Diagnostic potential of bronchoalveolar lavage fluid exosomal microRNAs for pneumonia in the intensive care unit

Diagnostic potential of bronchoalveolar lavage fluid exosomal microRNAs for pneumonia in the intensive care unit | 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 Diagnostic potential of bronchoalveolar lavage fluid exosomal microRNAs for pneumonia in the intensive care unit Yinfang Sun, Ying Xian, Zhiqin Duan, Zhiping Wan, Jianwei Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1508650/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 Early and appropriate antibiotic treatment reduces morbidity and mortality of pneumonia. However, early prediction of pulmonary infection remains a challenge for clinicians. Current clinical needs require the development and use of rapid and effective diagnostic indicators to accelerate the identification of pneumonia and process of microbiological diagnosis. MicroRNAs (miRNAs) in exosomes (EXOs) have become attractive candidates for novel biomarkers to evaluate the presence and progress of many diseases. We assessed their performance as biomarkers of pneumonia. Patients were divided into pneumonia group (with pneumonia) and control group (without pneumonia). We identified and compared two upregulated miRNAs in EXOs derived from bronchoalveolar lavage fluid (BALF) between the pneumonia and control groups (miR-17-5p, p=0.009; miR-193a-5p, p=0.031). Interestingly, miR-17-5p and miR-193a-5p in BALF-cell-debris pellets, BALF-EXO-free supernatants, total plasma, and plasma-EXOs did not differ significantly between both groups. In vitro experiments revealed that miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs derived from macrophages stimulated by LPS. Receiver operator characteristic (ROC) curve analysis indicated that exosomal miR-17-5p (area under the curve, AUC: 0.753) and exosomal miR-193a-5p (AUC: 0.629) have acceptable diagnostic value. This study is one of the few studies on BALF-EXO-miRNAs in patients with pneumonia, providing potential diagnostic biomarkers and therapeutic targets for pneumonia. Pneumonia exosomes miR-17-5p miR-193a-5p diagnostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lower respiratory tract infections (LRTIs), causing 2.6 million deaths worldwide in 2019, remained the world’s most deadly group of communicable diseases. LRTIs ranked as the fourth leading cause of death. In low-income countries, LRTIs are the most common cause of death; meanwhile, pneumonia is the only infectious disease among the top 10 causes of death in high income economies [1, 2]. Pneumonia is a common complication in the intensive care unit (ICU). Sometimes, it attributes to the direct cause of death in other common diseases, such as chronic obstructive pulmonary disease (COPD), lung cancer, and Alzheimer’s disease [3, 4]. Generally, severe pneumonia requires ICU admission. The 30-day mortality rate of patients with moderate-to-severe community-acquired pneumonia (CAP) in the ICU has been reported to be as high as 23–47% [5]. Patients with pneumonia who received effective antibiotic therapy within four hours were less likely to die than those who received delayed treatment [6]. Therefore, strengthening early identification and assessing pulmonary infection are beneficial to early warning response, timely prevention and control of infection, and reduction of the injury extent caused by infection to the body. These measures would improve prognosis of pneumonia among patients in the ICU. Currently, features of an acute respiratory infection, such as fever, cough, sputum production, shortness of breath, and leukocytosis, play an alarming role in initial pneumonia diagnosis. Such features aid in determining the final diagnosis, with supplementary evidence of lung consolidation discovered in computed tomography (CT) or chest X-rays [7]. Although diagnosis seems straightforward, the reliable diagnosis of pneumonia remains a complex and time-consuming process. First, high-risk groups, such as the infants or elderly, who often show atypical symptoms are at an enhanced risk of acute pulmonary failure or sepsis as secondary complications, if diagnosis and treatment intervention are not timely [8]. Second, misdiagnosis is not surprising, because various common co-pathologies, especially COPD and cardiac failure, can cause infiltration, which may be mistaken for areas of consolidation in patients with acute shortness of breath [1]. Third, abnormal clinical manifestations and pulmonary imaging are observed later in the course of the infection. To make matters worse, known biomarkers, such as high-sensitivity C-reactive protein (hsCRP), procalcitonin (PCT), and erythrocyte sedimentation rate (ESR), lack sufficient specificity [9], and the situation for early diagnosis of pneumonia remains grim. Extracellular vesicles (EVs) are membrane-bound vesicles in all body fluids. Exosomes (EXOs), a subset of EVs, refer to lipid bilayer particles with a diameter of 50–150 nm [10, 11]. They, containing several nucleic acids, lipids, and proteins, are released by almost all types of cells into the cell microenvironment for long-distance exchange of information [12, 13]. Among the various active components which can reflect the biological status of parent cells, miRNAs are considered as essential to the function of EVs and have risen to prominence as a novel tool to assist in medical decision-making [14, 15]. Accumulating evidence suggests that miRNAs are selectively incorporated into EVs rather than randomly wrapped into them [16]. In addition, EV-associated miRNAs are pivotal regulators in the pathogenesis of non-infectious and infectious pulmonary disorders [17, 18]. Therefore, extracellular miRNAs in EXOs may be promising diagnostic biomarkers for pulmonary infection and prognostic indicators for progression. In this study, we aimed to analyze the role of bronchoalveolar lavage fluid EXOs (BALF-EXOs) and their key miRNAs, which may have potential to indicate the presence of pneumonia or even predict the progression of the disease. To the best of our knowledge, there are relatively few studies on the BALF-EXOs of patients with pneumonia in the ICU. 2. Materials And Methods 2.1. Patients and Samples This study enrolled 74 patients on mechanical ventilation who were admitted to the ICU of The Third Affiliated Hospital of Sun Yat-sen University between October 2020 and October 2021. These patients were divided into two groups: the pneumonia group (patients with pulmonary infection) and control group (patients without pulmonary infection). We investigated the demographic and baseline characteristics, radiography and laboratory findings, clinical presentation, treatment, and outcomes of 74 patients for clinical diagnosis. The diagnosis of pulmonary infection was made on the basis of a composite reference standard, which included all microbiological tests and clinical adjudication, referring to the diagnostic criteria of CAP [19] and hospital-acquired pneumonia (HAP) [20]. For this study, we excluded the most severely immunocompromised patients—those with acquired immune deficiency syndrome (AIDS), acute leukemias, lymphomas or other severe congenital immunodeficiency syndromes, those receiving chemotherapy especially with neutropenia, and those who recently received solid organ or bone marrow transplants [1]. This study was approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (approval no. [2020] 02-254-02). The pulmonary and subpulmonary segments of the selected patients were lavaged with saline using electronic fiber bronchoscopy, and 15 mL BALF was collected (to avoid oral contamination) in sterile sputum collection tubes. Besides, 10 ml of blood were collected from all individuals and transferred to EDTA-containing tubes to isolate plasma. 2.2. Cell culture and treatment Human monocytes, THP-1, and human lung adenocarcinoma cell line, A549 cells, were maintained in RPIM1640 and DMEM medium, respectively, containing 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin and streptomycin at 37°C, and 5% CO 2 . THP-1 cells were cultured in maintenance media and supplemented with 100 ng/mL phorbol 12-myristate 13-acetate (PMA) for 24 hours to differentiate them into macrophages (THP-1 derived macrophages [tMACs]). For the control group, normal cultured A549 cells or tMACs were used; for the lipopolysaccharide (LPS) group, conventionally cultured cells were treated with LPS (1 ug/mL) for 24 hours. 2.3. Exosomal purification and identification EXOs were purified by differential and ultracentrifugation. Briefly, the BALF, plasma, and cell culture supernatant were centrifuged at a low speed (300 g for 10 min at 4°C) (15 ml polypropylene tube, swinging bucket rotor, model A-4-44, 5804R Refrigerated Centrifuge, Eppendorf, Germany) to remove floating cells. The removed cell debris pellets were collected as liquid biopsy elements. The resulting supernatants were transferred into 1.5 ml polypropylene tubes (Eppendorf, Germany) with a micropipette, and then centrifuged at 12,000 g for 30 min at 4°C (Fixed angle rotor, angle of 45°, model #3331, D-37520 Refrigerated Centrifuge, Thermo Electron Corporation, USA) to dislodge bacteria and some larger extracellular vesicles, such as apoptotic bodies. The resulting supernatants were transferred into Quick-Seal Centrifuge tubes (Beckman Coulter, USA) and centrifuged at 100,000 g for 90 min at 4°C in an Optima L-100xp tabletop ultracentrifuge (Swinging bucket rotor, model SW40 Ti, Optima L-100xp, Beckman Coulter, USA). The resultant pellet (EXOs), which were resuspended with phosphate-buffered saline (PBS) and EXO-free supernatants, was stored at −80°C. Further, negative-staining transmission electron microscopy (TEM) was used to analyze the EXOs. The EXOs were loaded on a copper grid and negatively stained with 3% (w/v) aqueous phosphotungstic acid for 1 min. The grid was then examined using an FEI Tecnai G2 Sprit Twin TEM (FEI, USA). Thereafter, exosomal particles were analyzed using NTA (NanoSight NS300, Malvern Instruments, United Kingdom). The NanoSight NS300 instrument equipped with a sCMOS camera, 488 nm laser (Blue), NTA 3.3 Dev Build 3.3.301 software, and 749 frames were used. In addition, exosomal positive markers (CD63 and CD81) were detected using BD accuri C6 flow cytometer. 2.4. Candidate miRNA selection Using the combination of keywords and MeSH terms for “pneumonia” and “microRNA”, we searched PubMed for articles that describe associations between miRNAs and pneumonia. Each article was reviewed, and associated miRNAs (“miRNAs cluster 1”) were recorded. To focus on the miRNAs with a high likelihood of relevance, we considered only miRNAs that had been studied in a population sample to be potential candidates for investigation. For additional details, see supplementary information. 2.5. RNA extraction and quantitative reverse transcription PCR (qRT-PCR) Total RNA was harvested using TRIzol, according to the manufacturer’s instructions. The expression of target miRNA was determined using the SYBR Green Master Mix kit (Takara, Japan). U6 snRNA was used as an internal control, and the fold change was calculated using the 2 −ΔΔCT method. For additional details, see supplementary information. 2.6. Statistical analysis The continuous variables are expressed as mean ± SD values (normally distributed), or as medians [interquartile ranges] (non-normally distributed), and were analyzed using the Student’s t-test or Mann–Whitney U test, respectively. The categorical variables are presented as sample rates (constituent ratio); they were compared using the Chi-squared test or Fisher’s exact test.Multiple comparisons among three or more groups were analyzed using one-way ANOVA test or Kruskal–Wallis test (non-parametric). Receiver-operating characteristic (ROC) curves were plotted to investigate diagnostic value of selected miRNAs. The area under the curve (AUC) was calculated to evaluate the performance of these miRNAs in predicting pulmonary infections. 3. Results 3.1. Patient characteristics Among the 74 patients, 61 patients had pulmonary infections. Baseline characteristics were similar between the two groups (Table 1). No significant differences in gender, age, and most comorbidities were present between the groups. The incidence of craniocerebral trauma was significantly higher in the control group than in the pneumonia group. This difference was observed because patients in the control group were mainly those who required mechanical ventilation on account of other medical conditions, such as surgery, but without pulmonary infection. A trend for decreased hospital stay was shown in the control group (17.8 days), compared with the pneumonia group (25.2 days); however, the difference was not statistically significant. The inflammation indicator, PCT, was significantly higher in the pneumonia group than in the control group. However, the hsCRP did not differ between both groups. Table 1. Demographic and baseline characteristics of patients Control Group (n=13) Pneumonia Group (n=61) P Age, mean (range), years 50.38（22-81） 57.70（21-89） 0.107 Sex, male, n (%) 11(84.6) 49(80.3) 1.000 Any comorbidity, n (%) Hypertension 9(69.2) 26(42.6) 0.081 Diabetes 3(23.1) 14(23.0) 1.000 Malignancy 0(0.0) 5(8.2) 0.579 Chronic liver disease 2(15.4) 14(23) 0.818 Cardiovascular disease 3(23.1) 10(10.6) 0.862 Chronic obstructive pulmonary disease 0(0.0) 3(4.9) 1.000 Renal disease 1(7.7) 9(14.8) 0.819 Craniocerebral trauma 9(69.2) 18(29.5) 0.017 Hospital, mean (range), days 17.8(2-39) 25.2(1-86) 0.054 hsCRP, mean (range), (mg/L) 52.4(0.9-151.7) 76.5(1.7-328.7) 0.236 PCT, mean (range), (ng/mL) 1.1(0.1-4.1) 4.9(0.02-45.0) 0.004 Abbreviations: hsCRP, high-sensitivity C-reactive protein; PCT, procalcitonin 3.2. Characterization of EXOs derived from different samples EXOs were purified from BALF, plasma, and cell culture supernatant using differential centrifugation, as described in the Materials and Methods section. First, the obtained EXOs were characterized according to the minimal experimental requirements for EVs, as defined by the International Society for Extracellular Vesicles [21]. The images captured using transmission electron microscopy (TEM) showed EXOs with a characteristic cup-shaped morphology, as reported in literature [22] (Figure 1A). The size distribution profile of the EXOs was investigated by nanoparticle tracking analysis (NTA) (Figure 1B). Furthermore, the presence of exosomal markers, CD63 and CD81, in all vesicles were revealed by flow cytometry (Figure 1C). 