchiectasis [ 33 , 34 ]. Neutrophils release various inflammatory mediators, including proteases, reactive oxygen species, and cytokines. Although neutrophil elastase has a positive role in bacterial killing through phagocytosis, it also has negative roles, such as airway mucus obstruction, alveolar epithelial injury by degradation of the extracellular matrix proteins, activation of proinflammatory signaling, and compromise of innate immunity[ 35 ] These can exacerbate the symptoms of patients and contribute to the cycle of infection and neutrophilic inflammation that characterizes the disease. Additionally, we observed that the CLR pathway is an important pathway related to the AE of bronchiectasis. The CLR pathway is involved in shaping immune responses to various types of infections (bacterial, viral, and fungal). CLRs can induce phagocytic, endocytic, anti-microbial, anti-inflammatory, and proinflammatory responses to protect against infection, and have several functions according to the signaling motifs in the cytoplasmic domains. Moreover, the key roles of CLRs in allergy, autoimmunity, and homeostasis maintenance have been studied[ 36 ]. A previous study showed that the expression of mannose binding lectin (MBL), a soluble CLR, is associated with the severity and AE of bronchiectasis [ 37 ]. This finding supports our results, which suggest that C-type lectin receptors play an important role in bronchiectasis and its AE. Our study has some limitations. First, the sample size is relatively small, and the study was performed at only one center in Korea. Therefore, further studies are required to validate these findings. Second, due to the small number of participants, we could not perform subgroup analyses according to the AE endotypes of bronchiectasis and bacterial colonization. Third, as our study was cross-sectional, we could not measure the BAL proteomic changes before and after AE of bronchiectasis. Conclusions In conclusion, different BAL protein expression levels were associated with the AE status of bronchiectasis. Neutrophil degranulation and CLR pathways are the major pathways involved in the AE of bronchiectasis. Future studies with larger cohorts should be performed to corroborate these findings. Declarations Acknowledgements None Author contributions J.Y.L., J.Y., H.Y.K., Y.D., M-S.K., H.L., and B.Y. conceived the study and performed the experiments. J.Y.L., J.Y., H.L., and B.Y. wrote the manuscript and designed the diagrams. H.L., and B.Y. edited the manuscript. D.E.K., G.M. I-S.J., E-G.K., J.K.C. and B.Y. critically reviewed the paper. Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of the Chungbuk National University Hospital (application no. 2021-10-007). Individual consent for this retrospective analysis was waived. Clinical trial number Not applicable. Conflicts of interest None declared. Data availability statemen The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Funding This work was supported by the Research Program funded by the National Research Council of Science and Technology (NST) (CRC22021-100). This work was supported by the Research Program funded by the National Research Council of Science and Technology (NST) (CRC22021-100). This work was supported by grants from the National Research Foundation (NRF) of Korea (No. 2020R1A5A2017476). Also, this work was supported by the NRF grant funded by the Korea government (MSIT) (No. 2022R1F1A1074749 to B.Y.). References Aliberti S, Goeminne PC, O'Donnell AE, Aksamit TR, Al-Jahdali H, Barker AF, Blasi F, Boersma WG, Crichton ML, De Soyza A, et al: Criteria and definitions for the radiological and clinical diagnosis of bronchiectasis in adults for use in clinical trials: international consensus recommendations. 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Carvalho PC, Xu T, Han X, Cociorva D, Barbosa VC, Yates III JR: YADA: a tool for taking the most out of high-resolution spectra. Bioinformatics 2009, 25: 2734-2736. Tabb DL, McDonald WH, Yates JR: DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. Journal of proteome research 2002, 1: 21-26. Raso C, Cosentino C, Gaspari M, Malara N, Han X, McClatchy D, Park SK, Renne M, Vadalà N, Prati U: Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis. Journal of proteome research 2012, 11: 3199-3210. Hull RC, Huang JTJ, Barton AK, Keir HR, Ellis H, Cookson WOC, Moffatt MF, Loebinger MR, Chalmers JD: Sputum Proteomics in Nontuberculous Mycobacterial Lung Disease. Chest 2022, 161: 1180-1191. D'Amato M, Iadarola P, Viglio S: Proteomic Analysis of Human Sputum for the Diagnosis of Lung Disorders: Where Are We Today? Int J Mol Sci 2022, 23 . Driscoll KE: Macrophage inflammatory proteins: biology and role in pulmonary inflammation. Exp Lung Res 1994, 20: 473-490. Lee IT, Yang CM: Inflammatory signalings involved in airway and pulmonary diseases. Mediators Inflamm 2013, 2013: 791231. Cheetham CJ, McKelvey MC, McAuley DF, Taggart CC: Neutrophil-Derived Proteases in Lung Inflammation: Old Players and New Prospects. Int J Mol Sci 2024, 25 . Gramegna A, Amati F, Terranova L, Sotgiu G, Tarsia P, Miglietta D, Calderazzo MA, Aliberti S, Blasi F: Neutrophil elastase in bronchiectasis. Respir Res 2017, 18: 211. Chalmers JD, Moffitt KL, Suarez-Cuartin G, Sibila O, Finch S, Furrie E, Dicker A, Wrobel K, Elborn JS, Walker B, et al: Neutrophil Elastase Activity Is Associated with Exacerbations and Lung Function Decline in Bronchiectasis. Am J Respir Crit Care Med 2017, 195: 1384-1393. Yang B, Lee H, Yun H, Seong C, Kim EG, Choi JK: Association between sputum myeloperoxidase concentration and acute exacerbation of bronchiectasis. Pulmonology 2024, 30: 401-405. Voynow JA, Shinbashi M: Neutrophil Elastase and Chronic Lung Disease. Biomolecules 2021, 11 . Hoving JC, Wilson GJ, Brown GD: Signalling C-type lectin receptors, microbial recognition and immunity. Cell Microbiol 2014, 16: 185-194. Chalmers JD, McHugh BJ, Doherty C, Smith MP, Govan JR, Kilpatrick DC, Hill AT: Mannose-binding lectin deficiency and disease severity in non-cystic fibrosis bronchiectasis: a prospective study. Lancet Respir Med 2013, 1: 224-232. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables240805Final.xlsx Supplementary Materials Supplementary Table 1. List of 1,577 identified and quantified proteins from BAL proteome samples from bronchiectasis patients in stable and AE status Abbreviations: AE, acute exacerbation Supplementary Table 2. List of 127 significantly differentially expressed BAL proteins between bronchiectasis patients in stable and AE status Abbreviations: AE, acute exacerbation Supplementary Table 3. List of 18 proteins whose difference values increased by more than two-fold among the proteins that passed t test. Supplementary Table 4. List of proteins whose difference values decreased by more than two-fold among the proteins that passed t test Cite Share Download PDF Status: Posted Version 1 posted 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-5322072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371906400,"identity":"13a14e62-cc97-4ab1-a350-0e1a71161cb3","order_by":0,"name":"Ju Yeon Lee","email":"","orcid":"","institution":"Korea Basic Science Institute","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"Yeon","lastName":"Lee","suffix":""},{"id":371906401,"identity":"adc78659-f766-4da7-98e8-035e96a02eb3","order_by":1,"name":"Jiyoul Yang","email":"","orcid":"","institution":"Chungbuk National University Hospital, Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiyoul","middleName":"","lastName":"Yang","suffix":""},{"id":371906402,"identity":"e39e4036-58b7-4093-b804-69f6e80e3322","order_by":2,"name":"Jin Young Kim","email":"","orcid":"","institution":"Korea Basic Science Institute","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Young","lastName":"Kim","suffix":""},{"id":371906403,"identity":"b9c71993-20ed-4b3d-b4f3-5dc79c116216","order_by":3,"name":"Yeji Do","email":"","orcid":"","institution":"DGIST","correspondingAuthor":false,"prefix":"","firstName":"Yeji","middleName":"","lastName":"Do","suffix":""},{"id":371906404,"identity":"9ef25013-d484-422d-b940-4870261a56a3","order_by":4,"name":"Min-Sik Kim","email":"","orcid":"","institution":"DGIST","correspondingAuthor":false,"prefix":"","firstName":"Min-Sik","middleName":"","lastName":"Kim","suffix":""},{"id":371906405,"identity":"ab34f081-e9dc-4b37-b677-0ebf1390543f","order_by":5,"name":"Dong Eun Kye","email":"","orcid":"","institution":"Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Eun","lastName":"Kye","suffix":""},{"id":371906406,"identity":"921f9285-5789-431a-87be-aa8c9b967ed3","order_by":6,"name":"Geonhui Min","email":"","orcid":"","institution":"Chungbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Geonhui","middleName":"","lastName":"Min","suffix":""},{"id":371906407,"identity":"128eaa85-ad27-42bc-829e-0287fe4aa1e1","order_by":7,"name":"In-Sook Jeon","email":"","orcid":"","institution":"Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"In-Sook","middleName":"","lastName":"Jeon","suffix":""},{"id":371906408,"identity":"c2822926-ded6-4c26-b25a-a7a3d0114408","order_by":8,"name":"Eung-Gook Kim","email":"","orcid":"","institution":"Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eung-Gook","middleName":"","lastName":"Kim","suffix":""},{"id":371906410,"identity":"1d17d4c8-25ca-4739-a890-8eb7bbc36618","order_by":9,"name":"Joong Kook Choi","email":"","orcid":"","institution":"Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Joong","middleName":"Kook","lastName":"Choi","suffix":""},{"id":371906412,"identity":"4382cd9c-9a6a-4caa-80a5-0742605fd41a","order_by":10,"name":"Hyun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJACZjjrA8laGGeQrIWZhxjl5uyn06QLau7IGxzvMXxsu8MusV/6AOOHjzm4tVj25G6TnnHsmeGGM2eMjXPPJCfO7Etglpy5DbcWgwNALTxshxk33Mgxk85tYzY2OMPAxsyLT8v5t0At/w7bb7j/xkzasq3e2J6glhtAW3jbDiduuMFjJs3YdljOgIeglrebrXn7DifPPJNWbNjbdlxO4gxjM36/nM/deJvn22HbvuOHNz742VbNw9/DfPDDRzxa4EDhAIcBlMnYQIR6IJBvYH9AnMpRMApGwSgYcQAAqTZSDbF2rjYAAAAASUVORK5CYII=","orcid":"","institution":"Hanyang University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hyun","middleName":"","lastName":"Lee","suffix":""},{"id":371906414,"identity":"760bc934-2691-4e54-aff0-54580c01a99a","order_by":11,"name":"Bumhee Yang","email":"","orcid":"","institution":"Chungbuk National University Hospital, Chungbuk National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bumhee","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-10-24 02:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5322072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5322072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70034486,"identity":"e6b25b7e-8c56-4b17-b162-5c2b850398e3","added_by":"auto","created_at":"2024-11-27 17:06:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128102,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental workflow used for the proteome analysis of the BAL samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BAL, bronchoalveolar lavage\u003c/p\u003e","description":"","filename":"figure1..png","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/af6ed9568216ea6758eb22ef.png"},{"id":70034489,"identity":"c5a379fc-aad0-4fea-b182-ef7954997985","added_by":"auto","created_at":"2024-11-27 17:06:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223191,"visible":true,"origin":"","legend":"\u003cp\u003eThe sequential steps of the statistical analysis performed.\u003c/p\u003e\n\u003cp\u003e(a) The Tandem Mass Tag intensity obtained from the chromatography-tandem mass spectrometry analyses was log\u003csub\u003e2\u003c/sub\u003e-transformed and normalized to the median of the column.\u003c/p\u003e\n\u003cp\u003e(b) The intensity versus frequency distributions for all eight samples are similar.\u003c/p\u003e\n\u003cp\u003e(c) The Pearson correlation coefficients among samples were over 0.9.\u003c/p\u003e\n\u003cp\u003e(d) The principal component analysis analysis could clearly discriminate the stable group from the AE group.\u003c/p\u003e\n\u003cp\u003e(e) Hierarchical clustering was performed for the 127 significant proteins between the two groups (p-value \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(f) and (g) show the upregulated and downregulated proteins in the AE group compared to stable group, respectively.