CD14 and LBP as Novel Biomarkers for Sarcoidosis by Proteomics of Extracellular Vesicles

CD14 and LBP as Novel Biomarkers for Sarcoidosis by Proteomics of Extracellular Vesicles | 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 CD14 and LBP as Novel Biomarkers for Sarcoidosis by Proteomics of Extracellular Vesicles Yu Futami, Yoshito Takeda, Taro Koba, Ryohei Narumi, Yosui Nojima, and 25 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-793224/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 Sarcoidosis is an inflammatory granulomatous multi-organ disease of unknown cause. The granulomatous inflammation in sarcoidosis is driven by the interplay between T cells and macrophages. Extracellular vesicles (EVs) play important roles in intercellular communication. Isolated serum EVs from seven patients with sarcoidosis and five control subjects were subjected to non-targeted proteomics analysis. Then 2,292 proteins were detected; 42 proteins were upregulated in patients with sarcoidosis relative to control subjects, and 324 proteins were downregulated. The protein signature of EVs from patients with sarcoidosis reflected disease characteristics such as antigen presentation and immunological disease. Candidate biomarkers were further verified by targeted proteomics analysis in 46 patients and 10 control subjects. CD14 and lipopolysaccharide-binding protein (LBP) were validated by targeted proteomics analysis and upregulation of them was confirmed by immunoblotting. Their expression was also upregulated in macrophages of lung granulomatous lesions. Consistent with these findings, CD14 levels were increased in lipopolysaccharide-stimulated macrophages during multinucleation, concomitant with increased levels of CD14 and LBP in EVs. The area under the curve values of CD14 and LBP were 0.81 and 0.84, respectively. Thus CD14 and LBP in serum EVs, which are associated with granulomatous pathogenesis, can improve the diagnostic accuracy in patients with sarcoidosis. Scientific Communication Bioinformatics Sarcoidosis inflammatory granulomatous multi-organ disease CD14 and LBP novel biomarkers extracellular vesicles Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Sarcoidosis is a systemic granulomatous disease associated with T-lymphocyte and macrophage activation and migration of these cells into affected organs. There is heterogeneity in disease manifestation, severity, and clinical course. 1–3 To better understand this disease, we need effective biomarkers for diagnosis and prognosis. Unfortunately, the ideal biomarker for sarcoidosis has not yet been discovered. Among identified serum biomarkers, angiotensin-converting enzyme (ACE) and soluble interleukin-2 receptor (sIL-2R) are most relevant but lack sensitivity and specificity. 4 Roughly 30–80% of patients with sarcoidosis have increased ACE levels; sensitivity ranges between 22 and 86% and specificity between 54 and 95%. 5 On the other hand, sIL-2R levels are proposed as a marker of disease activity in sarcoidosis. But elevated sIL-2R levels are not specific for sarcoidosis and can be found in other granulomatous diseases, hematological malignancies, and various autoimmune disorders. 4 The use of sIL-2R as a diagnostic marker for sarcoidosis remains a matter of debate. In light of progress in mass spectrometry (MS) technology, a great deal of attention has been paid to omics approaches for the study of heterogeneous diseases. 6 Although advances in proteomics have facilitated the discovery of protein biomarkers, the application of proteomics, which measures features that are closer to the phenotype, has been limited in comparison with genomics and transcriptomics. 7 Although serum is regarded as an ideal source of marker molecules due to high reproducibility and minimal invasiveness, the masking of small amounts of key proteins is unavoidable because of the wide dynamic range. 8 To solve this problem, we focused in this study on extracellular vesicles (EVs) in the serum. EVs are increasingly appreciated as important carriers of biologic cargo that play key roles in intercellular communication by transferring contents such as mRNA, microRNA, and proteins between neighboring cells. EV constituents play a variety of pathological roles in diseases including malignancies, inflammatory disorders, and infections. 7,9,10 From these backgrounds, EV sandwich ELISA system has been developed to diagnose lung cancer for clinical usage. 11 Although ultracentrifugation has been widely regarded as the gold standard to isolate EVs, size exclusion chromatography (SEC)-based EV isolation yields much higher purity through MS analysis than ultracentrifugation. 9 Despite progress in exosome research, no standard method exists for providing exact quantitative and qualitative analyses of exosomes. 12 Some methods have been developed for the isolation of exosomes, including ultracentrifugation, affinity-based methods, and SEC. Some comparative studies showed that the isolation of EVs by SEC retains the biophysical properties of EVs, resulting in a higher yield. 13 Tandem mass tag-based non-targeted proteomics analysis of serum EVs isolated by ultracentrifugation revealed that fibulin-3 in EVs may serve as a novel diagnostic biomarker for chronic obstructive pulmonary disease and might be closely related to its pathophysiology. 14 With this background, we isolated serum EVs using SEC, which can detect less abundant serum proteins, thereby applying label-free proteomics strategies to discover novel biomarkers for sarcoidosis. Methods Study Design. Participants were recruited for a discovery and a validation phase from the database of patients diagnosed with sarcoidosis at Osaka University Hospital (2013–2019). All patients with sarcoidosis were diagnosed according to the international ATS/ERS/WASOG criteria. 