3.3. Differentially expressed miRNAs specifically showed in BALF-EX0s Eighteen miRNAs (miR-542-3p, miR-16-5p, miR-20a-3p, miR-27a-5p, miR-92a-3p, miR-342-3p, miR-422a, miR-423-5p, miR-582-3p, miR-885-5p, miR-193b-5p, miR-432-5p, miR-493-3p, miR-452-5p, miR-200b-3p, miR-34a-3p, miR-17-5p, and miR-193a-5p), which were previously revealed to have dysregulated expression profiles in inflammatory diseases using RNA sequencing of population sample, were selected. Among them, 16 miRNAs between two groups showed no statistical difference (Supplementary Figure S1). MiR-17-5p and miR-193a-5p levels were remarkably upregulated in BALF-EXOs of the pneumonia group, compared with the control group (Figure 2A). Thereafter, the expression of these two miRNAs in BALF-cell-debris pellets (Figure 2B) and BALF-EXOs-free supernatants (Figure 2C) were assessed. However, no significant differences were observed between both groups. Notably, the expression of miR-17-5p in BALF-EXOs-free supernatants was too low to be detected. Additionally, the levels of these two miRNAs in total plasma, plasma-EXOs, and plasma-EXOs-free supernatants were measured. Surprisingly, in plasma-EXOs (Figure 2D), both miR-17-5p and miR-193a-5p showed no difference in level between both groups; the same trend was observed for total plasma (Figure 2E). Besides, the expression of miRNAs in plasma-EXOs-free supernatants was too low to detect. 3.4. Infectious stimuli increase the candidate miRNAs level in macrophage EXOs Due to the presence of several interfering factors in clinical samples, in vitro experiments were conducted to verify that the different expressions of candidate miRNAs were real. EVs in BALF are mainly derived from macrophages in response to LPS [9]. Congruously, due to the low expression levels, the candidate miRNAs were non-detectable in EXOs derived from human alveolar epithelial (A549) cells. Whereas miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs from THP-1-derived macrophages (tMAC-EXOs) of the LPS group, compared with the PBS group (Figure 3A). A same trend was not observed for tMAC-cell-debris pellets (Figure 3B) and tMAC-EXO-free supernatants (Figure 3C). 3.5. The candidate miRNA levels in macrophage-EXOs are dynamic in inflammatory response To explore the relationship between the expression of miR-17-5p and miR-193a-5p in LPS-induced tMAC-EXOs and inflammatory response, the levels of miRNAs in LPS-induced tMAC-EXOs were dynamically measured. In tMAC-EXOs (Figure 4A), the expression of miR-193a-5p was drastically increased in a time-dependent manner after LPS treatment and peaked at 12 hours; it gradually returned to normal levels. Concurrently, miR-17-5p showed the same trend. In tMAC-cell-debris pellets and tMAC-EXO-free supernatants, the levels of the candidate miRNAs were unaffected by LPS stimuli (Figure 4B-C). 3.6. Diagnostic Potential of BALF-Derived Exosomal miRNAs To investigate whether altered exosomal miRNAs have potential diagnostic value in pneumonia，we performed the ROC curve analysis of the upregulated miRNAs. Results showed that levels of PCT, miR-17-5p, and miR-193a-5p could serve as potential diagnostic markers to discriminate the pneumonia group from the control group; the AUCs were 0.685, 0.753, and 0.692, respectively (Figure 5). The diagnostic accuracy values of the indicators are shown in Table 2. miR-193a-5p had the best specificity (100%) but the lowest sensitivity (50.82%) as a single biomarker. Additionally, these two candidate miRNAs were included in the logistic regression analysis. As shown in Table 2, combining the biomarkers did not improve diagnostic utility. Table 2. Diagnostic accuracy of PCT, hsCRP, miR-17-5p, and miR-193a-5p as single and combined biomarkers for predicting the presence of pneumonia MicroRNA AUC 95% CI Specificity (%) Sensitivity (%) p-value miR-17-5p 0.753 0.639-0.846 84.62 59.02 0.0002 miR-193a-5p 0.692 0.574-0.794 100 50.82 0.0028 miR-17-5p+ miR-193a-5p 0.748 0.633-0.842 92.31 57.38 ＜0.0001 hsCRP 0.651 0.531-0.758 61.54 80.33 0.1420 PCT 0.685 0.566-0.788 69.23 62.30 0.0254 hsCRP, high-sensitivity C-reactive protein; PCT, procalcitonin; AUC, area under the curve; CI, confidence interval 3.7. MiRNA target prediction and pathway Analysis The roles of miR-17-5P and miR-193a-5p were investigated using bioinformatics.Multiple databases (TargetScan, miRDB, and Funrich) were combined to predict and screen their target genes. For miR-17-5p, 98 target genes were identified (Figure 6A), and the network is shown in Figure 6B. Thereafter, the 98 potential target genes were included in the DAVID database for KEGG_PATHWAY analysis. The results showed that the FoxO signaling pathway was the top-ranked (Figure 6C). Figure 6D-E illustrated the miR-193a-5p target genes. Particularly, protein processing in the endoplasmic reticulum signaling pathway was the enriched pathway for the target genes of two miRNA (Figure 6C, F). 4. Discussion BALF, including alveolar biochemical components and cells, is obtained by infusing physiological saline into alveoli by bronchoscopy, and then aspirating under negative pressure. It allows cells and solute of the lower respiratory tract to be harvested for cytological identification, microculture, and genetic diagnosis [23]. Information gained from BALF is regarded to be a complement of lung biopsy pathology. Therefore, BALF is called liquid biopsy because it accurately reflects changes in the lungs. Furthermore, compared with lung biopsy, BALF is less invasive, safer, and with few complications [24, 25]. EXOs, as an important component of BALF, have been found to be a new mediator of intercellular communication and well-grounded in various pulmonary diseases. In COPD, BALF EXOs have the ability of predicting the damage extent, and serve as an effective monitoring indicator for airway remodeling and airflow limitation [26-28]. In asthma, BALF EXOs can mediate antigen presentation to the adaptive immune system and promote the activation of mast cells, eosinophilia, and alveolar macrophages, to participate in the formation of reversible airway hyperresponsiveness, airway obstruction, and airway remodeling [29, 30]. Furthermore, BALF EXOs play a major role in the diagnosis and prognosis estimation of lung cancer [31-33], pulmonary tuberculosis [34, 35], and idiopathic pulmonary fibrosis [36, 37]. However, few studies have focused on BALF EXOs of pneumonia, which frequently causes acute lung injury (ALI) and its more critical form, acute respiratory distress syndrome (ARDS). In children with severe pneumonia, serum miR-483-3p and miR-29c can be used as biomarkers to provide a novel perspective for diagnosis [38, 39]. Lower levels of Mir-1323 were detected in blood samples of children with Mycoplasma pneumoniae -associated pneumonia (MPP) than in blood samples of healthy controls [40]. Hsa-miR-127-3p, hsa-miR-493-5p, and hsa-miR-409-3p, the top three upregulated miRNAs of whole blood microRNAs, provided a clear distinction between healthy individuals and children with adenovirus-infected pneumonia [41]. In pediatric pneumonia, miR-146b could attenuate inflammation injury by inhibiting the MyD88/NF-κB signaling pathway [42]. In severe community-acquired pneumonia (SCAP), miR-181b serves as a diagnostic and prognostic biomarker[43]. All these studies were based on free miRNAs in blood, which are easy to be degraded. Exosomal miRNAs are encapsulated in a bilayer lipid membrane, which protects them from degrading enzymes, such as ribonucleases, and confers an outstanding stability in body fluids. In fact, the stability is particularly important when developing novel biomarkers using body fluids [44, 45]. For exosomal miRNAs, serum exosomal miRNAs have been reported to have the ability to predict ARDS events in patients with SCAP [46] or be viewed as candidate diagnostic biomarkers in children with adenovirus-infected pneumonia [47]. Additionally, another study outlined the possibility of using miRNAs in serum EVs to differentiate patients with CAP from healthy volunteers [7]. Whereas circulating blood exosomal microRNAs are influenced by systemic pathophysiological states. Our study revealed that, in patients with pneumonia, miR-17-5p and miR-193a-5p were remarkably elevated in BALF-EXOs but not in plasma-EXOs, compared with the control group. This observation implies that plasma-EXO-miRNAs are not effective in identifying pulmonary infections in critically ill patients on mechanical ventilation with similar baseline characteristics. In contrast, BALF-EXOs derived from patients, which can specifically and factually respond to lung pathology, were rarely investigated. The existing studies have established the mouse model of pulmonary inflammation for obtaining BALF-EXOs and conducting in vitro experiments on respiratory-related cell lines [9]. BALF-EXOs are secreted by cells, such as epithelial cells, alveolar macrophages, endothelial cells, tumor cells, and stem cells, wherein macrophages and epithelial cells are the main sources [48]. EXOs and their carried miRNAs serve as mediators for the interaction between lung epithelial cells and alveolar macrophages to maintain lung homeostasis. The lung epithelial surfaces, which are directly in contact with the environment, encounter dynamic physical forces as the trachea and alveoli are compressed and stretched during ventilation [49]. Alveolar epithelial cell functions as the first line of defense against harmful injury and is vital in maintaining the integrality and function of lung [49, 50]. First, we measured the candidate miRNAs in A549 cell-EXOs. Quite interestingly, all three miRNAs could not be detected. Macrophages play a crucial role in innate immunity and host defense upon Infectious disease development [51]. In brief, they initiate inflammatory responses to pathogens [52]. Meanwhile, EVs in BALF collected from mice with intratracheal LPS instillation were mainly secreted from lung macrophages [9]. Consistently in our study, miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs from LPS-treated tMACs, compared with controls. The in vitro experiment revealed that the candidate miRNA levels in macrophage-EXOs is dynamic in inflammatory response; characteristically, they first rise and gradually recover. Therefore, the miRNAs in BALF-EXOs can serve as an early warning to prompt the preparation of microbial cultures, which may contribute to timely identification of pathogens. To the best of our knowledge, this study was the first to validate the discriminative ability of upregulated miRNAs in BALF-EXOs for identifying patients with pneumonia from patients without pulmonary infections using ROC curve analysis. Our results demonstrated that miR-17-5p levels may have an acceptable diagnostic value as a biomarker, with an AUC of 0.753, sensitivity of 59.02% sensitivity, and a fixed specificity of 84.62%. These findings highlight BALF-exosomal miRNA dysregulation in patients with pulmonary infections, and provide potential biomarkers for the diagnosis of pneumonia. Another insight of our report is that miR-193a-5p had a 100% specificity, which means that patients with miR-193a-5p levels in BALF-EXOs below the cutoff value is at low risk of lung infection. For these patients, the use of antibiotics can be appropriately reduced to avoid unnecessary antibiotic exposure. This benefit may have crucial impact on public health, especially for countries with excessive antibiotic consumption. MiRNAs are small non-coding RNAs that have the capability of regulating gene expression by promoting their target messenger RNA (mRNA) degradation or inhibiting the translation of target genes [53, 54]. Previously, miR-17-5p and miR-193a-5p were both identified as candidates for targeted therapy in many diseases, particularly in cancer therapy [55-60]. To further explore the molecular mechanisms of miR-17-5p and miR-193a-5p in infective lung disease, three databases (miRBD, Funrich, and TargetScan) were used to identify their respective potential targets. The target genes involved in the signaling pathways were evaluated using the KEGG database. The signaling pathway that drew our attention was protein processing in endoplasmic reticulum, which plays vital roles in the control of the progression of the cell cycle, differentiation, inflammation, aging, and immunity [61-63]. However, its role in the pathogenesis of lung inflammatory responses needs further experiments to verify. Our study had several limitations. First, all patients were recruited from a single center, and the sample size was relatively small. Second, exact mechanisms of how these miRNAs function in lung inflammation are still unclear. Future studies are needed to confirm the actual regulatory targets and biological functions of the discovered miRNAs to obtain practical experimental evidence of the mechanistic processes involved in pneumonia. In conclusion, our study indicated that, for patients on mechanical ventilation in the ICU, a further increase in miR-17-5p and miR-193a-5p levels in EXOs derived from BALF may suggest an increased risk of developing pneumonia. Moreover, miR-193a-5p may serve as an indicator to guide the strategy of antibiotic use. To achieve the successful use of these miRNAs as biomarkers, studies in larger patient cohorts will be required to confirm existing results. Declarations Ethics approval and consent to participate ： This study was approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (approval no. [2020] 02-254-02). Informed consent was obtained from all individual participants included in the study. Consent for publication ： Patients signed informed consent regarding publishing their data and photographs. Availability of data and materials ：Thedata and materials used to support the findings of this study are available from the corresponding author upon request. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Funding: This work was supported by the National Natural Science Foundation of China (No. 81902081), the Natural Science Foundation of Guangdong Province (No.2020A1515011573), Guangzhou Science and Technology Program (No. 202102100003), Guangdong Province Science and Technology Program (No. 2005A20901005), and Major Science and Technology Project of Guangdong Province (No. 2013B020224002). Author contributions: L.W., K.Z., and ZD.W. conceived and designed the experiments; Y.S., Y.X., Z.D., and ZP.W. performed and analyzed most of the experiments; Y.S., Y.X., J.L., Y.L., and X.B. contributed to the performance and analyses of experiments; L.W., Y.S., and K.Z. wrote the manuscript. Acknowledgements: Not Applicable References 1. Wunderink, R.G. and G. Waterer. 2017. Advances in the causes and management of community acquired pneumonia in adults . Bmj 358: j2471. 2. 2019.World Health Organization. The top 10 causes of death . https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death 3. Mitchell, S.L., J.M. Teno, D.K. Kiely, M.L. Shaffer, R.N. Jones, H.G. Prigerson, L. Volicer, J.L. Givens, and M.B. Hamel. 2009. The clinical course of advanced dementia . N Engl J Med 361(16): 1529-38. 4. Sharafkhaneh, A., A.M. Spiegelman, K. Main, S. Tavakoli-Tabasi, C. Lan, and D. Musher. 2017. Mortality in Patients Admitted for Concurrent COPD Exacerbation and Pneumonia . Copd 14(1): 23-29. 5. Chen, H., Y. Yin, H. Gao, Y. Guo, Z. Dong, X. Wang, Y. Zhang, S. Yang, Q. Peng, Y. Liu, and H. Wang. 2020. Clinical Utility of In-house Metagenomic Next-generation Sequencing for the Diagnosis of Lower Respiratory Tract Infections and Analysis of the Host Immune Response . Clin Infect Dis 71(Suppl 4): S416-s426. 6. Sligl, W.I., H. Hoang, D.T. Eurich, A. Malhotra, T.J. Marrie, and S.R. Majumdar. 2013. Macrolide use in the treatment of critically ill patients with pneumonia: Incidence, correlates, timing and outcomes . Can J Infect Dis Med Microbiol 24(4): e107-12. 7. Hermann, S., F. Brandes, B. Kirchner, D. Buschmann, M. Borrmann, M. Klein, S. Kotschote, M. Bonin, M. Reithmair, I. Kaufmann, G. Schelling, and M.W. Pfaffl. 2020. Diagnostic potential of circulating cell-free microRNAs for community-acquired pneumonia and pneumonia-related sepsis. J Cell Mol Med 24(20): 12054-12064. 8. Musher, D.M. and A.R. Thorner. 2014. Community-acquired pneumonia . N Engl J Med 371(17): 1619-28. 9. Zhang, D., H. Lee, X. Wang, M. Groot, L. Sharma, C.S. Dela Cruz, and Y. Jin. 2019. A potential role of microvesicle-containing miR-223/142 in lung inflammation . Thorax 74(9): 865-874. 10. Kalluri, R. and V.S. LeBleu. 2020. The biology, function, and biomedical applications of exosomes . Science 367(6478): eaau6977 11. Arabpour, M., A. Saghazadeh, and N. Rezaei. 2021. Anti-inflammatory and M2 macrophage polarization-promoting effect of mesenchymal stem cell-derived exosomes. Int Immunopharmacol 97: 107823. 12. Lőrincz Á, M., B. Bartos, D. Szombath, V. Szeifert, C.I. Timár, L. Turiák, L. Drahos, Á. Kittel, D.S. Veres, F. Kolonics, A. Mócsai, and E. Ligeti. 2020. Role of Mac-1 integrin in generation of extracellular vesicles with antibacterial capacity from neutrophilic granulocytes . J Extracell Vesicles 9(1): 1698889. 13. Gao, L., L. Wang, T. Dai, K. Jin, Z. Zhang, S. Wang, F. Xie, P. Fang, B. Yang, H. Huang, H. van Dam, F. Zhou, and L. Zhang. 2018. Tumor-derived exosomes antagonize innate antiviral immunity . Nat Immunol 19(3): 233-245. 14. Wang, L., Y. Liao, R. Yang, Z. Yu, L. Zhang, Z. Zhu, X. Wu, J. Shen, J. Liu, L. Xu, Z. Wu, and X. Sun. 2020. Sja-miR-71a in Schistosome egg-derived extracellular vesicles suppresses liver fibrosis caused by schistosomiasis via targeting semaphorin 4D . J Extracell Vesicles 9(1): 1785738. 15. de Gonzalo-Calvo, D., A. Vea, C. Bär, J. Fiedler, L.S. Couch, C. Brotons, V. Llorente-Cortes, and T. Thum. 2019. Circulating non-coding RNAs in biomarker-guided cardiovascular therapy: a novel tool for personalized medicine? Eur Heart J 40(20): 1643-1650. 16. Zhang, J., S. Li, L. Li, M. Li, C. Guo, J. Yao, and S. Mi. 2015. Exosome and exosomal microRNA: trafficking, sorting, and function. Genomics Proteomics Bioinformatics 13(1): 17-24. 17. Lee, H., E. Abston, D. Zhang, A. Rai, and Y. Jin. 2018. Extracellular Vesicle: An Emerging Mediator of Intercellular Crosstalk in Lung Inflammation and Injury. Front Immunol 9: 924. 18. Jung, A.L., M. Møller Jørgensen, R. Bæk, K. Griss, M. Han, K. Auf Dem Brinke, N. Timmesfeld, W. Bertrams, T. Greulich, R. Koczulla, S. Hippenstiel, N. Suttorp, and B. Schmeck. 2020. Surface Proteome of Plasma Extracellular Vesicles as Biomarkers for Pneumonia and Acute Exacerbation of Chronic Obstructive Pulmonary Disease . J Infect Dis 221(2): 325-335. 19. Metlay, J.P., G.W. Waterer, A.C. Long, A. Anzueto, J. Brozek, K. Crothers, L.A. Cooley, N.C. Dean, M.J. Fine, S.A. Flanders, M.R. Griffin, M.L. Metersky, D.M. Musher, M.I. Restrepo, and C.G. Whitney. 2019. Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med . 200(7): e45-e67. 20. Kalil, A.C., M.L. Metersky, M. Klompas, J. Muscedere, D.A. Sweeney, L.B. Palmer, L.M. Napolitano, N.P. O'Grady, J.G. Bartlett, J. Carratalà, A.A. El Solh, S. Ewig, P.D. Fey, T.M. File, Jr., M.I. Restrepo, J.A. Roberts, G.W. Waterer, P. Cruse, S.L. Knight, and J.L. Brozek. 2016. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis 63(5): e61-e111. 21. Lötvall, J., A.F. Hill, F. Hochberg, E.I. Buzás, D. Di Vizio, C. Gardiner, Y.S. Gho, I.V. Kurochkin, S. Mathivanan, P. Quesenberry, S. Sahoo, H. Tahara, M.H. Wauben, K.W. Witwer, and C. Théry. 2014. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J Extracell Vesicles 3: 26913. 22. Pascucci, L. and G. Scattini. 2021. Imaging extracelluar vesicles by transmission electron microscopy: Coping with technical hurdles and morphological interpretation . Biochim Biophys Acta Gen Subj 1865(4): 129648. 23. Liu, Z., J. Yan, L. Tong, S. Liu, and Y. Zhang. 2021.The role of exosomes from BALF in lung disease. J Cell Physiol 237(1):161-168. 24. Costabel, U. and J. Guzman. 2001. Bronchoalveolar lavage in interstitial lung disease . Curr Opin Pulm Med 7(5): 255-61. 25. Roth, K., J.A. Hardie, A.H. Andreassen, F. Leh, and T.M. Eagan. 2008. Predictors of diagnostic yield in bronchoscopy: a retrospective cohort study comparing different combinations of sampling techniques . BMC Pulm Med 8:2. 26. Takahashi, T. and H. Kubo. 2014. The role of microparticles in chronic obstructive pulmonary disease . Int J Chron Obstruct Pulmon Dis 9: 303-14. 27. Armstrong, D.A., A.B. Nymon, C.S. Ringelberg, C. Lesseur, H.F. Hazlett, L. Howard, C.J. Marsit, and A. Ashare. 2017. Pulmonary microRNA profiling: implications in upper lobe predominant lung disease . Clin Epigenetics 9: 56. 28. Genschmer, K.R., D.W. Russell, C. Lal, T. Szul, P.E. Bratcher, B.D. Noerager, M. Abdul Roda, X. Xu, G. Rezonzew, L. Viera, B.S. Dobosh, C. Margaroli, T.H. Abdalla, R.W. King, C.M. McNicholas, J.M. Wells, M.T. Dransfield, R. Tirouvanziam, A. Gaggar, and J.E. Blalock. 2019. Activated PMN Exosomes: Pathogenic Entities Causing Matrix Destruction and Disease in the Lung. Cell 176(1-2): 113-126.e15. 29. Fujita, Y., Y. Yoshioka, S. Ito, J. Araya, K. Kuwano, and T. Ochiya. 2014. Intercellular communication by extracellular vesicles and their microRNAs in asthma . Clin Ther 36(6): 873-81. 30. Hackett, T.L.. 2012. Epithelial-mesenchymal transition in the pathophysiology of airway remodelling in asthma. Curr Opin Allergy Clin Immunol 12(1): 53-9. 31. Thakur, B.K., H. Zhang, A. Becker, I. Matei, Y. Huang, B. Costa-Silva, Y. Zheng, A. Hoshino, H. Brazier, J. Xiang, C. Williams, R. Rodriguez-Barrueco, J.M. Silva, W. Zhang, S. Hearn, O. Elemento, N. Paknejad, K. Manova-Todorova, K. Welte, J. Bromberg, H. Peinado, and D. Lyden. 2014. Double-stranded DNA in exosomes: a novel biomarker in cancer detection. Cell Res 24(6): 766-9. 32. Kim, J.E., J.S. Eom, W.Y. Kim, E.J. Jo, J. Mok, K. Lee, K.U. Kim, H.K. Park, M.K. Lee, and M.H. Kim. 2018.Diagnostic value of microRNAs derived from exosomes in bronchoalveolar lavage fluid of early-stage lung adenocarcinoma: A pilot study . Thorac Cancer 9(8): 911-915. 33. Zhang, X., B. Sai, F. Wang, L. Wang, Y. Wang, L. Zheng, G. Li, J. Tang, and J. Xiang. 2019. Hypoxic BMSC-derived exosomal miRNAs promote metastasis of lung cancer cells via STAT3-induced EMT. Mol Cancer 18(1): 40. 34. Kruh-Garcia, N.A., L.M. Wolfe, L.H. Chaisson, W.O. Worodria, P. Nahid, J.S. Schorey, J.L. Davis, and K.M. Dobos. 2014. Detection of Mycobacterium tuberculosis peptides in the exosomes of patients with active and latent M. tuberculosis infection using MRM-MS . PLoS One 9(7): e103811. 35. Sinha, S., K. Gupta, D. Mandal, B.K. Das, and R.M. Pandey. 2018. Serum and Bronchoalveolar Lavage Fluid 25(OH)Vitamin D3 Levels in HIV-1 and Tuberculosis: A Cross-Sectional Study from a Tertiary Care Center in North India . Curr HIV Res . 16(2): 167-173. 36. Liu, B., T. Jiang, X. Hu, Z. Liu, L. Zhao, H. Liu, Z. Liu, and L. Ma. 2018. Downregulation of microRNA‑30a in bronchoalveolar lavage fluid from idiopathic pulmonary fibrosis patients. Mol Med Rep 18(6): 5799-5806. 37. Rollet-Cohen, V., M. Bourderioux, J. Lipecka, C. Chhuon, V.A. Jung, M. Mesbahi, T. Nguyen-Khoa, S. Guérin-Pfyffer, A. Schmitt, A. Edelman, I. Sermet-Gaudelus, and I.C. Guerrera. 2018. Comparative proteomics of respiratory exosomes in cystic fibrosis, primary ciliary dyskinesia and asthma . J Proteomics 185: 1-7. 38. Zhou, J., J. Lin, Y. Zhao, and X. Sun. 2021. Deregulated Expression of miR-483-3p Serves as a Diagnostic Biomarker in Severe Pneumonia Children with Respiratory Failure and Its Predictive Value for the Clinical Outcome of Patients . Mol Biotechnol 64(3):311-319. 39. Guo, J., N. Zhang, G. Liu, A. Zhang, X. Liu, and J. Zheng. 2021. Upregulated expression of long non-coding RNA MEG3 serves as a prognostic biomarker in severe pneumonia children and its regulatory mechanism . Bioengineered 12(1): 7120-7131. 40. Yin, L., Y. Ma, W. Wang, and Y. Zhu. 2021. The critical function of miR-1323/Il6 axis in children with Mycoplasma pneumoniae pneumonia. J Pediatr (Rio J) 97(5): 552-558. 41. Huang, F., J. Zhang, D. Yang, Y. Zhang, J. Huang, Y. Yuan, X. Li, and G. Lu. 2018. MicroRNA Expression Profile of Whole Blood Is Altered in Adenovirus-Infected Pneumonia Children. Mediators Inflamm 2018: 2320640. 42. Zhang, L., L. Dong, Y. Tang, M. Li, and M. Zhang. 2020. MiR-146b protects against the inflammation injury in pediatric pneumonia through MyD88/NF-κB signaling pathway . Infect Dis (Lond) 52(1): 23-32. 43. Li, Q., T. Wu, and S. Li. 2021. MiR-181b serves as diagnosis and prognosis biomarker in severe community-acquired pneumonia. Genet Mol Biol 44(3): e20200431. 44. Valadi, H., K. Ekström, A. Bossios, M. Sjöstrand, J.J. Lee, and J.O. Lötvall. 2007. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells . Nat Cell Biol 9(6): 654-9. 45. Li, Y., Q. Zheng, C. Bao, S. Li, W. Guo, J. Zhao, D. Chen, J. Gu, X. He, and S. Huang. 2015. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res 25(8): 981-4. 46. Wu, X., C. Wu, W. Gu, H. Ji, and L. Zhu. 2019. Serum Exosomal MicroRNAs Predict Acute Respiratory Distress Syndrome Events in Patients with Severe Community-Acquired Pneumonia . Biomed Res Int 2019: 3612020. 47. Huang, F., J. Bai, J. Zhang, D. Yang, H. Fan, L. Huang, T. Shi, and G. Lu. 2019. Identification of potential diagnostic biomarkers for pneumonia caused by adenovirus infection in children by screening serum exosomal microRNAs . Mol Med Rep 19(5): 4306-4314. 48. Alipoor, S.D., E. Mortaz, J. Garssen, M. Movassaghi, M. Mirsaeidi, and I.M. Adcock. 2016. Exosomes and Exosomal miRNA in Respiratory Diseases . Mediators Inflamm 2016: 5628404. 49. Whitsett, J.A. and T. Alenghat. 2015. Respiratory epithelial cells orchestrate pulmonary innate immunity. Nat Immunol 16(1): 27-35. 50. Lee, H., D. Zhang, J. Wu, L.E. Otterbein, and Y. Jin. 2017. Lung Epithelial Cell-Derived Microvesicles Regulate Macrophage Migration via MicroRNA-17/221-Induced Integrin β(1) Recycling. J Immunol 199(4): 1453-1464. 