\u003c/p\u003e","description":"","filename":"figure2..png","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/83c57eed2d8df89b30e0472c.png"},{"id":70034488,"identity":"d8e7e95d-2520-4b5f-a3d1-f4c9564991fc","added_by":"auto","created_at":"2024-11-27 17:06:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178634,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment analysis using the 127 significant proteins whose expression was altered between the AE and stable groups. The top 20 pathways with the most significant p-values (the -log\u003csub\u003e10\u003c/sub\u003e(p) values indicate that p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eAE, acute exacerbation\u003c/p\u003e","description":"","filename":"figure3..png","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/1a0de3b34f3d16906037884d.png"},{"id":70034487,"identity":"e1d5dd0d-71b1-4628-b44c-a9c6652a5a23","added_by":"auto","created_at":"2024-11-27 17:06:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157410,"visible":true,"origin":"","legend":"\u003cp\u003eTwenty-three proteins showing markedly differences between the AE status and stable status. Volcano plot was drawn to quantitatively compare the protein levels between the stable and AE groups. The dark blue and orange colors indicate the proteins whose levels were decreased and increased by more than two-fold, respectively (p-value \u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eAE, acute exacerbation\u003c/p\u003e","description":"","filename":"figure4..png","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/b7d8aa2deb2333644e0e0e8a.png"},{"id":70699103,"identity":"d79b5f8c-7fe6-4a76-a7cf-fe123ea73809","added_by":"auto","created_at":"2024-12-05 18:31:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2340747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/37d2cf7e-e058-4fc9-9b46-c6b520b49b8e.pdf"},{"id":70034491,"identity":"b5d04d77-44f0-43fd-9e87-cc46019ecbc2","added_by":"auto","created_at":"2024-11-27 17:06:57","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6084179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eList of 1,577 identified and quantified proteins from BAL proteome samples from bronchiectasis patients in stable and AE status\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eAE, acute exacerbation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. \u003c/strong\u003eList of 127 significantly differentially expressed BAL proteins between bronchiectasis patients in stable and AE status\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eAE, acute exacerbation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3. \u003c/strong\u003eList of 18 proteins whose difference values increased by more than two-fold among the proteins that passed t test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4. \u003c/strong\u003eList of proteins whose difference values decreased by more than two-fold among the proteins that passed t test\u003c/p\u003e","description":"","filename":"SupplementaryTables240805Final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5322072/v1/e8d6a88199d8081acaca1a76.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bronchoalveolar Lavage Proteomics in Acute Exacerbation of Bronchiectasis","fulltext":[{"header":"Background","content":"\u003cp\u003eBronchiectasis is a chronic respiratory disorder characterized by irreversible dilation and damage to the bronchial walls, leading to impaired mucus clearance and increased susceptibility to recurrent bronchial infections [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence and socioeconomic burden of bronchiectasis have increased worldwide [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The acute exacerbation (AE) of bronchiectasis, defined as a sustained worsening of the respiratory symptoms, is associated with poor treatment outcomes, and the prevention and/or appropriate management of AE is crucial for altering the natural course of this disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accordingly, understanding the mechanisms underlying AE development is imperative for the development of novel treatments to reduce the occurrence of AE in bronchiectasis.\u003c/p\u003e \u003cp\u003eProteomics is a powerful and rapidly evolving field of study that offers an innovative lens for examining the intricate molecular signatures and protein dynamics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unlike genomics, which provides information about the genetic blueprint of an individual, proteomics enables the direct analysis of protein expression, modifications, and interactions within a biological system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, proteomic investigations can yield invaluable insights into the functional alterations in cellular pathways, which are crucial to understanding the pathogenesis of bronchiectasis.\u003c/p\u003e \u003cp\u003ePrevious studies evaluating the role of proteomics in the development of AE in bronchiectasis were performed using sputum samples [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although these studies have revealed important novel findings, sputum samples have limitations in terms of contamination and these may not reflect true local inflammation (in the active parenchymal area of bronchiectasis) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, to overcome these limitations, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS), a high-throughput proteomic screening method, to bronchoalveolar lavage (BAL) fluid samples obtained from patients with bronchiectasis. Our study aimed to determine whether a high-throughput proteomic screening method could reveal differentially expressed proteins associated with the AE of bronchiectasis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis prospective study evaluated 8 patients with bronchiectasis between January 2021 and January 2022 at Chungbuk National University Hospital, a tertiary referral hospital in Cheongju, Republic of Korea. Bronchiectasis was diagnosed by two pulmonologists (BHY and SHK) based on respiratory symptoms and computed tomography findings, according to published guidelines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDuring the study period, 4 patients in a stable status and 4 patients in an AE status were enrolled in the study. No attempt to match for age, sex, or body mass index (BMI) was made, as prior studies on both chronic obstructive pulmonary disease (COPD) and bronchiectasis found no relationship between the sputum proteome and these parameters [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The AE of bronchiectasis is defined as the worsening of three or more major symptoms for 48 h or more, necessitating a change in treatment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Major symptoms include coughing, sputum volume/viscosity, sputum suppuration, dyspnea, exercise ability, fatigue, malaise, and hemoptysis. Moreover, we define a stable status as a condition in which a patient does not require treatment changes, including antibiotic or corticosteroid use, for 4 weeks or longer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The study protocol was approved by the Institutional Review Board of the Chungbuk National University Hospital (application no. 2021-10-007).