1 Serum samples were separated by centrifugation and stored frozen at −80°C for further analyses. To sustain the sample quality, the procedure such as freezing and thawing of serum was avoided as much as possible. For the discovery cohort, 5 control subjects and 7 patients with sarcoidosis were selected (Table S1). Patients with sarcoidosis were untreated when the samples were collected. The samples were subjected to quantitative high-throughput proteomics using liquid chromatography-mass spectrometry (LC-MS/MS). The validation cohort included 10 control subjects and 46 patients with sarcoidosis. For validation, candidate proteins identified in the discovery cohort were quantified by targeted proteomics using MS (selected reaction monitoring [SRM]) as described previously. 15 The clinical information of participants is shown in Table S2. This study was performed according to the guidelines described in the Declaration of Helsinki for medical research involving human subjects. This study protocol was approved by the Ethics Committee of Osaka University (no. 17148), and all study participants gave written informed consent. EV Isolation. Serum EVs were isolated by size-exclusion chromatography, using an EV Second L70 SEC column (GL Science, Tokyo, Japan). 16 After blocking with fetal bovine serum (FBS) and washing with phosphate-buffered saline (PBS), 400 μl of serum was loaded onto the column and eluted with PBS. The first 300 μl of the eluate was discarded; thereafter, the eluate was collected in three fractions of 100 μl each. The isolation of EVs was confirmed according to the guidelines delineated in Minimal information for studies of extracellular vesicles 2018 (MISEV2018). 17 Size distributions and numbers were confirmed by NanoSight nanoparticle tracking analysis (Malvern Instruments, Malvern, UK). The processing of the EV proteins for proteomic analysis was performed as indicated in the Supplementary Information. Non-Targeted Proteomics and Targeted Proteomics (Selected Reaction Monitoring). In the discovery phase, quantitative proteomics was performed using an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific) combined with UltiMate 3000 RSLC nano-flow high-performance liquid chromatography (HPLC) (Thermo Scientific). In the validation phase, protein abundance was measured by selected reaction monitoring (SRM) on a TSQVantage triple quadrupole mass spectrometer (Thermo Fisher Scientific) as described. 18–20 SRM data were analyzed using the Skyline software (MacCoss Lab Software, Seattle, WA, USA). 21 Peak area ratios of endogenous light (L) peptides and their heavy (H) isotope-labeled internal standards were used for accurate quantification. Peptide light/heavy ratios were log2 transformed and median-centered. Bioinformatics Analysis of the Proteome With Version Information. To identify biologically relevant molecular networks and pathways in the proteome, upregulated protein IDs were used as input data. The following tools were used for analysis: Ingenuity Pathways Analysis (IPA) (ver 01.13, Qiagen N.V.) for enrichment analysis and upstream analysis, Clue GO/Clue Pedia plugin (ver 2.5.7) from Cytoscape (ver 3.8.0)(5) for enrichment analysis, and KeyMolnet (ver 6.2, KM Data Inc., https://www.km-data.jp) for network analysis. Statistical Analysis. Pearson’s chi-square test or Welch’s t-test were used to compare healthy control subjects and patients with sarcoidosis. Correlations between two parameters were calculated using Spearman’s rank correlation coefficients. Differences were considered statistically significant at P < 0.05. Receiver operating characteristic curves were constructed using the SRM results. Area under the curve (AUC) values were used to evaluate the diagnostic value of each marker. Multiple logistic regression analysis was applied to calculate the predictive probability of a multimarker for the diagnosis of sarcoidosis. Statistical analysis were conducted using JMP Pro v. 14.3.0 (SAS Institute, Cary, NC, USA). Transmission Electron Microscopy. Samples of EVs (10 μg) were adsorbed onto a formvar/carbon-coated nickel grid for 1 h. EVs were fixed with 2% paraformaldehyde and then incubated with the following primary antibodies: anti-CD9 (MM2/57; Thermo Fisher Scientific), anti-CD14 [EPR3653] (ab133335; Abcam, Cambridge, UK), and anti-lipopolysaccharide-binding protein (LBP) polyclonal (PA5-21642; Invitrogen, USA). Immunoreactive EVs were visualized using anti-mouse IgG(H+L) (EMGMHL10) and anti-rabbit IgG(H+L) (EMGFAR10; BBI Solutions, UK) antibodies preabsorbed with 10-nm gold particles. Cell Culture and Lipopolysaccharide Stimulation. RAW 264.7 , a murine macrophage/monocyte lineage cell line, obtained from ATCC (ATCC no.: TIB-71), was cultured in DMEM containing 0.5% FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin. For stimulation with lipopolysaccharide (LPS), RAW 264.7 cells (1×10 6 cells/ml) were seeded into 6-well plates in DMEM with exosome-free FBS and stimulated with 10 ng/ml LPS for 6 h. EVs in the supernatant (650 μl/well) were collected using size exclusion chromatography column. EVs were then concentrated by ultracentrifugation (100,000×g, 4°C, 70 min), dissolved in RIPA buffer, and evaluated by blotting. RAW 264.7 cells were fixed and stained with Diff-Quick stain (Sysmex, Kobe, Japan). Results Proteomics for discovery of novel biomarkers for sarcoidosis. To discover a novel biomarker for sarcoidosis, we performed quantitative high-throughput proteomics using LC-MS/MS, followed by SRM verification (Fig. 1 A). EVs were isolated by SEC from serum samples of control subjects and patients with sarcoidosis (Table S1). 