51. Zhu, Z., D. Zhang, H. Lee, A.A. Menon, J. Wu, K. Hu, and Y. Jin. 2017. Macrophage-derived apoptotic bodies promote the proliferation of the recipient cells via shuttling microRNA-221/222. J Leukoc Biol 101(6): 1349-1359. 52. Bissonnette, E.Y., J.F. Lauzon-Joset, J.S. Debley, and S.F. Ziegler. 2020. Cross-Talk Between Alveolar Macrophages and Lung Epithelial Cells is Essential to Maintain Lung Homeostasis. Front Immunol 11: 583042. 53. He, L. and G.J. Hannon. 2004. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5(7): 522-31. 54. Guiot, J., M. Cambier, A. Boeckx, M. Henket, O. Nivelles, F. Gester, E. Louis, M. Malaise, F. Dequiedt, R. Louis, I. Struman, and M.S. Njock. 2020. Macrophage-derived exosomes attenuate fibrosis in airway epithelial cells through delivery of antifibrotic miR-142-3p. Thorax 75(10): 870-881. 55. Xu, H., Y. Liu, P. Cheng, C. Wang, Y. Liu, W. Zhou, Y. Xu, and G. Ji. 2020. CircRNA_0000392 promotes colorectal cancer progression through the miR-193a-5p/PIK3R3/AKT axis. J Exp Clin Cancer Res 39(1): 283. 56. Wang, J.T. and Z.H. Wang. 2018. Role of miR-193a-5p in the proliferation and apoptosis of hepatocellular carcinoma. Eur Rev Med Pharmacol Sci 22(21): 7233-7239. 57. Azar, M., H. Aghazadeh, H.N. Mohammed, M.R.S. Sara, A. Hosseini, N. Shomali, R. Tamjidifar, S. Tarzi, M. Mansouri, S.P. Sarand, F. Marofi, M. Akbari, H. Xu, and S.S. Shotorbani. 2021. miR-193a-5p as a promising therapeutic candidate in colorectal cancer by reducing 5-FU and Oxaliplatin chemoresistance by targeting CXCR4. Int Immunopharmacol 92: 107355. 58. Duan, B., S. Shi, H. Yue, B. You, Y. Shan, Z. Zhu, L. Bao, and Y. You. 2019. Exosomal miR-17-5p promotes angiogenesis in nasopharyngeal carcinoma via targeting BAMBI. J Cancer 10(26): 6681-6692. 59. Song, J., Y. Liu, T. Wang, B. Li, and S. Zhang. 2020. MiR-17-5p promotes cellular proliferation and invasiveness by targeting RUNX3 in gastric cancer. Biomed Pharmacother 128: 110246. 60. Zhang, Y., S. Wang, Q. Lai, Y. Fang, C. Wu, Y. Liu, Q. Li, X. Wang, C. Gu, J. Chen, J. Cai, A. Li, and S. Liu. 2020. Cancer-associated fibroblasts-derived exosomal miR-17-5p promotes colorectal cancer aggressive phenotype by initiating a RUNX3/MYC/TGF-β1 positive feedback loop. Cancer Lett 491: 22-35. 61. Moon, H.W., H.G. Han, and Y.J. Jeon. 2018. Protein Quality Control in the Endoplasmic Reticulum and Cancer . Int J Mol Sci 19(10): 3020 62. López-Otín, C., M.A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. 2013. The hallmarks of aging. Cell 153(6): 1194-217. 63. Wang, M. and R.J. Kaufman. 2016. Protein misfolding in the endoplasmic reticulum as a conduit to human disease. Nature 529(7586): 326-35. 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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-1508650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":96934428,"identity":"19558f6a-1000-4635-be80-14ebb5c7d431","order_by":0,"name":"Yinfang Sun","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yinfang","middleName":"","lastName":"Sun","suffix":""},{"id":96934429,"identity":"de22ac38-f4b5-4c3f-a0df-340f5c299740","order_by":1,"name":"Ying Xian","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xian","suffix":""},{"id":96934430,"identity":"a26eed4f-b37f-4fc2-9347-3e5cd17ae7a5","order_by":2,"name":"Zhiqin Duan","email":"","orcid":"","institution":"Guangzhou Medical University Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiqin","middleName":"","lastName":"Duan","suffix":""},{"id":96934431,"identity":"ce8ce6a8-f345-4b41-b336-7c0b5bd9d363","order_by":3,"name":"Zhiping Wan","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiping","middleName":"","lastName":"Wan","suffix":""},{"id":96934432,"identity":"bce55593-9fc9-4b62-9037-a0c74ed83914","order_by":4,"name":"Jianwei Li","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Li","suffix":""},{"id":96934433,"identity":"c53f22a0-3da0-4ba8-86f9-42b60663a5fa","order_by":5,"name":"Yao Liao","email":"","orcid":"","institution":"Sun Yat-sen University Zhongshan School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Liao","suffix":""},{"id":96934434,"identity":"b84787b3-aa00-4cce-8271-62a45f58b0e9","order_by":6,"name":"Xiaogang Bi","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaogang","middleName":"","lastName":"Bi","suffix":""},{"id":96934435,"identity":"5f406622-40a8-41d0-b986-1392fd6ea364","order_by":7,"name":"Lifu Wang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lifu","middleName":"","lastName":"Wang","suffix":""},{"id":96934436,"identity":"16bdb2b8-e7b4-437c-9000-307b1e8acf86","order_by":8,"name":"Zhongdao Wu","email":"","orcid":"","institution":"Sun Yat-sen University Zhongshan School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhongdao","middleName":"","lastName":"Wu","suffix":""},{"id":96934437,"identity":"5599587f-a6cf-43cd-9c83-b52e1ef01c21","order_by":9,"name":"Kouxing Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCTYQacPAwMzcABMzIEZLGlALI2laDgMxsVrkZ7clPuZtOx/N387YwPizrS6xgb15mwRDzR2cWhjnHDtszHPmdu6Mw4wNzLxthxMbeI6VSTAce4ZTC7NEeps0T8Xt3AaQFsa2A4kNEjlmEowNh3FqYQNrMTiXO/8wzGHyb/Br4ZFIOwa05UDuBqAWBt42ZqAtPPi1SEikJRvOOZOcuxGo5TDPucPGbTxpxRYJx3BrkZ+RZvjgbZtd7rzzhw8+/FFWJ9vPfnjjjQ81uLWggAOMwEgCx1MCcRpA4A/xSkfBKBgFo2DkAACHTFJhttg9IwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1174-174X","institution":"Department of general intensive care unit Third Affiliated Hospital of Sun Yat-Sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kouxing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-03-31 10:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1508650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1508650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20205231,"identity":"dabdf34a-5d16-4b29-84eb-89de21721e36","added_by":"auto","created_at":"2022-04-11 17:03:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205232,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of EXOs derived from different samples. (A) TEM images of EXOs isolated from BALF, plasma, and cell culture supernatant. Scale bars, 200 nm. (B) Different samples’ EV particles were investigated by NTA. (C) Exosomal positive markers (CD63 and CD81) were detected by flow cytometry analysis.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/2784074c9662376273500e56.png"},{"id":20205638,"identity":"0a8078c2-ad2e-4379-be84-1cef0aeadd7f","added_by":"auto","created_at":"2022-04-11 17:13:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122967,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed miRNAs specifically shown in BALF-EXOs. Comparison of screened miRNAs levels in BALF-EXOs(A), BALF-cell-debris pellets (B), BALF-EXOs-free supernatants (C), plasma-EXOs(D), and total plasma (E) between different groups. Data presented as a relative fold change for each miRNA. Box plots are displayed, where the horizontal bar represents the median, the box represents the IQR, and the whiskers represent the maximum and minimum values. Comparisons were made using the Mann–Whitney U test.\u003cem\u003e miRNA, \u003c/em\u003emicroRNA; \u003cem\u003eIQR,\u003c/em\u003e interquartile range\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/2e24606dff455b86c8c9420c.png"},{"id":20205235,"identity":"f221f594-7b63-4844-a780-5f9099317db7","added_by":"auto","created_at":"2022-04-11 17:03:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171256,"visible":true,"origin":"","legend":"\u003cp\u003eInfectious stimuli increase the candidate miRNA levels in macrophage-EXOs. Comparison of screened miRNAs levels in tMAC-EXOs (A), tMAC-cell-debris pellets (B), and tMAC-EXO-free supernatants (C) between two groups. Data presented as a relative fold change for each miRNA. Box plots are displayed, where the horizontal bar represents the median, the box represents the IQR, and the whiskers represent the maximum and minimum values. Comparisons made using the unpaired t test.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/8289f0aba92824feb5959b3c.png"},{"id":20205232,"identity":"a9fb437c-9504-417e-8a5f-28888fffeb0a","added_by":"auto","created_at":"2022-04-11 17:03:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217718,"visible":true,"origin":"","legend":"\u003cp\u003eThe candidate miRNA levels in macrophage-EXOs are dynamic in inflammatory response. tMACs cells were treated with PBS (control) or LPS (1ug/mL) for 6, 12, 24, 48, and 72 hours. The time and concentration curves of miR-17-5p and miR-193a-5p in tMACs-EXOs(A), tMAC-cell-debris pellets(B), and tMAC-EXO-free supernatants (C) were plotted according to the results from the qRT-PCR. Data were expressed as mean ± SD.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/9c581bc9bf2a8f4c5ae6e3ba.png"},{"id":20205580,"identity":"181d278a-1d6d-4e93-a221-0f3637f7df7e","added_by":"auto","created_at":"2022-04-11 17:08:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129564,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for comparing the ability of exosomal miR-17-5p and miR-193a-5p to discriminate the pneumonia group from the control group. Area under the curves (AUCs) and 95% confidence interval (CI) of AUC are indicated.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/1a83b4f20b6d73fd0ebcc740.png"},{"id":20205236,"identity":"c5139040-9f9c-4b44-b641-b4b1060190f4","added_by":"auto","created_at":"2022-04-11 17:03:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":217446,"visible":true,"origin":"","legend":"\u003cp\u003eMiRNA target prediction and pathway analysis. TargetScan, Funrich, and miRDB were used to predict downstream target genes of miR-17-5p (A) and miR-193a-5p (D). Construction of miR-17-5p (B) and miR-193a-5p(E) centered target gene regulatory network. KEGG analysis of the enrichment pathway of miR-17-5p target genes(C) and miR-193a-5p target genes (F).\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/02499eecd9c5f22cf9bae97b.png"},{"id":21871133,"identity":"b589d5a6-1ef9-4475-816b-6fdd17ee2482","added_by":"auto","created_at":"2022-05-25 14:46:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1258956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/118c872f-de81-4a11-849e-820da88c9459.pdf"},{"id":20205237,"identity":"7957d74b-db3a-44ba-8e4e-e6d8328fa553","added_by":"auto","created_at":"2022-04-11 17:03:37","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":141449,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1508650/v1/6eec206e14a3b341d7a40a97.docx"}],"financialInterests":"","formattedTitle":"Diagnostic potential of bronchoalveolar lavage fluid exosomal microRNAs for pneumonia in the intensive care unit","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLower respiratory tract infections (LRTIs), causing 2.6 million deaths worldwide in 2019, remained the world\u0026rsquo;s most deadly group of communicable diseases. LRTIs ranked as the fourth leading cause of death. In low-income countries, LRTIs are the most common cause of death; meanwhile, pneumonia is the only infectious disease among the top 10 causes of death in high income economies\u0026nbsp;[1, 2]. Pneumonia is a common complication in the intensive care unit (ICU). Sometimes, it attributes to the direct cause of death in other common diseases, such as chronic obstructive pulmonary disease (COPD), lung cancer, and Alzheimer\u0026rsquo;s disease\u0026nbsp;[3, 4]. Generally, severe pneumonia requires ICU admission. The 30-day mortality rate of patients with moderate-to-severe community-acquired pneumonia (CAP) in the ICU has been reported to be as high as 23\u0026ndash;47%\u0026nbsp;[5]. Patients with pneumonia who received effective antibiotic therapy within four hours were less likely to die than those who received delayed treatment\u0026nbsp;[6]. Therefore, strengthening early identification and assessing pulmonary infection are beneficial to early warning response, timely prevention and control of infection, and reduction of the injury extent caused by infection to the body. These measures would improve prognosis of pneumonia among patients in the ICU.\u003c/p\u003e\n\u003cp\u003eCurrently, features of an acute respiratory infection, such as fever, cough, sputum production, shortness of breath, and leukocytosis, play an alarming role in initial pneumonia diagnosis. Such features aid in determining the final diagnosis, with supplementary evidence of lung consolidation discovered in computed tomography (CT) or chest X-rays\u0026nbsp;[7]. Although diagnosis seems straightforward, the reliable diagnosis of pneumonia remains a complex and time-consuming process. First, high-risk groups, such as the infants or elderly, who often show atypical symptoms are at an enhanced risk of acute pulmonary failure or sepsis as secondary complications, if diagnosis and treatment intervention are not timely\u0026nbsp;[8]. Second, misdiagnosis is not surprising, because various common co-pathologies, especially COPD and cardiac failure, can cause infiltration, which may be mistaken for areas of consolidation in patients with acute shortness of breath\u0026nbsp;[1]. Third, abnormal clinical manifestations and pulmonary imaging are observed later in the course of the infection. To make matters worse, known biomarkers, such as high-sensitivity C-reactive protein (hsCRP), procalcitonin (PCT), and erythrocyte sedimentation rate (ESR), lack sufficient specificity\u0026nbsp;[9], and the situation for early diagnosis of pneumonia remains grim.