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of this study was to identify differences in BAL proteomics and related pathways associated with the AE status of bronchiectasis.\u003c/p\u003e\n\u003ch3\u003eMeasurements of the clinical variables\u003c/h3\u003e\n\u003cp\u003eThe baseline demographic characteristics included the age, sex, BMI (weight in kilograms divided by the height in meters squared), and smoking status (never, former, or current smokers). Dyspnea was evaluated using the modified Medical Round Council scale [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The presence of patient-reported physician-diagnosed comorbidities was assessed at baseline. The white blood cell count, neutrophil count, and high-sensitivity C-reactive protein levels were measured in all patients.\u003c/p\u003e \u003cp\u003ePre- and post-bronchodilator spirometry were performed according to the recommendations of the American Thoracic Society/European Respiratory Society [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The absolute values of forced expiratory volume in 1 s (FEV\u003csub\u003e1\u003c/sub\u003e) and forced vital capacity (FVC) were recorded; additionally, the percentages of the predicted values for FEV\u003csub\u003e1\u003c/sub\u003e (FEV\u003csub\u003e1\u003c/sub\u003e%pred) and FVC (FVC %pred) were calculated using an automatic calculator by using a reference equation obtained from a representative Korean sample [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicrobiological analyses of specimens were performed using standard methods [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Conventional semi-quantitative bacterial and fungal cultures were also performed. All samples underwent initial Gram staining before culturing the BAL fluid if they met the Murray and Washington criteria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBronchoalveolar lavage\u003c/h3\u003e\n\u003cp\u003eBAL fluid and microbiological analyses were conducted for all patients. BAL procedures were performed by two pulmonologists (BHY and SHK) according to the American Thoracic Society recommendations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Bronchoscopy was performed using a standard flexible bronchoscope (BF-1TQ260; Olympus, Tokyo, Japan). During the BAL, the scope was placed in a wedge position within the selected segmental bronchi. A total volume of normal saline between 100 and 300 ml, divided into three to five aliquots, was instilled through the channel of the bronchoscope. After the instillation of each aliquot, the instilled saline was gently retrieved using a negative suction pressure of less than 100 mmHg to avoid visible airway collapse.\u003c/p\u003e\n\u003ch3\u003eComprehensive proteomic analysis of the bronchoalveolar lavage samples of patients with bronchiectasis\u003c/h3\u003e\n\u003cp\u003eProtein analysis of the BAL samples was conducted as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The detailed process is described in the next sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003eThe High-Select\u0026trade; Top14 abundant protein depletion resin (catalog number A36370), which was used for removing the 14 abundant proteins from the BAL samples, and the Tandem Mass Tag (TMT) Pro 16-plex isobaric label reagent set were purchased from Thermo Fisher Scientific (Rockford, USA). An Amicon Ultra centrifugal filter (3 kDa MWCO) and S-Trap mini spin columns were obtained from Merck Millipore (Billerica, USA) and ProFit (New York, USA), respectively. Sodium dodecyl sulfate, 1,4-dithiothreitho (DTT), iodoacetamide (IAA), ammonium bicarbonate (ABC), ammonium acetate, and triethylammonium bicarbonate (TEAB) buffers were purchased from Sigma-Aldrich. Trypsin Gold (mass spectrometry grade) from Promega (Madison, WI, USA) was used.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunoaffinity depletion\u003c/h3\u003e\n\u003cp\u003eFourteen abundant proteins in the BAL samples (albumin, IgA, IgD, IgE, IgG, IgG light chains, IgM, alpha-1-acid glycoprotein, alpha-1-antitrypsin, alpha-2-macroglobulin, apolipoprotein A1, fibrinogen, haptoglobin, and transferrin) were removed using the High-Select\u0026trade; Top14 resin following the manufacturer's protocol. Briefly, the depletion spin columns were equilibrated to room temperature, and 10 \u0026micro;l of samples were directly added to the resin slurry in the column. The samples were mixed by inverting the column several times until the resin was completely homogenous. The mixture in the column was incubated under gentle end-over-end mixing for 10 min at room temperature. After incubation, the contents of the mini column were added to a 1.5 ml collection tube and centrifuged at 1,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 2 min. The 14 most abundant proteins were removed from the collected samples. The samples were filtered using an Amicon Ultra centrifugal filter (3 kDa MWCO) for concentration and desalting.\u003c/p\u003e\n\u003ch3\u003eProteome sample preparation\u003c/h3\u003e\n\u003cp\u003eDepleted and desalted BAL samples were digested using S-Trap mini spin columns following the manufacturer's protocol. The proteins were denatured in sodium dodecyl sulfate and TEAB, reduced using TCEP, alkylated with IAA in the dark, and quenched with phosphoric acid. After adding the binding/wash buffer, the solution was centrifuged in the S-Trap column, washed, and digested with trypsin at 37\u0026deg;C for 16 h. Peptides were eluted using three different buffers and centrifuged. The resulting peptide solutions were pooled, dried, and quantified using a BCA assay. These peptides were then labeled with TMT-16 plex reagents, and the reaction was quenched using a hydroxylamine solution. The labeled samples were combined and dried for further analyses.