16 The isolation of EVs was confirmed according to the MISEV2018 guidelines. 17 EVs from both groups expressed the EV marker protein CD9 and were similar in shape and size, less than 100 nm (Fig. 1 B). EVs were positive for flotillin-1, CD63, and CD9 and negative for calnexin and haptoglobin (Fig. 1 C). In the nanoparticle tracking analysis, serum EVs from both groups were indistinguishable in size and number (Fig. 1 D, E and F). The non-targeted proteomics analysis of EVs identified 2,292 proteins. Of those, 42 proteins were significantly upregulated in patients with sarcoidosis, and 324 were downregulated (Fig. 2 A, Table 1 , and Table S3). A principal component analysis of EV protein abundance partially separated control subjects from patients with sarcoidosis (Fig. 2 B). Identified proteins were present in the cytoplasm (51%), plasma membrane (29%), and extracellular space (6%; Fig. 2 C). Notably, the Ingenuity Pathway Analysis (IPA) of the protein signature of sarcoidosis EVs revealed factors involved in antigen presentation, immune response, and inflammatory response (Fig. 2 D). Both tumor necrosis factor-α and transforming growth factor β1 pathways ranked highly as upstream signaling factors, suggesting that the protein fingerprints of serum EVs from patients with sarcoidosis reflect not only disease characteristics, but also its pathogenesis (Fig. 2 E). We visualized the network of functions and pathways of the 42 upregulated proteins using the Clue GO/Clue Pedia plugin from Cytoscape. The nominal significant ( P < 0.05) pathways and associated proteins are shown in Fig. 2 F. Protein-protein interaction analyses revealed that the 42 upregulated proteins were clustered in six functional groups including transfer of LPS from the LBP carrier to CD14 (37.5%) and antigen processing cross-presentation (12.5%). Table 1 Significantly Upregulated Proteins in Extracellular Vesicles From Patients With Sarcoidosis Compared to Those From Healthy Subjects Uniprot ID Description Fold change P -value P36222 Chitinase-3-like protein 1 ∞ 0.032 O60704 Protein-tyrosine sulfotransferase 2 ∞ 0.033 Q9NZM1 Myoferlin ∞ 0.004 Q7RTS3 Pancreas transcription factor 1 subunit alpha ∞ 0.024 Q9Y230 RuvB-like 2 ∞ 0.034 Q8TAY7 Protein FAM11 D ∞ 0.047 Q92522 Histone H1x ∞ 0.038 P01040 Cystatin-A ∞ 0.040 P06732 Creatine kinase M-type ∞ 0.049 P98171 Rho GTPase-activating protein 4 ∞ 0.014 P36269 Glutathione hydrolase 5 proenzyme ∞ 0.007 A8MVU1 Putative neutrophil cytosol factor 1C ∞ 0.040 P14598 Neutrophil cytosol factor 1 ∞ 0.040 A6NI72 Putative neutrophil cytosol factor 1B ∞ 0.040 Q8NCG7 Sn1-specific diacylglycerol lipase beta ∞ 0.004 Q8N5C1 Protein FAM26E ∞ 0.048 O15357 Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2 ∞ 0.019 Q75V66 Anoctamin-5 ∞ 0.045 Q14406 Chorionic somatomammotropin hormone-like 1 12.92 0.015 A5YKK6 CCR4-NOT transcription complex subunit 1 11.77 0.027 Q9BYX4 Interferon-induced helicase C domain-containing protein 1 7.52 0.008 Q6A163 Keratin, type I cytoskeletal 39 5.78 0.019 P20963 T-cell surface glycoprotein CD3 zeta chain 5.53 0.030 Q15256 Receptor-type tyrosine-protein phosphatase R 5.22 0.007 O14815 Calpain-9 4.22 0.032 P42574 Caspase-3 4.15 0.001 Q10588 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 2 3.83 0.018 A0A075B6K4 Immunoglobulin lambda variable 3 − 1 3.74 0.013 Q92928 Putative Ras-related protein Rab-1C 3.38 0.048 Q3MII6 TBC1 domain family member 25 3.24 0.025 P24941 Cyclin-dependent kinase 2 2.72 0.039 Q8IU81 Interferon regulatory factor 2-binding protein 1 2.55 0.038 Q96JQ0 Protocadherin-16 2.26 0.044 Q9Y5X9 Endothelial lipase 2.26 0.024 P58166 Inhibin beta E chain 2.26 0.042 P15291 Beta-1,4-galactosyltransferase 1 1.91 0.024 P26572 Alpha-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase 1.89 0.012 P18428 Lipopolysaccharide-binding protein 1.79 0.004 P02766 Transthyretin 1.78 0.027 P08571 Monocyte differentiation antigen CD14 1.71 0.019 P04003 C4b-binding protein alpha chain 1.67 0.041 Q9UK55 Protein Z-dependent protease inhibitor 1.58 0.019 Subsequently, we selected 25 proteins, taking into consideration of previous reports, in the validation cohort using SRM (Fig. 3 A, Table S2, and Table S4). Targeted proteomics enables the efficient and specific verification of biomarker candidates without requiring antibodies and is, thus, a powerful tool for biomarker validation. 14 , 15 , 20 Among 25 biomarker candidates, the expression levels of LBP and the monocyte differentiation antigen CD14 were markedly elevated in patients with sarcoidosis (Fig. 3 B). Consistent with these findings, LPS signaling is highly ranked in the IPA upstream analysis (Fig. 1 F), suggesting that these proteins are involved in sarcoidosis pathogenesis. Although serum levels of CD14 were elevated in patients with sarcoidosis, 22 we did not observe this difference in free serum levels of both CD14 and LBP, in contrast to the EV levels, measured by ELISA (Fig. 3 C). Analyses in KeyMolnet revealed that both CD14 and LBP were closely linked upstream and downstream to key molecules related to granuloma formation (Fig. 3 D). The presence and upregulation of these proteins were subsequently confirmed by western blotting (Fig. 4 A) and immunoelectron microscopy (Fig. 4 B), respectively. Furthermore, we confirmed the presence of CD14 and LBP in serum EVs isolated by both ultracentrifugation and the phosphatidylserine affinity method (data not shown). The expression of CD14 and LBP in vivo and in vitro. We also assessed the expression levels of these proteins in vivo by immunostaining of tissue samples. CD14 and LBP were weakly expressed in mononuclear cells in the lung and lymph nodes of healthy control subjects. By contrast, the expression of CD14 and LBP was strikingly