\u003c/p\u003e\n\u003cp\u003eExtracellular vesicles (EVs) are membrane-bound vesicles in all body fluids. Exosomes (EXOs), a subset of EVs, refer to lipid bilayer particles with a diameter of 50\u0026ndash;150\u0026thinsp;nm\u0026nbsp;[10, 11]. They, containing several nucleic acids, lipids, and proteins, are released by almost all types of cells into the cell microenvironment for long-distance exchange of information\u0026nbsp;[12, 13]. Among the various active components which can reflect the biological status of parent cells, miRNAs are considered as essential to the function of EVs and have risen to prominence as a novel tool to assist in medical decision-making\u0026nbsp;[14, 15]. Accumulating evidence suggests that miRNAs are selectively incorporated into EVs rather than randomly wrapped into them\u0026nbsp;[16]. In addition, EV-associated miRNAs are pivotal regulators in the pathogenesis of non-infectious and infectious pulmonary disorders\u0026nbsp;[17, 18]. Therefore, extracellular miRNAs in EXOs may be promising diagnostic biomarkers for pulmonary infection and prognostic indicators for progression.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to analyze the role of bronchoalveolar lavage fluid EXOs (BALF-EXOs) and their key miRNAs, which may have potential to indicate the presence of pneumonia or even predict the progression of the disease. To the best of our knowledge, there are relatively few studies on the BALF-EXOs of patients with pneumonia in the ICU.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e\u003cem\u003e2.1. Patients and Samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study enrolled 74 patients on mechanical ventilation who were admitted to the ICU of The Third Affiliated Hospital of Sun Yat-sen University between October 2020 and October 2021. These patients were divided into two groups: the pneumonia group (patients with pulmonary infection) and control group (patients without pulmonary infection). We investigated the demographic and baseline characteristics, radiography and laboratory findings, clinical presentation, treatment, and outcomes of 74 patients for clinical diagnosis. The diagnosis of pulmonary infection was made on the basis of a composite reference standard, which included all microbiological tests and clinical adjudication, referring to the diagnostic criteria of CAP\u0026nbsp;[19]\u0026nbsp;and hospital-acquired pneumonia (HAP)\u0026nbsp;[20].\u0026nbsp;For this study, we excluded the most severely immunocompromised patients\u0026mdash;those with acquired immune deficiency syndrome (AIDS), acute leukemias, lymphomas or other severe congenital immunodeficiency syndromes, those receiving chemotherapy especially with neutropenia, and those who recently received solid organ or bone marrow transplants\u0026nbsp;[1]. This study was approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University\u0026nbsp;(approval no. [2020] 02-254-02).\u003c/p\u003e\n\u003cp\u003eThe pulmonary and subpulmonary segments of the selected patients were lavaged with saline using electronic fiber bronchoscopy, and 15 mL BALF was collected (to avoid oral contamination) in sterile sputum collection tubes. Besides, 10 ml of blood were collected from all individuals and transferred to EDTA-containing tubes to isolate plasma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Cell culture and treatment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHuman monocytes, THP-1, and human lung adenocarcinoma cell line, A549 cells, were maintained in RPIM1640 and DMEM medium, respectively, containing 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin and streptomycin at 37\u0026deg;C, and 5% CO\u003csub\u003e2\u003c/sub\u003e. THP-1 cells were cultured in maintenance media and supplemented with 100\u0026thinsp;ng/mL phorbol 12-myristate 13-acetate (PMA) for 24\u0026thinsp;hours to differentiate them into macrophages (THP-1 derived macrophages [tMACs]). For the control group, normal cultured A549 cells or tMACs were used; for the lipopolysaccharide (LPS) group, conventionally cultured cells were treated with LPS (1\u0026nbsp;ug/mL) for 24 hours.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Exosomal purification and identification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEXOs were purified by differential and ultracentrifugation. Briefly, the BALF, plasma, and cell culture supernatant were centrifuged at a low speed (300 g for 10 min at 4\u0026deg;C) (15 ml polypropylene tube, swinging bucket rotor, model A-4-44, 5804R Refrigerated Centrifuge, Eppendorf, Germany) to remove floating cells. The removed cell debris pellets were collected as liquid biopsy elements. The resulting supernatants were transferred into 1.5 ml polypropylene tubes (Eppendorf, Germany) with a micropipette, and then centrifuged at 12,000 g for 30 min at 4\u0026deg;C (Fixed angle rotor, angle of 45\u0026deg;, model #3331, D-37520 Refrigerated Centrifuge, Thermo Electron Corporation, USA) to dislodge bacteria and some larger extracellular vesicles, such as apoptotic bodies. The resulting supernatants were transferred into Quick-Seal Centrifuge tubes (Beckman Coulter, USA) and centrifuged at 100,000 g for 90 min at 4\u0026deg;C in an Optima L-100xp tabletop ultracentrifuge (Swinging bucket rotor, model SW40 Ti, Optima L-100xp, Beckman Coulter, USA). The resultant pellet (EXOs), which were resuspended with phosphate-buffered saline (PBS) and EXO-free supernatants, was stored at \u0026minus;80\u0026deg;C. Further, negative-staining transmission electron microscopy (TEM) was used to analyze the EXOs. The EXOs were loaded on a copper grid and negatively stained with 3% (w/v) aqueous phosphotungstic acid for 1 min. The grid was then examined using an FEI Tecnai G2 Sprit Twin TEM (FEI, USA). Thereafter, exosomal particles were analyzed using NTA (NanoSight NS300, Malvern Instruments, United Kingdom). The NanoSight NS300 instrument equipped with a sCMOS camera, 488 nm laser (Blue), NTA 3.3 Dev Build 3.3.301 software, and 749 frames were used. In addition, exosomal positive markers (CD63 and CD81) were detected using BD accuri C6 flow cytometer.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4. Candidate miRNA selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the combination of keywords and MeSH terms for \u0026ldquo;pneumonia\u0026rdquo; and \u0026ldquo;microRNA\u0026rdquo;, we searched PubMed for articles that describe associations between miRNAs and pneumonia. Each article was reviewed, and associated miRNAs (\u0026ldquo;miRNAs cluster 1\u0026rdquo;) were recorded. To focus on the miRNAs with a high likelihood of relevance, we considered only miRNAs that had been studied in a population sample to be potential candidates for investigation. For additional details, see supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5. RNA extraction and quantitative reverse transcription PCR (qRT-PCR)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was harvested using TRIzol, according to the manufacturer\u0026rsquo;s instructions. The expression of target miRNA was determined using the SYBR Green Master Mix kit (Takara, Japan). U6 snRNA was used as an internal control, and the fold change was calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u003c/sup\u003e method. For additional details, see supplementary information.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6. Statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe continuous variables are expressed as mean \u0026plusmn; SD values (normally distributed), or as medians [interquartile ranges] (non-normally distributed), and were analyzed using the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test, respectively. The categorical variables are presented as sample rates (constituent ratio); they were compared using the Chi-squared test or Fisher\u0026rsquo;s exact test.Multiple comparisons among three or more groups were analyzed using one-way ANOVA test or Kruskal\u0026ndash;Wallis test (non-parametric). Receiver-operating characteristic (ROC) curves were plotted to investigate diagnostic value of selected miRNAs. The area under the curve (AUC) was calculated to evaluate the performance of these miRNAs in predicting pulmonary infections.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. Patient characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 74 patients, 61 patients had pulmonary infections. Baseline characteristics were similar between the two groups (Table 1). No significant differences in gender, age, and most comorbidities were present between the groups. The incidence of craniocerebral trauma was significantly higher in the control group than in the pneumonia group. This difference was observed because patients in the control group were mainly those who required mechanical ventilation on account of other medical conditions, such as surgery, but without pulmonary infection. A trend for decreased hospital stay was shown in the control group (17.8 days), compared with the pneumonia group (25.2 days); however, the difference was not statistically significant. The inflammation indicator, PCT, was significantly higher in the pneumonia group than in the control group. However, the hsCRP did not differ between both groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic and baseline characteristics of patients\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003eControl Group (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003ePneumonia Group (n=61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eAge, mean (range), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e50.38（22-81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e57.70（21-89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eSex, male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e11(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e49(80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eAny comorbidity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e9(69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e26(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e3(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e14(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e5(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eChronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e2(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e14(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e3(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e10(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e3(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eRenal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e1(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e9(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eCraniocerebral trauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e9(69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e18(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eHospital, mean (range), days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e17.8(2-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e25.2(1-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ehsCRP, mean (range), (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e52.4(0.9-151.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e76.5(1.7-328.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePCT, mean (range), (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.991596638655462%\"\u003e\n \u003cp\u003e1.1(0.1-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.18487394957983%\"\u003e\n \u003cp\u003e4.9(0.02-45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.966386554621849%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: hsCRP, high-sensitivity C-reactive protein; PCT, procalcitonin\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. Characterization of EXOs derived from different samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEXOs were purified from BALF, plasma, and cell culture supernatant using differential centrifugation, as described in the Materials and Methods section. First, the obtained EXOs were characterized according to the minimal experimental requirements for EVs, as defined by the International Society for Extracellular Vesicles [21]. The images captured using transmission electron microscopy (TEM) showed EXOs with a characteristic cup-shaped morphology, as reported in literature [22] (Figure 1A). The size distribution profile of the EXOs was investigated by nanoparticle tracking analysis (NTA) (Figure 1B). Furthermore, the presence of exosomal markers, CD63 and CD81, in all vesicles were revealed by flow cytometry (Figure 1C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Differentially expressed miRNAs specifically showed in BALF-EX0s\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEighteen miRNAs (miR-542-3p, miR-16-5p, miR-20a-3p, miR-27a-5p, miR-92a-3p, miR-342-3p, miR-422a, miR-423-5p, miR-582-3p, miR-885-5p, miR-193b-5p, miR-432-5p, miR-493-3p, miR-452-5p, miR-200b-3p, miR-34a-3p, miR-17-5p, and miR-193a-5p), which were previously revealed to have dysregulated expression profiles in inflammatory diseases using RNA sequencing of population sample, were selected. Among them, 16 miRNAs between two groups showed no statistical difference (Supplementary Figure S1). MiR-17-5p and miR-193a-5p levels were remarkably upregulated in BALF-EXOs of the pneumonia group, compared with the control group (Figure 2A). Thereafter, the expression of these two miRNAs in BALF-cell-debris pellets (Figure 2B) and BALF-EXOs-free supernatants (Figure 2C) were assessed. However, no significant differences were observed between both groups. Notably, the expression of miR-17-5p in BALF-EXOs-free supernatants was too low to be detected.