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHigh pH reversed-phase liquid chromatography for peptide fractionation\u003c/h2\u003e \u003cp\u003eTMT-labeled samples were fractionated using an XBridge BEH Shield RP18 column on a Nexera XR high-performance liquid chromatography (HPLC) system, and 40 fractions were collected and concentrated into 10 fractions for LC-MS/MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLiquid chromatography-tandem mass spectrometry\u003c/h2\u003e \u003cp\u003eThese ten fractions were analyzed using an Easy nLC 1200 system and an Orbitrap Fusion Lumos mass spectrometer equipped with a nanoelectrospray source. The samples were initially trapped in a C18 precolumn and then separated using a C18 analytical column at a flow rate of 250 nl/min. The mobile phase consisted of water (A) and an acetonitrile-water-formic acid mixture (B). The LC gradient varied from 5\u0026ndash;95% for phase B over a specified time course. The mass spectrometer was operated in the data-dependent mode, alternating between MS and MS/MS scans. The MS spectra were collected at a resolution of 120,000, whereas the MS/MS spectra were collected at a resolution of 60,000 with specific settings for ion injection times, AGC target values, and collision energy. The system used a 30 s exclusion time for previously fragmented ions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing and data processing\u003c/h2\u003e \u003cp\u003ePrior to the LC-MS/MS analysis, sample preparation involved protein trapping to remove the non-essential proteins and contaminants from the BAL samples, thus concentrating the necessary proteins for analysis. This process successfully eliminated 14 abundant plasma proteins, including albumin and immunoglobulins, with only the relevant proteins remaining. Subsequently, protein digestion was performed using trypsin. These peptides were labeled with isobaric TMT agents for detailed quantitative analysis. HPLC was used to fractionate the labeled samples into 10 parts, simplifying their complexity, before performing LC-MS/MS. This method enables the accurate measurement, quantification, and identification of peptides, thereby providing essential data on diagnostic markers and proteolytic processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProtein identification and quantitation\u003c/h2\u003e \u003cp\u003eThe Integrated Proteomics Pipeline with built-in search engines (IP2, version 6.5.5, Integrated Proteomics) was used for data analysis using the UniProt human protein database (September 2021, reviewed). Reverse sequences of all proteins were appended to the database to calculate the false discovery rate. ProLuCID [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to identify the peptides with a precursor mass error of 5 ppm and a fragment ion mass error of 50 ppm. Trypsin was selected as the enzyme to use because of two potentially missed cleavages. The TMT modification (+\u0026thinsp;304.2071) of the N-terminus and lysine residue using the labeling reagent and carbamidomethylation of cysteine were chosen as static modifications. Methionine oxidation was chosen as the variable modification. The reporter ions were extracted from small windows (approximately 20 ppm) around their expected m/z values in the HCD spectrum. The output data files were filtered and sorted to compose the protein list using DTASelect (The Scripps Research Institute, USA), with two or more peptide assignments for protein identification and a false positive rate of less than 0.01 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eQuantitative analysis was conducted using Census in the IP2 pipeline (Integrated Proteomics, USA) with only unique peptides. The intensity of the reporter ion channel for a protein was calculated as the sum of the intensities of the reporter ions from all constituent peptides of the identified protein [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The reverse and potentially contaminating proteins were removed. The Perseus platform (version 2.0.5.0) was used for data processing. The intensity of the protein was input into Perseus, log\u003csub\u003e2\u003c/sub\u003e-transformed, and normalized to the median of the column for protein quantification. Student\u0026rsquo;s t-test was performed to select the significant proteins with a p-value between the two groups below 0.05 to identify the significant proteins. Moreover, pathway analysis of the significantly differentially expressed proteins was performed to determine the functional proteins whose expression differed between the two groups. The Perseus software was used to perform unsupervised hierarchical clustering and principal component analysis (PCA), and to generate volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology (GO) pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eThe proteins that remained altered following resuscitation in each tissue sample were subjected to GO pathway analysis using Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein names were converted into Entrez gene IDs and redundant identifiers were merged into a single Entrez gene ID. Pathway and process enrichment analyses utilized various ontology sources, including GO biological processes, KEGG pathway, reactome gene sets, CORUM, PanGenBase, WikiPathways, and the PANTHER pathway. Significant terms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; minimum count of three; and an enrichment factor\u0026thinsp;\u0026gt;\u0026thinsp;1.5) were clustered based on membership similarities and the most significant term within each cluster represented the respective cluster. P-values were calculated based on an accumulative hypergeometric distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis of the clinical variables\u003c/h2\u003e \u003cp\u003eData are presented as the mean and standard deviation (SD) for the continuous variables and as frequency (percentage) for the categorical variables. Continuous variables were compared using the Mann\u0026ndash;Whitney U test, and the Pearson chi-square test or Fisher\u0026rsquo;s exact test was used to compare the categorical variables, as appropriate. All tests were two-sided, and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. All statistical analyses were performed using the IBM SPSS Statistics for Windows (version 27.0; IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eThe