increased in granulomatous lesions, especially in multinucleated giant cells (MGCs) and surrounding mononuclear cells (Fig. 5 A and B). MGCs, the hallmarks of granuloma, are generated by monocytes in response to various stimuli, including LPS. 23 To observe the dynamic changes of CD14 and LBP during MGC formation in vitro , we stimulated RAW 264.7 cells with LPS. Consistent with the in vivo results showing that CD14 was upregulated in macrophages, CD14 levels in EVs from cell culture supernatants were significantly elevated (Fig. 5 C). Moreover, the LBP levels in EVs were also elevated, although these levels were unaltered in LPS-stimulated macrophages. Taken together, both CD14 and LBP were upregulated in the process of granuloma formation in vitro and in vivo . Moreover, given that in macrophages, the tetraspanin CD9 is closely colocalized with CD14, thereby inhibiting LPS-induced signaling, 24 and that CD9 is strongly involved in macrophage fusion into MGCs, 25 CD14 in macrophages and EVs might actively participate in the pathogenesis of granulomatous diseases (Fig. 5 D). Diagnostic potential of CD14 and LBP in serum EVs for sarcoidosis. To further evaluate the diagnostic potential of the identified biomarkers, we analyzed their AUC values. The AUC values for CD14 and LBP were 0.81 and 0.84, respectively (Fig. 6 A). Considering that most of the previously reported biomarkers appear to have some limitations in terms of sensitivity and specificity, it is intriguing to investigate different combinations. By combining the novel biomarkers with ACE, sIL-2R, and both, the AUC values could be increased further to 0.96, 0.96, and 0.98, respectively (Fig. 6 B and Figure S1). Although LBP was weakly correlated with clinical parameters such as ACE (r: 0.45; P < 0.01), Krebs von den Lungen-6 (r: 0.35; P < 0.05), and C-reactive protein (r: 0.60; P < 0.01), the level of CD14 was not correlated with any parameters including monocyte number (Table S5). Although 59.6% of patients were ACE-negative, as many as 45.1% and 48.3% of ACE-negative patients could be further diagnosed by CD14 and LBP, respectively. Similarly, 46.8% of patients were sIL-2R-negative, and 40.8% and 45.5% of sIL-2R-negative patients could be further diagnosed by CD14 and LBP, respectively (Fig. 6 C). These findings suggest that our novel biomarkers possess distinct properties in comparison to conventional biomarkers. To further assess the potential of the novel biomarkers to monitor treatment response, we compared their levels in three patients with sarcoidosis before and after steroid administration. The CD14 and LBP levels in serum EVs of these patients decreased remarkably after therapy (Figure S2), suggesting that they could also serve as therapeutic biomarkers. Discussion Because both ACE and sIL-2R have several limitations such as insufficient sensitivity and specificity, there is an urgent need to develop better biomarkers for sarcoidosis. Although peripheral blood may be regarded as an ideal source of biomarkers, MS-based serum proteomics is exceptionally challenging due to its broad dynamic range with abundant proteins such as albumin. 8 To overcome these problems, we focused on serum EVs and identified new biomarkers for sarcoidosis by employing both non-targeted, label-free proteomics and targeted proteomics. Although the importance of EVs, especially exosomes, has been increasingly demonstrated in cancer and immune diseases, 11,26−28 proteomics-based discovery of biomarkers for inflammatory diseases is in its infancy. Considering that isolating EVs can reduce the complexity of serum protein analysis and that EVs reflect both immune response and granuloma formation, serum EVs could become an ideal source of novel biomarkers, providing liquid biopsy samples for personalized medicine approaches. While EVs have advantages such as stability and accessibility, they also have disadvantages such as difficulties in both isolation and quantitation. 17 Although ultracentrifugation is widely regarded as the gold standard, SEC-based EV isolation yields much higher purity through MS analysis than ultracentrifugation. 9 For this reason, we isolated EVs using SEC instead of ultracentrifugation. Previous studies leveraging MS-based proteomics to examine bronchoalveolar lavage fluid, alveolar macrophages, plasma, and EVs in sarcoidosis identified several differentially expressed proteins, including pulmonary surfactant A2, vitamin D-binding protein, and amyloid P. 7 Regarding serum EVs, the vitamin D-binding protein levels in EVs derived from plasma samples or bronchoalveolar lavage fluid are elevated in patients with sarcoidosis. 29 Although we detected a higher number of proteins, we could not confirm this finding, which was presumably caused by differences in isolation and MS methods. The verification of many biomarker candidates by conventional immunoblotting would have been time-consuming and less specific; the targeted proteomics approach allowed us to verify biomarker candidates specifically and efficiently without using any antibody. Combining non-targeted and targeted proteomics approaches, we identified CD14 and LBP as potential biomarkers of sarcoidosis, followed by their confirmation in western blots and immunohistochemical tissue staining. CD14 is a myeloid differentiation antigen mainly expressed on monocytes and macrophages. Both CD14 and LBP are required for recognition of LPS by Toll-like receptor (TLR)4. 