\u003c/p\u003e\n\u003cp\u003eAdditionally, the levels of these two miRNAs in total plasma, plasma-EXOs, and plasma-EXOs-free supernatants were measured. Surprisingly, in plasma-EXOs (Figure 2D), both miR-17-5p and miR-193a-5p showed no difference in level between both groups; the same trend was observed for total plasma (Figure 2E). Besides, the expression of miRNAs in plasma-EXOs-free supernatants was too low to detect.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4. Infectious stimuli increase the candidate miRNAs level in macrophage EXOs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDue to the presence of several interfering factors in clinical samples, \u003cem\u003ein vitro\u003c/em\u003e experiments were conducted to verify that the different expressions of candidate miRNAs were real. EVs in BALF are mainly derived from macrophages in response to LPS [9]. Congruously, due to the low expression levels, the candidate miRNAs were non-detectable in EXOs derived from human alveolar epithelial (A549) cells. Whereas miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs from THP-1-derived macrophages (tMAC-EXOs) of the LPS group, compared with the PBS group (Figure 3A). A same trend was not observed for tMAC-cell-debris pellets (Figure 3B) and tMAC-EXO-free supernatants (Figure 3C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. The candidate miRNA levels in macrophage-EXOs are dynamic in inflammatory response\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between the expression of miR-17-5p and miR-193a-5p in LPS-induced tMAC-EXOs and inflammatory response, the levels of miRNAs in LPS-induced tMAC-EXOs were dynamically measured. In tMAC-EXOs (Figure 4A), the expression of miR-193a-5p was drastically increased in a time-dependent manner after LPS treatment and peaked at 12 hours; it gradually returned to normal levels. Concurrently, miR-17-5p showed the same trend. In tMAC-cell-debris pellets and tMAC-EXO-free supernatants, the levels of the candidate miRNAs were unaffected by LPS stimuli (Figure 4B-C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.6. Diagnostic Potential of BALF-Derived Exosomal miRNAs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether altered exosomal miRNAs have potential diagnostic value in pneumonia，we performed the ROC curve analysis of the upregulated miRNAs. Results showed that levels of PCT, miR-17-5p, and miR-193a-5p could serve as potential diagnostic markers to discriminate the pneumonia group from the control group; the AUCs were 0.685, 0.753, and 0.692, respectively (Figure 5). The diagnostic accuracy values of the indicators are shown in Table 2. miR-193a-5p had the best specificity (100%) but the lowest sensitivity (50.82%) as a single biomarker. Additionally, these two candidate miRNAs were included in the logistic regression analysis. As shown in Table 2, combining the biomarkers did not improve diagnostic utility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Diagnostic accuracy of PCT, hsCRP, miR-17-5p, and miR-193a-5p as single and combined biomarkers for predicting the presence of pneumonia\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003eMicroRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003emiR-17-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.639-0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003e84.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003e59.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003emiR-193a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.574-0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003e50.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003emiR-17-5p+ miR-193a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.633-0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003e92.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003e57.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003e＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003ehsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.531-0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003e61.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003e80.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003e0.1420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.013422818791945%\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.261744966442953%\"\u003e\n \u003cp\u003e0.566-0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.771812080536913%\"\u003e\n \u003cp\u003e69.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.939597315436242%\"\u003e\n \u003cp\u003e62.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.751677852348994%\"\u003e\n \u003cp\u003e0.0254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ehsCRP, high-sensitivity C-reactive protein; PCT, procalcitonin; AUC, area under the curve; CI, confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.7. MiRNA target prediction and pathway Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe roles of miR-17-5P and miR-193a-5p were investigated using bioinformatics.Multiple databases (TargetScan, miRDB, and Funrich) were combined to predict and screen their target genes. For miR-17-5p, 98 target genes were identified (Figure 6A), and the network is shown in Figure 6B. Thereafter, the 98 potential target genes were included in the DAVID database for KEGG_PATHWAY analysis. The results showed that the FoxO signaling pathway was the top-ranked (Figure 6C). Figure 6D-E illustrated the miR-193a-5p target genes. Particularly, protein processing in the endoplasmic reticulum signaling pathway was the enriched pathway for the target genes of two miRNA (Figure 6C, F).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBALF, including alveolar biochemical components and cells, is obtained by infusing physiological saline into alveoli by bronchoscopy, and then aspirating under negative pressure. It allows cells and solute of the lower respiratory tract to be harvested for cytological identification, microculture, and genetic diagnosis\u0026nbsp;[23]. Information gained from BALF is regarded to be a complement of lung biopsy pathology. Therefore, BALF is called liquid biopsy because it accurately reflects changes in the lungs. Furthermore, compared with lung biopsy, BALF is less invasive, safer, and with few complications\u0026nbsp;[24, 25]. EXOs, as an important component of BALF, have been found to be a new mediator of intercellular communication and well-grounded in various pulmonary diseases. In COPD, BALF EXOs have the ability of predicting the damage extent, and serve as an effective monitoring indicator for airway remodeling and airflow limitation\u0026nbsp;[26-28]. In asthma, BALF EXOs can mediate antigen presentation to the adaptive immune system and promote the activation of mast cells, eosinophilia, and alveolar macrophages, to participate in the formation of reversible airway hyperresponsiveness, airway obstruction, and airway remodeling\u0026nbsp;[29, 30]. Furthermore, BALF EXOs play a major role in the diagnosis and prognosis estimation of lung cancer\u0026nbsp;[31-33], pulmonary tuberculosis\u0026nbsp;[34, 35], and idiopathic pulmonary fibrosis\u0026nbsp;[36, 37]. However, few studies have focused on BALF EXOs of pneumonia, which frequently causes acute lung injury (ALI) and its more critical form, acute respiratory distress syndrome (ARDS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn children with severe pneumonia, serum miR-483-3p and miR-29c can be used as biomarkers to provide a novel perspective for diagnosis\u0026nbsp;[38, 39]. Lower levels of Mir-1323 were detected in blood samples of children with \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e-associated pneumonia (MPP) than in blood samples of healthy controls\u0026nbsp;[40]. Hsa-miR-127-3p, hsa-miR-493-5p, and hsa-miR-409-3p, the top three upregulated miRNAs of whole blood microRNAs, provided a clear distinction between healthy individuals and children with adenovirus-infected pneumonia\u0026nbsp;[41]. In pediatric pneumonia, miR-146b could attenuate inflammation injury by inhibiting the MyD88/NF-\u0026kappa;B signaling pathway\u0026nbsp;[42]. In severe community-acquired pneumonia (SCAP), miR-181b serves as a diagnostic and prognostic biomarker[43]. All these studies were based on free miRNAs in blood, which are easy to be degraded. Exosomal miRNAs are encapsulated in a bilayer lipid membrane, which protects them from degrading enzymes, such as ribonucleases, and confers an outstanding stability in body fluids. In fact, the stability is particularly important when developing novel biomarkers using body fluids\u0026nbsp;[44, 45]. For exosomal miRNAs, serum exosomal miRNAs have been reported to have the ability to predict ARDS events in patients with SCAP\u0026nbsp;[46]\u0026nbsp;or be viewed as candidate diagnostic biomarkers in children with adenovirus-infected pneumonia\u0026nbsp;[47]. Additionally, another study outlined the possibility of using miRNAs in serum EVs to differentiate patients with CAP from healthy volunteers\u0026nbsp;[7]. Whereas circulating blood exosomal microRNAs are influenced by systemic pathophysiological states. Our study revealed that, in patients with pneumonia, miR-17-5p and miR-193a-5p were remarkably elevated in BALF-EXOs but not in plasma-EXOs, compared with the control group. This observation implies that plasma-EXO-miRNAs are not effective in identifying pulmonary infections in critically ill patients on mechanical ventilation with similar baseline characteristics. In contrast, BALF-EXOs derived from patients, which can specifically and factually respond to lung pathology, were rarely investigated. The existing studies have established the mouse model of pulmonary inflammation for obtaining BALF-EXOs and conducting \u003cem\u003ein vitro\u003c/em\u003e experiments on respiratory-related cell lines\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBALF-EXOs are secreted by cells, such as epithelial cells, alveolar macrophages, endothelial cells, tumor cells, and stem cells, wherein macrophages and epithelial cells are the main sources\u0026nbsp;[48]. EXOs and their carried miRNAs serve as mediators for the interaction between lung epithelial cells and alveolar macrophages to maintain lung homeostasis. The lung epithelial surfaces, which are directly in contact with the environment, encounter dynamic physical forces as the trachea and alveoli are compressed and stretched during ventilation\u0026nbsp;[49]. Alveolar epithelial cell functions as the first line of defense against harmful injury and is vital in maintaining the integrality and function of lung\u0026nbsp;[49, 50]. First, we measured the candidate miRNAs in A549 cell-EXOs. Quite interestingly, all three miRNAs could not be detected. Macrophages play a crucial role in innate immunity and host defense upon Infectious disease development\u0026nbsp;[51]. In brief, they initiate inflammatory responses to pathogens\u0026nbsp;[52]. Meanwhile, EVs in BALF collected from mice with intratracheal LPS instillation were mainly secreted from lung macrophages\u0026nbsp;[9]. Consistently in our study, miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs from LPS-treated tMACs, compared with controls. The \u003cem\u003ein vitro\u003c/em\u003e experiment revealed that the candidate miRNA levels in macrophage-EXOs is dynamic in inflammatory response; characteristically, they first rise and gradually recover. Therefore, the miRNAs in BALF-EXOs can serve as an early warning to prompt the preparation of microbial cultures, which may contribute to timely identification of pathogens.\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, this study was the first to validate the discriminative ability of upregulated miRNAs in BALF-EXOs for identifying patients with pneumonia from patients without pulmonary infections using ROC curve analysis. Our results demonstrated that miR-17-5p levels may have an acceptable diagnostic value as a biomarker, with an AUC of 0.753, sensitivity of 59.02% sensitivity, and a fixed specificity of 84.62%. These findings highlight BALF-exosomal miRNA dysregulation in patients with pulmonary infections, and provide potential biomarkers for the diagnosis of pneumonia. Another insight of our report is that miR-193a-5p had a 100% specificity, which means that patients with miR-193a-5p levels in BALF-EXOs below the cutoff value is at low risk of lung infection. For these patients, the use of antibiotics can be appropriately reduced to avoid unnecessary antibiotic exposure. This benefit may have crucial impact on public health, especially for countries with excessive antibiotic consumption.\u003c/p\u003e\n\u003cp\u003eMiRNAs are small non-coding RNAs that have the capability of regulating gene expression by promoting their target messenger RNA (mRNA) degradation or inhibiting the translation of target genes\u0026nbsp;[53, 54]. Previously, miR-17-5p and miR-193a-5p were both identified as candidates for targeted therapy in many diseases, particularly in cancer therapy\u0026nbsp;[55-60]. To further explore the molecular mechanisms of miR-17-5p and miR-193a-5p in infective lung disease, three databases (miRBD, Funrich, and TargetScan) were used to identify their respective potential targets. The target genes involved in the signaling pathways were evaluated using the KEGG database. The signaling pathway that drew our attention was protein processing in endoplasmic reticulum, which plays vital roles in the control of the progression of the cell cycle, differentiation, inflammation, aging, and immunity\u0026nbsp;[61-63]. However, its role in the pathogenesis of lung inflammatory responses needs further experiments to verify.