baseline characteristics of the study population are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the 8 patients, 4 patients were in the AE group and eight were in the stable group. No significant differences in the baseline characteristics were found between the AE and stable groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAE status (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStable status (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.6 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.3 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent or ex-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious history of TB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurologic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTM-PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpirometry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.4 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1, L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1, % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.1 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.0 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1/FVC, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.3 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.3 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobiology\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emMRC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLab findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8265 (3781.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10433 (3,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6098 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil count, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.7 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.4 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEosinophil count, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehs-CRP, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eData are presented as the mean (standard deviation) or number (%).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BMI, body mass index; COPD, chronic obstructive pulmonary disease; TB, tuberculosis; FVC, forced vital capacity; FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in 1 second; NTM, nontuberculous mycobacteria; \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e; \u003cem\u003eH. influenzae\u003c/em\u003e, \u003cem\u003eHaemophilus influenzae\u003c/em\u003e; \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e; \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e; \u003cem\u003eA. baumannii\u003c/em\u003e, \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e; mMRC \u003cem\u003eModified Medical Research Council; WBC count, white blood cells count; hs-CRP,\u0026nbsp;\u003c/em\u003ehigh-sensitivity C-reactive protein\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cem\u003eKlebsiella pneumonia\u003c/em\u003e and \u003cem\u003eStaphylococcus aure\u003c/em\u003e\u003cem\u003eus\u003c/em\u003e were identified in one patient.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eProteome analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003eNormalization, correlation, PCA, and protein variation\u003c/h2\u003e\n \u003cp\u003eIn total, a total of 1,577 proteins were identified and quantified in stable and AE BAL samples using LC-MS/MS analysis, coupled with TMT labeling and HPLC fractionation into ten fractions (\u003cstrong\u003eSupplemental Table\u0026nbsp;1\u003c/strong\u003e). The intensity of the TMT reporter ions was extracted from the TMT labelled peptides with an accuracy of 25 ppm. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the sequential steps of the statistical analysis. The protein intensity summed by the TMT reporter ions from the peptide was normalized to the median of each sample after the log\u003csub\u003e2\u003c/sub\u003e transformation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The intensity versus frequency histograms for all samples showed a Gaussian distribution (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). The Pearson correlation values among all four stable and four AE samples were over 0.9 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\n \u003cp\u003ePCA was performed on all 127 significantly differentially expressed proteins after performing Student\u0026apos;s t-test between the two groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). It clearly discriminated two groups, with component 1 accounting for 72.9% of the variance and component 2 accounting for 9.3%. Hierarchical analysis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee) showed that the expression of these 127 significant proteins increased (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef) and decreased (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg) in the AE groups. Detailed information on the 127 proteins was summarized in the \u003cstrong\u003eSupplemental Table\u0026nbsp;2\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eOf 127 differentially expressed proteins, 23 proteins showed more than 2-fold differences between the AE and stable status groups. Compared to the stable group, 18 proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2) were significantly upregulated by more than 2-fold in the AE group (\u003cstrong\u003eSupplementary Table\u0026nbsp;3\u003c/strong\u003e). In contrast, 5 proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC) were significantly downregulated by more than 2-fold in the AE group compared to the stable group (\u003cstrong\u003eSupplementary Table\u0026nbsp;4\u003c/strong\u003e). The volcano plot shows representative proteins whose expression was quantitatively increased (SPRR3, ORM1, S100A9, TPI1, and BPI) and decreased (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC) in the AE group compared to the stable group (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; |log\u003csub\u003e2\u003c/sub\u003e value| \u0026ge; 0.5). Overall, these differences indicate that increased neutrophilic inflammation and altered mucin and mucosal immunity are associated with the AE status of bronchiectasis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional analysis\u003c/h2\u003e\n \u003cp\u003eThe 127 significant proteins obtained using the Student\u0026apos;s t-test were analyzed via gene ontology (GO) using Metascape (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Twenty clusters enriched in all the 127 significant proteins are listed (accumulative hypergeometric p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Neutrophil degranulation (R-HSA-6798695) was the most enriched pathway among those enriched in the 127 significant proteins, followed by C-type lection receptor (CLR) signaling (R-HSA-5621481).