30 LPS is a major component of the gram-negative bacterial cell wall and a typical example of a pathogen-associated molecular pattern. To facilitate microbial recognition and to amplify cellular responses, certain TLRs require additional proteins, such as LBP, CD14, CD36, and high mobility group box 1. As several infectious organisms like viruses, Mycobacterium spp. , and Propionibacterium acnes have been implicated in the pathogenesis of sarcoidosis, 13132 the newly identified biomarkers CD14 and LBP may support an infectious etiology of sarcoidosis. Previous studies also showed that the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway was more activated in patients with sarcoidosis, and particularly STAT1 and STAT3 played a role in granuloma formation. 33 – 35 Some EV proteins detected in the discovery phase were correlated with CD14 and LBP through TLRs and the STAT pathway (Fig. 3 D). Given that CD14 and LBP were upregulated in the process of granuloma formation in vitro and in vivo (Fig. 5 ) and that CD14-positive monocytes promote MGC formation, 36 both CD14 and LBP in serum EVs could be involved in the pathophysiology of sarcoidosis. Considering the limited sensitivity and specificity of the conventional biomarkers ACE and sIL-2R and the positive correlations between various biomarkers, different combinations might be warranted. Our novel biomarkers possess both distinct properties and higher diagnostic potential in comparison to conventional biomarkers. Although LBP levels were weakly correlated with some clinical parameters, the CD14 levels were not correlated with any parameter. Several studies have demonstrated that a combination of different biomarkers increases both sensitivity and specificity. 37 Hence, a combination of our novel biomarkers with either ACE or sIL-2R may strikingly improve their diagnostic potential in patients with sarcoidosis. Despite the great advantages of SRM to verify many biomarkers efficiently, our approach has a couple of limitations. First, we examined our new biomarkers regarding organ specificity and disease severity but observed no significant correlation (Figure S3). This might have been caused by the relatively small sample size, thus further studies are warranted. Moreover, the expression levels of the novel biomarkers were not increased in inflammatory lung disease, including COPD, bronchial asthma, and lung fibrosis (data not shown), implying their disease specificity. Next, given that genome, transcriptome, and metabolome networks are also important for precision medicine, it would be intriguing to aggregate our data by integrating multiple omics approaches. 6 , 38 A system biology platform using clinical data, omics, and bioinformatics may lead to a better understanding of sarcoidosis. 6 Declarations Acknowledgements We thank Hiroko Omori and Rie Taniguchi for their technical support. Author contributions Y.F., Y.T., and A.K. contributed to study design, data analysis, data interpretation, and manuscript writing. K. U. performed label-free proteomics and analyzed data. R.N., M.I., J. A., and T.T. performed targeted proteomics and analyzed data. Y. N. and M. I. contributed to bioinformatics analysis. And all authors reviewed the manuscript. Conflict of Interest There are no conflicts of interest to declare. Funding This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP18H05282 to A.K., JP19K08650 to Y.T., JP18K15924 to T.K., and JP19K17636 to Y.F.), a grant from the Uehara Memorial Foundation, and a grant from the Japanese Respiratory Foundation (to Y.T.). The funding sources had no involvement in the study design or conduct; the collection, analysis, and interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. References Hunninghake, G. W. et al. Statement on sarcoidosis. American Journal of Respiratory and Critical Care Medicine , 160 , 736–755 (1999). Spagnolo, P. et al. Pulmonary sarcoidosis. Lancet Respir. Med , 6 , 389–402 (2018). 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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-793224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":46428156,"identity":"11afaa81-a96e-4673-9348-9e3be05fc9e7","order_by":0,"name":"Yu Futami","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Futami","suffix":""},{"id":46428157,"identity":"f06c3a8a-9138-4f67-83da-a21e8a619f67","order_by":1,"name":"Yoshito 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Health and Nutrition","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Adachi","suffix":""},{"id":46428202,"identity":"ba40ba8b-ee63-453a-a5a2-ab2cb97f2455","order_by":27,"name":"Takeshi Tomonaga","email":"","orcid":"","institution":"National Institute of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Tomonaga","suffix":""},{"id":46428203,"identity":"4a0a4a4c-2d5d-4dd9-a595-07a35db2fe64","order_by":28,"name":"Koji Ueda","email":"","orcid":"","institution":"Cancer Precision Medicine Center, Japanese Foundation for Cancer Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Koji","middleName":"","lastName":"Ueda","suffix":""},{"id":46428204,"identity":"836e9996-f9a8-4ae5-b9aa-e9836a25107f","order_by":29,"name":"Atsushi Kumanogoh","email":"","orcid":"","institution":"Osaka University Graduate School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Kumanogoh","suffix":""}],"badges":[],"createdAt":"2021-08-08 20:59:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-793224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-793224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12577344,"identity":"3449d676-d0d9-42b0-8f60-27d074edf860","added_by":"auto","created_at":"2021-08-19 13:47:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464996,"visible":true,"origin":"","legend":"Strategy for discovery of novel biomarkers for sarcoidosis. A, In the discovery phase, serum EVs from patients with sarcoidosis and healthy control subjects were isolated using SEC and analyzed. In the validation phase, biomarker candidates were quantified using targeted proteomics SRM. Finally, the