\u003c/p\u003e\n\u003cp\u003eOur study had several limitations. First, all patients were recruited from a single center, and the sample size was relatively small. Second, exact mechanisms of how these miRNAs function in lung inflammation are still unclear. Future studies are needed to confirm the actual regulatory targets and biological functions of the discovered miRNAs to obtain practical experimental evidence of the mechanistic processes involved in pneumonia.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study indicated that, for patients on mechanical ventilation in the ICU, a further increase in miR-17-5p and miR-193a-5p levels in EXOs derived from BALF may suggest an increased risk of developing pneumonia. Moreover, miR-193a-5p may serve as an indicator to guide the strategy of antibiotic use. To achieve the successful use of these miRNAs as biomarkers, studies in larger patient cohorts will be required to confirm existing results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and\u0026nbsp;consent to participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study was approved by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University\u0026nbsp;(approval no. [2020] 02-254-02).\u0026nbsp;Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003ePatients signed informed consent regarding publishing their data and photographs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e：Thedata and materials used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (No. 81902081), the Natural Science Foundation of Guangdong Province (No.2020A1515011573), Guangzhou Science and Technology Program (No.\u0026nbsp;202102100003), Guangdong Province Science and Technology Program (No.\u0026nbsp;2005A20901005), and Major Science and Technology Project of Guangdong Province (No. 2013B020224002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eL.W., K.Z., and ZD.W. conceived and designed the experiments; Y.S., Y.X., Z.D., and ZP.W. performed and analyzed most of the experiments; Y.S., Y.X., J.L., Y.L., and X.B. contributed to the performance and analyses of experiments; L.W., Y.S., and K.Z. wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not Applicable\u003c/p\u003e\n"},{"header":"References","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Wunderink, R.G. and G. Waterer. 2017. Advances in the causes and management of community acquired pneumonia in adults\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBmj\u003c/em\u003e 358: j2471.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2019.World Health Organization. The top 10 causes of death\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003ca href=\"https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\"\u003e\u003cem\u003ehttps://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\u003c/em\u003e\u003c/a\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mitchell, S.L., J.M. Teno, D.K. Kiely, M.L. Shaffer, R.N. Jones, H.G. Prigerson, L. Volicer, J.L. Givens, and M.B. Hamel. 2009. The clinical course of advanced dementia\u003cem\u003e. N Engl J Med\u003c/em\u003e 361(16): 1529-38.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sharafkhaneh, A., A.M. Spiegelman, K. Main, S. Tavakoli-Tabasi, C. Lan, and D. Musher. 2017. Mortality in Patients Admitted for Concurrent COPD Exacerbation and Pneumonia\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCopd\u003c/em\u003e 14(1): 23-29.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chen, H., Y. Yin, H. Gao, Y. Guo, Z. Dong, X. Wang, Y. Zhang, S. Yang, Q. Peng, Y. Liu, and H. Wang. 2020. Clinical Utility of In-house Metagenomic Next-generation Sequencing for the Diagnosis of Lower Respiratory Tract Infections and Analysis of the Host Immune Response\u003cem\u003e.\u003c/em\u003e \u003cem\u003eClin Infect Dis\u003c/em\u003e 71(Suppl 4): S416-s426.\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sligl, W.I., H. Hoang, D.T. Eurich, A. Malhotra, T.J. Marrie, and S.R. Majumdar. 2013. Macrolide use in the treatment of critically ill patients with pneumonia: Incidence, correlates, timing and outcomes\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCan J Infect Dis Med Microbiol\u003c/em\u003e 24(4): e107-12.\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hermann, S., F. Brandes, B. Kirchner, D. Buschmann, M. Borrmann, M. Klein, S. Kotschote, M. Bonin, M. Reithmair, I. Kaufmann, G. Schelling, and M.W. Pfaffl. 2020. Diagnostic potential of circulating cell-free microRNAs for community-acquired pneumonia and pneumonia-related sepsis.\u003cem\u003e\u0026nbsp;J Cell Mol Med\u003c/em\u003e 24(20): 12054-12064.\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Musher, D.M. and A.R. Thorner. 2014. Community-acquired pneumonia\u003cem\u003e.\u003c/em\u003e \u003cem\u003eN Engl J Med\u003c/em\u003e 371(17): 1619-28.\u003c/p\u003e\n\u003cp\u003e9.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Zhang, D., H. Lee, X. Wang, M. Groot, L. Sharma, C.S. Dela Cruz, and Y. Jin. 2019. A potential role of microvesicle-containing miR-223/142 in lung inflammation\u003cem\u003e.\u003c/em\u003e \u003cem\u003eThorax\u003c/em\u003e 74(9): 865-874.\u003c/p\u003e\n\u003cp\u003e10.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kalluri, R. and V.S. LeBleu. 2020. The biology, function, and biomedical applications of exosomes\u003cem\u003e.\u003c/em\u003e \u003cem\u003eScience\u003c/em\u003e 367(6478): eaau6977\u003c/p\u003e\n\u003cp\u003e11.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Arabpour, M., A. Saghazadeh, and N. Rezaei. 2021. Anti-inflammatory and M2 macrophage polarization-promoting effect of mesenchymal stem cell-derived exosomes.\u003cem\u003eInt Immunopharmacol\u003c/em\u003e 97: 107823.\u003c/p\u003e\n\u003cp\u003e12.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lőrincz \u0026Aacute;, M., B. Bartos, D. Szombath, V. Szeifert, C.I. Tim\u0026aacute;r, L. Turi\u0026aacute;k, L. Drahos, \u0026Aacute;. Kittel, D.S. Veres, F. Kolonics, A. M\u0026oacute;csai, and E. Ligeti. 2020. Role of Mac-1 integrin in generation of extracellular vesicles with antibacterial capacity from neutrophilic granulocytes\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Extracell Vesicles\u003c/em\u003e 9(1): 1698889.\u003c/p\u003e\n\u003cp\u003e13.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gao, L., L. Wang, T. Dai, K. Jin, Z. Zhang, S. Wang, F. Xie, P. Fang, B. Yang, H. Huang, H. van Dam, F. Zhou, and L. Zhang. 2018. Tumor-derived exosomes antagonize innate antiviral immunity\u003cem\u003e.\u003c/em\u003e \u003cem\u003eNat Immunol\u003c/em\u003e 19(3): 233-245.\u003c/p\u003e\n\u003cp\u003e14.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Wang, L., Y. Liao, R. Yang, Z. Yu, L. Zhang, Z. Zhu, X. Wu, J. Shen, J. Liu, L. Xu, Z. Wu, and X. Sun. 2020. Sja-miR-71a in Schistosome egg-derived extracellular vesicles suppresses liver fibrosis caused by schistosomiasis via targeting semaphorin 4D\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Extracell Vesicles\u003c/em\u003e 9(1): 1785738.\u003c/p\u003e\n\u003cp\u003e15.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;de Gonzalo-Calvo, D., A. Vea, C. B\u0026auml;r, J. Fiedler, L.S. Couch, C. Brotons, V. Llorente-Cortes, and T. Thum. 2019. Circulating non-coding RNAs in biomarker-guided cardiovascular therapy: a novel tool for personalized medicine? \u003cem\u003eEur Heart J\u003c/em\u003e 40(20): 1643-1650.\u003c/p\u003e\n\u003cp\u003e16.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhang, J., S. Li, L. Li, M. Li, C. Guo, J. Yao, and S. Mi. 2015. Exosome and exosomal microRNA: trafficking, sorting, and function. \u003cem\u003eGenomics Proteomics Bioinformatics\u003c/em\u003e 13(1): 17-24.\u003c/p\u003e\n\u003cp\u003e17.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lee, H., E. Abston, D. Zhang, A. Rai, and Y. Jin. 2018. Extracellular Vesicle: An Emerging Mediator of Intercellular Crosstalk in Lung Inflammation and Injury. \u003cem\u003eFront Immunol\u003c/em\u003e 9: 924.\u003c/p\u003e\n\u003cp\u003e18.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Jung, A.L., M. M\u0026oslash;ller J\u0026oslash;rgensen, R. B\u0026aelig;k, K. Griss, M. Han, K. Auf Dem Brinke, N. Timmesfeld, W. Bertrams, T. Greulich, R. Koczulla, S. Hippenstiel, N. Suttorp, and B. Schmeck. 2020. Surface Proteome of Plasma Extracellular Vesicles as Biomarkers for Pneumonia and Acute Exacerbation of Chronic Obstructive Pulmonary Disease\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Infect Dis\u003c/em\u003e 221(2): 325-335.\u003c/p\u003e\n\u003cp\u003e19.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Metlay, J.P., G.W. Waterer, A.C. Long, A. Anzueto, J. Brozek, K. Crothers, L.A. Cooley, N.C. Dean, M.J. Fine, S.A. Flanders, M.R. Griffin, M.L. Metersky, D.M. Musher, M.I. Restrepo, and C.G. Whitney. 2019. Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America.\u003cem\u003e\u0026nbsp;Am J Respir Crit Care Med\u003c/em\u003e. 200(7): e45-e67.\u003c/p\u003e\n\u003cp\u003e20.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kalil, A.C., M.L. Metersky, M. Klompas, J. Muscedere, D.A. Sweeney, L.B. Palmer, L.M. Napolitano, N.P. O\u0026apos;Grady, J.G. Bartlett, J. Carratal\u0026agrave;, A.A. El Solh, S. Ewig, P.D. Fey, T.M. File, Jr., M.I. Restrepo, J.A. Roberts, G.W. Waterer, P. Cruse, S.L. Knight, and J.L. Brozek. 2016. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. \u003cem\u003eClin Infect Dis\u003c/em\u003e 63(5): e61-e111.\u003c/p\u003e\n\u003cp\u003e21.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;L\u0026ouml;tvall, J., A.F. Hill, F. Hochberg, E.I. Buz\u0026aacute;s, D. Di Vizio, C. Gardiner, Y.S. Gho, I.V. Kurochkin, S. Mathivanan, P. Quesenberry, S. Sahoo, H. Tahara, M.H. Wauben, K.W. Witwer, and C. Th\u0026eacute;ry. 2014. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. \u003cem\u003eJ Extracell Vesicles\u003c/em\u003e 3: 26913.\u003c/p\u003e\n\u003cp\u003e22.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pascucci, L. and G. Scattini. 2021. Imaging extracelluar vesicles by transmission electron microscopy: Coping with technical hurdles and morphological interpretation\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBiochim Biophys Acta Gen Subj\u003c/em\u003e 1865(4): 129648.\u003c/p\u003e\n\u003cp\u003e23.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Liu, Z., J. Yan, L. Tong, S. Liu, and Y. Zhang. 2021.The role of exosomes from BALF in lung disease. \u003cem\u003eJ Cell Physiol\u003c/em\u003e 237(1):161-168.\u003c/p\u003e\n\u003cp\u003e24.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Costabel, U. and J. Guzman. 2001. Bronchoalveolar lavage in interstitial lung disease\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCurr Opin Pulm Med\u003c/em\u003e 7(5): 255-61.\u003c/p\u003e\n\u003cp\u003e25.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Roth, K., J.A. Hardie, A.H. Andreassen, F. Leh, and T.M. Eagan. 2008. Predictors of diagnostic yield in bronchoscopy: a retrospective cohort study comparing different combinations of sampling techniques\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBMC Pulm Med\u003c/em\u003e 8:2.\u003c/p\u003e\n\u003cp\u003e26.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Takahashi, T. and H. Kubo. 2014. The role of microparticles in chronic obstructive pulmonary disease\u003cem\u003e. Int J Chron Obstruct Pulmon Dis\u003c/em\u003e 9: 303-14.\u003c/p\u003e\n\u003cp\u003e27.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Armstrong, D.A., A.B. Nymon, C.S. Ringelberg, C. Lesseur, H.F. Hazlett, L. Howard, C.J. Marsit, and A. Ashare. 2017. Pulmonary microRNA profiling: implications in upper lobe predominant lung disease\u003cem\u003e.\u003c/em\u003e \u003cem\u003eClin Epigenetics\u003c/em\u003e 9: 56.\u003c/p\u003e\n\u003cp\u003e28.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Genschmer, K.R., D.W. Russell, C. Lal, T. Szul, P.E. Bratcher, B.D. Noerager, M. Abdul Roda, X. Xu, G. Rezonzew, L. Viera, B.S. Dobosh, C. Margaroli, T.H. Abdalla, R.W. King, C.M. McNicholas, J.M. Wells, M.T. Dransfield, R. Tirouvanziam, A. Gaggar, and J.E. Blalock. 2019. Activated PMN Exosomes: Pathogenic Entities Causing Matrix Destruction and Disease in the Lung. Cell 176(1-2): 113-126.e15.\u003c/p\u003e\n\u003cp\u003e29.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fujita, Y., Y. Yoshioka, S. Ito, J. Araya, K. Kuwano, and T. Ochiya. 2014. Intercellular communication by extracellular vesicles and their microRNAs in asthma\u003cem\u003e.\u003c/em\u003e \u003cem\u003eClin Ther\u003c/em\u003e 36(6): 873-81.\u003c/p\u003e\n\u003cp\u003e30.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hackett, T.L.. 2012. Epithelial-mesenchymal transition in the pathophysiology of airway remodelling in asthma. Curr Opin Allergy Clin Immunol 12(1): 53-9.\u003c/p\u003e\n\u003cp\u003e31.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Thakur, B.K., H. Zhang, A. Becker, I. Matei, Y. Huang, B. Costa-Silva, Y. Zheng, A. Hoshino, H. Brazier, J. Xiang, C. Williams, R. Rodriguez-Barrueco, J.M. Silva, W. Zhang, S. Hearn, O. Elemento, N. Paknejad, K. Manova-Todorova, K. Welte, J. Bromberg, H. Peinado, and D. Lyden. 