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a proteomic analysis of BAL fluid using liquid chromatography-tandem mass spectrometry. Among the 1,577 proteins identified, 127 showed statistically significant differences between the patients with AE and those with stable bronchiectasis; we identified 18 proteins whose expression increased by more than 2-fold and 5 proteins whose expression decreased by more than 2-fold in the AE group compared to the stable group. GO analysis identified 20 functional clusters enriched in the significant proteins, with neutrophil degranulation being the most enriched pathway, followed by CLR signaling.\u003c/p\u003e \u003cp\u003eWhile previous studies have employed sputum analysis for proteomics research in bronchiectasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], this study is the first to utilize BAL fluid samples to molecularly characterize the AE of bronchiectasis. Here, we found 127 proteins differentially expressed between the individuals in AE and stable status. Of these, 23 proteins showed marked differences demonstrating AE of bronchiectasis was associated with 18 upregulated proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2) and 5 downregulated proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC). Increased levels of CRP, S100A8, S100A9, BPI, and ORM1 suggest ongoing neutrophil inflammatory conditions, and are involved in the regulation of inflammatory processes and immune responses in chronic inflammatory lung disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The decreased levels of MUC5B and MUC5AC suggest that their crucial role in maintaining and protecting the mucus layer in the airways is interrupted in inflammatory lung disease, and the decreased IGHA1 levels indicate that the protective role of immunoglobulin A in mucosal immunity is impaired [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Overall, these results indicate that neutrophilic inflammation and an altered mucin and mucosal immunity are closely linked to the AE status of bronchiectasis, which is in line with a recent study that analyzed the sputum proteomics associated with the AE of bronchiectasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, the differences in these inflammatory proteins observed show that neutrophil degranulation (R-HSA-6798695) was the most enriched pathway, reinforcing its important role in exacerbating bronchiectasis. Neutrophilic inflammation is the main mechanism of chronic airway inflammation in bronchiectasis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, neutrophil degranulation, including the release of neutrophil elastase and myeloperoxidase, plays a significant role in the AE of bronchiectasis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Neutrophils release various inflammatory mediators, including proteases, reactive oxygen species, and cytokines. Although neutrophil elastase has a positive role in bacterial killing through phagocytosis, it also has negative roles, such as airway mucus obstruction, alveolar epithelial injury by degradation of the extracellular matrix proteins, activation of proinflammatory signaling, and compromise of innate immunity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] These can exacerbate the symptoms of patients and contribute to the cycle of infection and neutrophilic inflammation that characterizes the disease.\u003c/p\u003e \u003cp\u003eAdditionally, we observed that the CLR pathway is an important pathway related to the AE of bronchiectasis. The CLR pathway is involved in shaping immune responses to various types of infections (bacterial, viral, and fungal). CLRs can induce phagocytic, endocytic, anti-microbial, anti-inflammatory, and proinflammatory responses to protect against infection, and have several functions according to the signaling motifs in the cytoplasmic domains. Moreover, the key roles of CLRs in allergy, autoimmunity, and homeostasis maintenance have been studied[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A previous study showed that the expression of mannose binding lectin (MBL), a soluble CLR, is associated with the severity and AE of bronchiectasis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This finding supports our results, which suggest that C-type lectin receptors play an important role in bronchiectasis and its AE.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, the sample size is relatively small, and the study was performed at only one center in Korea. Therefore, further studies are required to validate these findings. Second, due to the small number of participants, we could not perform subgroup analyses according to the AE endotypes of bronchiectasis and bacterial colonization. Third, as our study was cross-sectional, we could not measure the BAL proteomic changes before and after AE of bronchiectasis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, different BAL protein expression levels were associated with the AE status of bronchiectasis. Neutrophil degranulation and CLR pathways are the major pathways involved in the AE of bronchiectasis. Future studies with larger cohorts should be performed to corroborate these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.Y.L., J.Y., H.Y.K., Y.D., M-S.K., H.L., and B.Y. conceived the study and performed the experiments. J.Y.L., J.Y., H.L., and B.Y. wrote the manuscript and designed the diagrams. H.L., and B.Y. edited the manuscript. D.E.K., G.M. I-S.J., E-G.K., J.K.C. and B.Y. \u0026nbsp;critically reviewed the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of\u0026nbsp;the\u0026nbsp;Chungbuk National University Hospital (application no. 2021-10-007). Individual consent for this retrospective analysis was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statemen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research Program funded by the National Research Council of Science and Technology (NST) (CRC22021-100). This work was supported by the Research Program funded by the National Research Council of Science and Technology (NST) (CRC22021-100). This work was supported by grants from the National Research Foundation (NRF) of Korea (No. 2020R1A5A2017476). Also, this work was supported by the NRF grant funded by the Korea government (MSIT) (No. 2022R1F1A1074749 to B.Y.