biomarkers were confirmed by immunoblotting. B, Transmission electron microscopy images of serum EVs from a healthy control subject and a patient with sarcoidosis (CD9 immunogold labeling). C, Comparison of serum EVs with serum and A549 cell lysates. Immunoblots of flotillin-1, CD63, CD9, haptoglobin, and calnexin are shown. D, Representative distribution curves showing the particle sizes of serum EVs from a healthy control subject and a patient with sarcoidosis, analyzed using NanoSight. E and F, The mean diameters and numbers of serum EVs from patients with sarcoidosis and healthy control subjects, analyzed using NanoSight, are not significantly different. Error bars represent means ± standard deviations. ESI = electrospray ionization; EV = extracellular vesicle; HPLC = high-performance liquid chromatography; N.S. = not significant; SEC = size exclusion chromatography; SRM = selected reaction monitoring.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/2d73b0761ee7d29fadf5ee28.jpg"},{"id":12577340,"identity":"27b6d4e8-ded5-4572-80ae-ca6c6cbac120","added_by":"auto","created_at":"2021-08-19 13:47:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":471577,"visible":true,"origin":"","legend":"The proteomic profile of serum EVs reflects sarcoidosis characteristics and pathogenesis. A, A volcano plot of all identified 2,292 serum EV proteins by non-targeted proteomic analyses in patients with sarcoidosis and healthy control subjects. A total of 42 proteins were significantly upregulated, and 324 proteins were significantly downregulated in EVs of patients with sarcoidosis compared to those of healthy control subjects. Red line, P-value = 0.05. B, Healthy control subjects and patients with sarcoidosis are separated by principal component analysis using all EV proteins identified by non-targeted proteomics analysis. C, Localization of all identified proteins in the Ingenuity Pathway Analysis. D and E, Pathways (D) and upstream molecules (E) determined in the Ingenuity Pathway Analysis to be over- (×1.5 or more) or under- (×0.8 or less) -represented in non-targeted proteomic analyses of serum EVs from patients with sarcoidosis compared to those from healthy control subjects. Red line, P-value = 0.05. F, A protein-protein network constructed with enrichment analysis based on the Reactome database reveals the interactions between the 42 upregulated proteins. EV = extracellular vesicle.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/b6516bb35d604b084bedff00.jpg"},{"id":12577338,"identity":"2c8c470f-e526-437e-9078-84574481cab2","added_by":"auto","created_at":"2021-08-19 13:47:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464003,"visible":true,"origin":"","legend":"Validation of the selected reaction monitoring results. A, A flow chart of this study and representative SRM Figure. The quantitation of each endogenous protein in serum EVs was performed by comparing the brown area of an endogenous peptide (Light) to the corresponding synthetic peptide (Heavy). For further information, please see section Targeted Proteomics (Selected Reaction Monitoring) in the Supplementary information. B, CD14, LBP and CD9 levels in serum EVs are upregulated in patients with sarcoidosis compared to healthy control subjects, as determined by targeted proteomics (SRM). C, No significant difference in the serum levels of soluble CD14 and LBP can be detected between healthy control subjects and patients with sarcoidosis. D, A highly complex network of targets with significant relationships, generated by KeyMolnet. Red ellipses: upregulated proteins. Blue ellipses: downregulated proteins. ESI = electrospray ionization; EV = extracellular vesicle; HPLC = high-performance liquid chromatography; LBP = lipopolysaccharide-binding protein; N.S. = not significant; SRM = selected reaction monitoring.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/686f525608d35692d12742f0.jpg"},{"id":12578001,"identity":"bda360de-af6e-4e0d-b89c-c2375359b9b3","added_by":"auto","created_at":"2021-08-19 13:53:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":322752,"visible":true,"origin":"","legend":"Expression of CD14 and LBP in serum EVs. A, A representative immunoblot comparing CD14 and LBP in serum EVs of healthy control subjects and patients with sarcoidosis. B, Transmission electron microscopy images showing the CD14 and LBP expression in EVs labeled with primary antibody and anti-IgG gold-labeled secondary antibody. Data are representative of three independent studies with similar results. EV = extracellular vesicle; LBP = lipopolysaccharide-binding protein.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/742d9aa90777d5c91f0a8b5f.jpg"},{"id":12577651,"identity":"eae8939b-479c-4466-993f-02bef1b3af05","added_by":"auto","created_at":"2021-08-19 13:50:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":798125,"visible":true,"origin":"","legend":"Expression of CD14 and LBP in vivo and in vitro. A and B, Immunohistochemical stainings of the lung and lymph node in a patient with sarcoidosis. CD14 (A) and LBP (B) are highly expressed in the granuloma (black arrows). These markers are also expressed in macrophages present in the lung and lymph node of the healthy control subject. C, Dynamic CD14 and LBP changes in EVs during macrophage multinucleation. Data are representative of three independent studies with similar results. D, The upregulation of CD14 in serum EVs is associated with granuloma formation in patients with sarcoidosis. EV = extracellular vesicle; LBP = lipopolysaccharide-binding protein.","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/28f5d9b4a1280b018a9fb186.jpg"},{"id":12577650,"identity":"4154f88f-8f4b-4742-8649-c72655c31799","added_by":"auto","created_at":"2021-08-19 