2014. \u0026nbsp;Double-stranded DNA in exosomes: a novel biomarker in cancer detection. \u003cem\u003eCell Res\u0026nbsp;\u003c/em\u003e24(6): 766-9.\u003c/p\u003e\n\u003cp\u003e32.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kim, J.E., J.S. Eom, W.Y. Kim, E.J. Jo, J. Mok, K. Lee, K.U. Kim, H.K. Park, M.K. Lee, and M.H. Kim. 2018.Diagnostic value of microRNAs derived from exosomes in bronchoalveolar lavage fluid of early-stage lung adenocarcinoma: A pilot study\u003cem\u003e.\u003c/em\u003e \u003cem\u003eThorac Cancer\u0026nbsp;\u003c/em\u003e9(8): 911-915.\u003c/p\u003e\n\u003cp\u003e33.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhang, X., B. Sai, F. Wang, L. Wang, Y. Wang, L. Zheng, G. Li, J. Tang, and J. Xiang. 2019. Hypoxic BMSC-derived exosomal miRNAs promote metastasis of lung cancer cells via STAT3-induced EMT.\u003cem\u003e\u0026nbsp;Mol Cancer\u003c/em\u003e 18(1): 40.\u003c/p\u003e\n\u003cp\u003e34.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kruh-Garcia, N.A., L.M. Wolfe, L.H. Chaisson, W.O. Worodria, P. Nahid, J.S. Schorey, J.L. Davis, and K.M. Dobos. 2014. Detection of Mycobacterium tuberculosis peptides in the exosomes of patients with active and latent M. tuberculosis infection using MRM-MS\u003cem\u003e.\u003c/em\u003e \u003cem\u003ePLoS One\u003c/em\u003e 9(7): e103811.\u003c/p\u003e\n\u003cp\u003e35.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sinha, S., K. Gupta, D. Mandal, B.K. Das, and R.M. Pandey. 2018. Serum and Bronchoalveolar Lavage Fluid 25(OH)Vitamin D3 Levels in HIV-1 and Tuberculosis: A Cross-Sectional Study from a Tertiary Care Center in North India\u003cem\u003e. Curr HIV Res\u003c/em\u003e. 16(2): 167-173.\u003c/p\u003e\n\u003cp\u003e36.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Liu, B., T. Jiang, X. Hu, Z. Liu, L. Zhao, H. Liu, Z. Liu, and L. Ma. 2018. Downregulation of microRNA‑30a in bronchoalveolar lavage fluid from idiopathic pulmonary fibrosis patients. \u003cem\u003eMol Med Rep\u003c/em\u003e 18(6): 5799-5806.\u003c/p\u003e\n\u003cp\u003e37.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rollet-Cohen, V., M. Bourderioux, J. Lipecka, C. Chhuon, V.A. Jung, M. Mesbahi, T. Nguyen-Khoa, S. Gu\u0026eacute;rin-Pfyffer, A. Schmitt, A. Edelman, I. Sermet-Gaudelus, and I.C. Guerrera. 2018. Comparative proteomics of respiratory exosomes in cystic fibrosis, primary ciliary dyskinesia and asthma\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Proteomics\u003c/em\u003e 185: 1-7.\u003c/p\u003e\n\u003cp\u003e38.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhou, J., J. Lin, Y. Zhao, and X. Sun. 2021. Deregulated Expression of miR-483-3p Serves as a Diagnostic Biomarker in Severe Pneumonia Children with Respiratory Failure and Its Predictive Value for the Clinical Outcome of Patients\u003cem\u003e.\u003c/em\u003e \u003cem\u003eMol Biotechnol\u003c/em\u003e 64(3):311-319.\u003c/p\u003e\n\u003cp\u003e39.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Guo, J., N. Zhang, G. Liu, A. Zhang, X. Liu, and J. Zheng. 2021. Upregulated expression of long non-coding RNA MEG3 serves as a prognostic biomarker in severe pneumonia children and its regulatory mechanism\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBioengineered\u0026nbsp;\u003c/em\u003e12(1): 7120-7131.\u003c/p\u003e\n\u003cp\u003e40.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Yin, L., Y. Ma, W. Wang, and Y. Zhu. 2021. The critical function of miR-1323/Il6 axis in children with Mycoplasma pneumoniae pneumonia. \u003cem\u003eJ Pediatr (Rio J)\u003c/em\u003e 97(5): 552-558.\u003c/p\u003e\n\u003cp\u003e41.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Huang, F., J. Zhang, D. Yang, Y. Zhang, J. Huang, Y. Yuan, X. Li, and G. Lu. 2018. MicroRNA Expression Profile of Whole Blood Is Altered in Adenovirus-Infected Pneumonia Children. \u003cem\u003eMediators Inflamm\u003c/em\u003e 2018: 2320640.\u003c/p\u003e\n\u003cp\u003e42.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhang, L., L. Dong, Y. Tang, M. Li, and M. Zhang. 2020. MiR-146b protects against the inflammation injury in pediatric pneumonia through MyD88/NF-\u0026kappa;B signaling pathway\u003cem\u003e.\u003c/em\u003e \u003cem\u003eInfect Dis (Lond)\u003c/em\u003e 52(1): 23-32.\u003c/p\u003e\n\u003cp\u003e43.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Li, Q., T. Wu, and S. Li. 2021. MiR-181b serves as diagnosis and prognosis biomarker in severe community-acquired pneumonia. \u003cem\u003eGenet Mol Biol\u003c/em\u003e 44(3): e20200431.\u003c/p\u003e\n\u003cp\u003e44.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Valadi, H., K. Ekstr\u0026ouml;m, A. Bossios, M. Sj\u0026ouml;strand, J.J. Lee, and J.O. L\u0026ouml;tvall. 2007. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells\u003cem\u003e.\u003c/em\u003e \u003cem\u003eNat Cell Biol\u003c/em\u003e 9(6): 654-9.\u003c/p\u003e\n\u003cp\u003e45.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Li, Y., Q. Zheng, C. Bao, S. Li, W. Guo, J. Zhao, D. Chen, J. Gu, X. He, and S. Huang. 2015. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. \u003cem\u003eCell Res\u003c/em\u003e 25(8): 981-4.\u003c/p\u003e\n\u003cp\u003e46.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Wu, X., C. Wu, W. Gu, H. Ji, and L. Zhu. 2019. Serum Exosomal MicroRNAs Predict Acute Respiratory Distress Syndrome Events in Patients with Severe Community-Acquired Pneumonia\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBiomed Res Int\u003c/em\u003e 2019: 3612020.\u003c/p\u003e\n\u003cp\u003e47.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Huang, F., J. Bai, J. Zhang, D. Yang, H. Fan, L. Huang, T. Shi, and G. Lu. 2019. Identification of potential diagnostic biomarkers for pneumonia caused by adenovirus infection in children by screening serum exosomal microRNAs\u003cem\u003e.\u003c/em\u003e \u003cem\u003eMol Med Rep\u003c/em\u003e 19(5): 4306-4314.\u003c/p\u003e\n\u003cp\u003e48.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Alipoor, S.D., E. Mortaz, J. Garssen, M. Movassaghi, M. Mirsaeidi, and I.M. Adcock. 2016. Exosomes and Exosomal miRNA in Respiratory Diseases\u003cem\u003e.\u003c/em\u003e \u003cem\u003eMediators Inflamm\u003c/em\u003e 2016: 5628404.\u003c/p\u003e\n\u003cp\u003e49.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Whitsett, J.A. and T. Alenghat. 2015. Respiratory epithelial cells orchestrate pulmonary innate immunity. \u003cem\u003eNat Immunol\u003c/em\u003e 16(1): 27-35.\u003c/p\u003e\n\u003cp\u003e50.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lee, H., D. Zhang, J. Wu, L.E. Otterbein, and Y. Jin. 2017. Lung Epithelial Cell-Derived Microvesicles Regulate Macrophage Migration via MicroRNA-17/221-Induced Integrin \u0026beta;(1) Recycling. \u003cem\u003eJ Immunol\u0026nbsp;\u003c/em\u003e199(4): 1453-1464.\u003c/p\u003e\n\u003cp\u003e51.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhu, Z., D. Zhang, H. Lee, A.A. Menon, J. Wu, K. Hu, and Y. Jin. 2017. Macrophage-derived apoptotic bodies promote the proliferation of the recipient cells via shuttling microRNA-221/222.\u003cem\u003e\u0026nbsp;J Leukoc Biol\u003c/em\u003e 101(6): 1349-1359.\u003c/p\u003e\n\u003cp\u003e52.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bissonnette, E.Y., J.F. Lauzon-Joset, J.S. Debley, and S.F. Ziegler. 2020. Cross-Talk Between Alveolar Macrophages and Lung Epithelial Cells is Essential to Maintain Lung Homeostasis. \u003cem\u003eFront Immunol\u003c/em\u003e 11: 583042.\u003c/p\u003e\n\u003cp\u003e53.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;He, L. and G.J. Hannon. 2004. MicroRNAs: small RNAs with a big role in gene regulation. \u003cem\u003eNat Rev Genet\u003c/em\u003e 5(7): 522-31.\u003c/p\u003e\n\u003cp\u003e54.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Guiot, J., M. Cambier, A. Boeckx, M. Henket, O. Nivelles, F. Gester, E. Louis, M. Malaise, F. Dequiedt, R. Louis, I. Struman, and M.S. Njock. 2020. Macrophage-derived exosomes attenuate fibrosis in airway epithelial cells through delivery of antifibrotic miR-142-3p. \u003cem\u003eThorax\u003c/em\u003e 75(10): 870-881.\u003c/p\u003e\n\u003cp\u003e55.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Xu, H., Y. Liu, P. Cheng, C. Wang, Y. Liu, W. Zhou, Y. Xu, and G. Ji. 2020. CircRNA_0000392 promotes colorectal cancer progression through the miR-193a-5p/PIK3R3/AKT axis. \u003cem\u003eJ Exp Clin Cancer Res\u0026nbsp;\u003c/em\u003e39(1): 283.\u003c/p\u003e\n\u003cp\u003e56.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Wang, J.T. and Z.H. Wang. 2018. Role of miR-193a-5p in the proliferation and apoptosis of hepatocellular carcinoma.\u003cem\u003e\u0026nbsp;Eur Rev Med Pharmacol Sci\u003c/em\u003e 22(21): 7233-7239.\u003c/p\u003e\n\u003cp\u003e57.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Azar, M., H. Aghazadeh, H.N. Mohammed, M.R.S. Sara, A. Hosseini, N. Shomali, R. Tamjidifar, S. Tarzi, M. Mansouri, S.P. Sarand, F. Marofi, M. Akbari, H. Xu, and S.S. Shotorbani. 2021. miR-193a-5p as a promising therapeutic candidate in colorectal cancer by reducing 5-FU and Oxaliplatin chemoresistance by targeting CXCR4. \u003cem\u003eInt Immunopharmacol\u003c/em\u003e 92: 107355.\u003c/p\u003e\n\u003cp\u003e58.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Duan, B., S. Shi, H. Yue, B. You, Y. Shan, Z. Zhu, L. Bao, and Y. You. 2019. Exosomal miR-17-5p promotes angiogenesis in nasopharyngeal carcinoma via targeting BAMBI. \u003cem\u003eJ Cancer\u003c/em\u003e 10(26): 6681-6692.\u003c/p\u003e\n\u003cp\u003e59.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Song, J., Y. Liu, T. Wang, B. Li, and S. Zhang. 2020. MiR-17-5p promotes cellular proliferation and invasiveness by targeting RUNX3 in gastric cancer. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e 128: 110246.\u003c/p\u003e\n\u003cp\u003e60.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Zhang, Y., S. Wang, Q. Lai, Y. Fang, C. Wu, Y. Liu, Q. Li, X. Wang, C. Gu, J. Chen, J. Cai, A. Li, and S. Liu. 2020. Cancer-associated fibroblasts-derived exosomal miR-17-5p promotes colorectal cancer aggressive phenotype by initiating a RUNX3/MYC/TGF-\u0026beta;1 positive feedback loop. \u003cem\u003eCancer Lett\u003c/em\u003e 491: 22-35.\u003c/p\u003e\n\u003cp\u003e61.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Moon, H.W., H.G. Han, and Y.J. Jeon. 2018. Protein Quality Control in the Endoplasmic Reticulum and Cancer\u003cem\u003e.\u003c/em\u003e \u003cem\u003eInt J Mol Sci\u003c/em\u003e 19(10): 3020\u003c/p\u003e\n\u003cp\u003e62.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;L\u0026oacute;pez-Ot\u0026iacute;n, C., M.A. Blasco, L. Partridge, M. Serrano, and G. Kroemer. 2013. The hallmarks of aging. \u003cem\u003eCell\u003c/em\u003e 153(6): 1194-217.\u003c/p\u003e\n\u003cp\u003e63.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Wang, M. and R.J. Kaufman. 2016. Protein misfolding in the endoplasmic reticulum as a conduit to human disease. \u003cem\u003eNature\u003c/em\u003e 529(7586): 326-35.\u003c/p\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":"Pneumonia, exosomes, miR-17-5p, miR-193a-5p, diagnostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-1508650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1508650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Early and appropriate antibiotic treatment reduces morbidity and mortality of pneumonia. However, early prediction of pulmonary infection remains a challenge for clinicians. Current clinical needs require the development and use of rapid and effective diagnostic indicators to accelerate the identification of pneumonia and process of microbiological diagnosis. MicroRNAs (miRNAs) in exosomes (EXOs) have become attractive candidates for novel biomarkers to evaluate the presence and progress of many diseases. We assessed their performance as biomarkers of pneumonia. Patients were divided into pneumonia group (with pneumonia) and control group (without pneumonia). We identified and compared two upregulated miRNAs in EXOs derived from bronchoalveolar lavage fluid (BALF) between the pneumonia and control groups (miR-17-5p, p=0.009; miR-193a-5p, p=0.031). Interestingly, miR-17-5p and miR-193a-5p in BALF-cell-debris pellets, BALF-EXO-free supernatants, total plasma, and plasma-EXOs did not differ significantly between both groups. In vitro experiments revealed that miR-17-5p and miR-193a-5p were strikingly upregulated in EXOs derived from macrophages stimulated by LPS. Receiver operator characteristic (ROC) curve analysis indicated that exosomal miR-17-5p (area under the curve, AUC: 0.753) and exosomal miR-193a-5p (AUC: 0.629) have acceptable diagnostic value. This study is one of the few studies on BALF-EXO-miRNAs in patients with pneumonia, providing potential diagnostic biomarkers and therapeutic targets for pneumonia.","manuscriptTitle":"Diagnostic potential of bronchoalveolar lavage fluid exosomal microRNAs for pneumonia in the intensive care unit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-11 17:03:34","doi":"10.21203/rs.3.rs-1508650/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":"2f974f81-0d0f-4b60-8072-108f1cdba353","owner":[],"postedDate":"April 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-25T14:46:42+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-11 17:03:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1508650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1508650","identity":"rs-1508650","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}