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAliberti S, Goeminne PC, O\u0026apos;Donnell AE, Aksamit TR, Al-Jahdali H, Barker AF, Blasi F, Boersma WG, Crichton ML, De Soyza A, et al: \u003cstrong\u003eCriteria and definitions for the radiological and clinical diagnosis of bronchiectasis in adults for use in clinical trials: international consensus recommendations.\u003c/strong\u003e \u003cem\u003eLancet Respir Med \u003c/em\u003e2022, \u003cstrong\u003e10:\u003c/strong\u003e298-306.\u003c/li\u003e\n\u003cli\u003eAthanazio RA: \u003cstrong\u003eBronchiectasis: moving from an orphan disease to an unpleasant socioeconomic burden.\u003c/strong\u003e \u003cem\u003eERJ Open Res \u003c/em\u003e2021, \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHenkle E, Chan B, Curtis JR, Aksamit TR, Daley CL, Winthrop KL: \u003cstrong\u003eCharacteristics and Health-care Utilization History of Patients With Bronchiectasis in US Medicare Enrollees With Prescription Drug Plans, 2006 to 2014.\u003c/strong\u003e \u003cem\u003eChest \u003c/em\u003e2018, \u003cstrong\u003e154:\u003c/strong\u003e1311-1320.\u003c/li\u003e\n\u003cli\u003eChoi H, Yang B, Nam H, Kyoung DS, Sim YS, Park HY, Lee JS, Lee SW, Oh YM, Ra SW, et al: \u003cstrong\u003ePopulation-based prevalence of bronchiectasis and associated comorbidities in South Korea.\u003c/strong\u003e \u003cem\u003eEur Respir J \u003c/em\u003e2019, \u003cstrong\u003e54\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAbo-Leyah H, Chalmers JD: \u003cstrong\u003eManaging and preventing exacerbation of bronchiectasis.\u003c/strong\u003e \u003cem\u003eCurr Opin Infect Dis \u003c/em\u003e2020, \u003cstrong\u003e33:\u003c/strong\u003e189-196.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Donnell AE: \u003cstrong\u003eBronchiectasis - A Clinical Review.\u003c/strong\u003e \u003cem\u003eN Engl J Med \u003c/em\u003e2022, \u003cstrong\u003e387:\u003c/strong\u003e533-545.\u003c/li\u003e\n\u003cli\u003eAl-Amrani S, Al-Jabri Z, Al-Zaabi A, Alshekaili J, Al-Khabori M: \u003cstrong\u003eProteomics: Concepts and applications in human medicine.\u003c/strong\u003e \u003cem\u003eWorld J Biol Chem \u003c/em\u003e2021, \u003cstrong\u003e12:\u003c/strong\u003e57-69.\u003c/li\u003e\n\u003cli\u003eHolman JD, Dasari S, Tabb DL: \u003cstrong\u003eInformatics of protein and posttranslational modification detection via shotgun proteomics.\u003c/strong\u003e \u003cem\u003eMethods Mol Biol \u003c/em\u003e2013, \u003cstrong\u003e1002:\u003c/strong\u003e167-179.\u003c/li\u003e\n\u003cli\u003eGraves PR, Haystead TA: \u003cstrong\u003eMolecular biologist\u0026apos;s guide to proteomics.\u003c/strong\u003e \u003cem\u003eMicrobiol Mol Biol Rev \u003c/em\u003e2002, \u003cstrong\u003e66:\u003c/strong\u003e39-63; 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recognition and immunity.\u003c/strong\u003e \u003cem\u003eCell Microbiol \u003c/em\u003e2014, \u003cstrong\u003e16:\u003c/strong\u003e185-194.\u003c/li\u003e\n\u003cli\u003eChalmers JD, McHugh BJ, Doherty C, Smith MP, Govan JR, Kilpatrick DC, Hill AT: \u003cstrong\u003eMannose-binding lectin deficiency and disease severity in non-cystic fibrosis bronchiectasis: a prospective study.\u003c/strong\u003e \u003cem\u003eLancet Respir Med \u003c/em\u003e2013, \u003cstrong\u003e1:\u003c/strong\u003e224-232.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bronchiectasis, Bronchoalveolar lavage, Proteomics, Neutrophil degranulation.","lastPublishedDoi":"10.21203/rs.3.rs-5322072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5322072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe molecular pathophysiology underlying the development of bronchiectasis with acute exacerbation at the proteomic level has not been clarified using bronchoalveolar lavage fluid samples. This study aimed to evaluate the bronchoalveolar lavage fluid inflammatory profiles associated with acute exacerbation of bronchiectasis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed the bronchoalveolar lavage fluid specimens from 4 patients in the acute exacerbation status and 4 patients in a stable status using liquid chromatography-tandem mass spectrometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 1,577 proteins were identified using proteomic analysis, with 127 differentially expressed proteins. Of 127 differentially expressed proteins, 23 proteins showed more than 2-fold differences between the acute exacerbation and stable status groups. The acute exacerbation status was associated with 18 upregulated proteins (TPI1, CRP, BPI, ORM1, PTPRE, S100A9, BPY2, TPM4, ERVFC1-1, CYS1, CLEC3B, S100A8, PSAT1, NDUFA10, MDGA1, SPRR3, ALDOA, and PSMB2)and five downregulated proteins (MUC5B, HSPE1, KLK13, IGHA1, and MUC5AC). Pathway analysis revealed that the neutrophil degranulation pathway (R-HSA-6798695) was the most enriched pathway in these proteins, followed by the C-type lectin receptor pathway (R-HSA-5621481).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe bronchoalveolar lavage fluid protein expression in patients in the acute exacerbation status of bronchiectasis was significantly different from that in patients in the stable status, indicating that neutrophil degranulation and C-type lectin receptor pathways are the most enriched pathways during acute exacerbation.\u003c/p\u003e","manuscriptTitle":"Bronchoalveolar Lavage Proteomics in Acute Exacerbation of Bronchiectasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 17:06:52","doi":"10.21203/rs.3.rs-5322072/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5412118-bf19-459d-89f9-a0408be26ef3","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-09T14:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 17:06:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5322072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5322072","identity":"rs-5322072","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}