13:50:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":504529,"visible":true,"origin":"","legend":"Diagnostic accuracy of the novel biomarkers for sarcoidosis. A, ROC curves of CD14 and LBP levels in serum EVs to diagnose sarcoidosis. B, ROC curves of CD14 and LBP combined with conventional biomarkers. C, CD14 and LBP in serum EVs can partially identify sarcoidosis patients without elevation of serum ACE or sIL-2R level. ACE = angiotensin-converting enzyme; AUC = area under the curve; EV = extracellular vesicle; LBP = lipopolysaccharide-binding protein; ROC = receiver operating characteristics; sIL-2R = soluble interleukin-2 receptor.","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/edaa17938f686cd481972496.jpg"},{"id":15122395,"identity":"3fe03a19-0337-4dab-9886-1c118fa239a5","added_by":"auto","created_at":"2021-11-02 06:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1191801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/5166185e-9ae8-4c12-85b0-6742be42f1ce.pdf"},{"id":12577648,"identity":"3239d3ec-b1ca-4019-9331-5d84b93191b7","added_by":"auto","created_at":"2021-08-19 13:50:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2333585,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/2d493787659b63fc117b59d7.pdf"},{"id":12578002,"identity":"180bedb0-4175-4019-810e-f0202e42c054","added_by":"auto","created_at":"2021-08-19 13:53:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":134599,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-793224/v1/826ac963cedf3ce8f4422ba6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCD14 and LBP as Novel Biomarkers for Sarcoidosis by Proteomics of Extracellular Vesicles\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSarcoidosis is a systemic granulomatous disease associated with T-lymphocyte and macrophage activation and migration of these cells into affected organs. There is heterogeneity in disease manifestation, severity, and clinical course.\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e To better understand this disease, we need effective biomarkers for diagnosis and prognosis. Unfortunately, the ideal biomarker for sarcoidosis has not yet been discovered. Among identified serum biomarkers, angiotensin-converting enzyme (ACE) and soluble interleukin-2 receptor\u0026nbsp;(sIL-2R) are\u0026nbsp;most relevant but lack\u0026nbsp;sensitivity and specificity.\u003csup\u003e4\u003c/sup\u003e Roughly 30\u0026ndash;80% of patients with sarcoidosis have increased ACE levels;\u0026nbsp;sensitivity ranges between 22 and 86% and specificity between 54 and 95%.\u003csup\u003e5\u003c/sup\u003e On the other hand, sIL-2R levels are proposed as a marker of disease activity in sarcoidosis. But elevated sIL-2R levels are not specific for sarcoidosis and can be found in other granulomatous diseases, hematological malignancies, and various autoimmune disorders.\u003csup\u003e4\u003c/sup\u003e The use of sIL-2R as a diagnostic marker for sarcoidosis remains a matter of debate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn light of progress in mass spectrometry (MS) technology, a great deal of attention has been paid to omics approaches for the study of heterogeneous diseases.\u003csup\u003e6\u003c/sup\u003e Although advances in proteomics have facilitated the discovery of protein biomarkers,\u0026nbsp;the application of proteomics, which measures features that are closer to the phenotype, has been limited in comparison with genomics and transcriptomics.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAlthough\u0026nbsp;serum is regarded as an ideal source of marker molecules\u0026nbsp;due to high reproducibility and minimal invasiveness, the masking of small amounts of key proteins is unavoidable because of the wide dynamic range.\u003csup\u003e8\u003c/sup\u003e To solve this problem, we focused in this study on extracellular vesicles (EVs) in the serum. EVs are increasingly appreciated as important carriers of biologic cargo that play key roles in intercellular communication by transferring contents such as mRNA, microRNA, and proteins between neighboring cells. EV constituents play a variety of pathological roles in diseases including malignancies, inflammatory disorders, and infections.\u003csup\u003e7,9,10\u003c/sup\u003e From these backgrounds, EV sandwich ELISA system has been developed to diagnose lung cancer \u0026nbsp;for clinical usage.\u003csup\u003e11\u003c/sup\u003e Although ultracentrifugation has been widely regarded as the gold standard to isolate EVs, size exclusion chromatography (SEC)-based EV isolation yields much higher purity through MS analysis than ultracentrifugation.\u003csup\u003e9\u003c/sup\u003e Despite progress in exosome research, no standard method exists for providing exact quantitative and qualitative analyses of exosomes.\u003csup\u003e12\u003c/sup\u003e Some methods have been developed for the isolation of exosomes, including ultracentrifugation, affinity-based methods, and SEC. Some comparative studies showed that the isolation of EVs by SEC retains the biophysical properties of EVs, resulting in a higher yield.\u003csup\u003e13\u003c/sup\u003e Tandem mass tag-based non-targeted proteomics analysis\u0026nbsp;of serum EVs isolated by ultracentrifugation revealed that fibulin-3 in EVs may serve as a novel diagnostic biomarker for\u0026nbsp;chronic obstructive pulmonary disease and might be closely related to its\u0026nbsp;pathophysiology.\u003csup\u003e14\u003c/sup\u003e With this background, we isolated serum EVs using SEC, which can detect less abundant serum proteins, thereby applying label-free proteomics strategies to discover novel biomarkers for sarcoidosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eParticipants were recruited for a discovery and a validation phase from the database of patients diagnosed with sarcoidosis at Osaka University Hospital (2013\u0026ndash;2019). All patients with sarcoidosis were diagnosed according to the international ATS/ERS/WASOG criteria.\u003csup\u003e1\u003c/sup\u003e Serum samples were separated by centrifugation and stored frozen at \u0026minus;80\u0026deg;C for further analyses. To sustain the sample quality, the procedure such as freezing and thawing of serum was avoided as much as possible. For the discovery cohort, 5 control subjects and 7 patients with sarcoidosis were selected (Table S1). Patients with sarcoidosis were untreated when the samples were collected. The samples were subjected to quantitative high-throughput proteomics using liquid chromatography-mass spectrometry (LC-MS/MS). The validation cohort included 10 control subjects and 46 patients with sarcoidosis. For validation, candidate proteins identified in the discovery cohort were quantified by targeted proteomics using MS (selected reaction monitoring [SRM]) as described previously.\u003csup\u003e15\u003c/sup\u003e The clinical information of participants is shown in Table S2. This study was performed according to the guidelines described in the Declaration of Helsinki for medical research involving human subjects. \u0026nbsp;This study protocol was approved by the Ethics Committee of Osaka University (no. 17148), and all study participants gave written informed consent.\u003c/p\u003e\n\u003ch2\u003eEV Isolation. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSerum EVs were isolated by size-exclusion chromatography, using an EV Second L70 SEC column (GL Science, Tokyo, Japan).\u003csup\u003e16\u003c/sup\u003e After blocking with fetal bovine serum (FBS) and washing with phosphate-buffered saline (PBS), 400 \u0026mu;l of serum was loaded onto the column and eluted with PBS. The first 300 \u0026mu;l of the eluate was discarded; thereafter, the eluate was collected in three fractions of 100 \u0026mu;l each. The isolation of EVs was confirmed according to the guidelines delineated in Minimal information for studies of extracellular vesicles 2018 (MISEV2018).\u003csup\u003e17\u003c/sup\u003e Size distributions and numbers were confirmed by NanoSight nanoparticle tracking analysis (Malvern Instruments, Malvern, UK). The processing of the EV proteins for proteomic analysis was performed as indicated in the Supplementary Information.\u003c/p\u003e\n\u003ch2\u003eNon-Targeted Proteomics and Targeted Proteomics (Selected Reaction Monitoring).\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn the discovery phase, quantitative proteomics was performed using an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific) combined with UltiMate 3000 RSLC nano-flow high-performance liquid chromatography (HPLC) (Thermo Scientific). In the validation phase, protein abundance was measured by selected reaction monitoring (SRM) on a TSQVantage triple quadrupole mass spectrometer (Thermo Fisher Scientific) as described.\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e SRM data were analyzed using the Skyline software (MacCoss Lab Software, Seattle, WA, USA).\u003csup\u003e21\u003c/sup\u003e Peak area ratios of endogenous light (L) peptides and their heavy (H) isotope-labeled internal standards were used for accurate quantification. Peptide light/heavy ratios were log2 transformed and median-centered.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eBioinformatics Analysis of the Proteome With Version Information. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo identify biologically relevant molecular networks and pathways in the proteome, upregulated protein IDs were used as input data. The following tools were used for analysis: Ingenuity Pathways Analysis (IPA) (ver 01.13, Qiagen N.V.) for enrichment analysis and upstream analysis, Clue GO/Clue Pedia plugin (ver 2.5.7) from Cytoscape (ver 3.8.0)(5) for enrichment analysis, and KeyMolnet (ver 6.2, KM Data Inc., https://www.km-data.jp) for network analysis.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePearson\u0026rsquo;s chi-square test or Welch\u0026rsquo;s t-test were used to compare healthy control subjects and patients with sarcoidosis. Correlations between two parameters were calculated using Spearman\u0026rsquo;s rank correlation coefficients. Differences were considered statistically significant at P \u0026lt; 0.05. Receiver operating characteristic curves were constructed using the SRM results. Area under the curve (AUC) values were used to evaluate the diagnostic value of each marker. Multiple logistic regression analysis was applied to calculate the predictive probability of a multimarker for the diagnosis of sarcoidosis. Statistical analysis were conducted using JMP Pro v. 14.3.0 (SAS Institute, Cary, NC, USA).\u003c/p\u003e\n\u003ch2\u003eTransmission Electron Microscopy. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSamples of EVs (10 \u0026mu;g) were adsorbed onto a formvar/carbon-coated nickel grid for 1 h. EVs were fixed with 2% paraformaldehyde and then incubated with the following primary antibodies: anti-CD9 (MM2/57; Thermo Fisher Scientific), anti-CD14 [EPR3653] (ab133335; Abcam, Cambridge, UK), and anti-lipopolysaccharide-binding protein (LBP) polyclonal (PA5-21642; Invitrogen, USA). Immunoreactive EVs were visualized using anti-mouse IgG(H+L) (EMGMHL10) and anti-rabbit IgG(H+L) (EMGFAR10; BBI Solutions, UK) antibodies preabsorbed with 10-nm gold particles.\u003c/p\u003e\n\u003ch2\u003eCell Culture and Lipopolysaccharide Stimulation. \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eRAW 264.7 , a murine macrophage/monocyte lineage cell line, obtained from ATCC (ATCC no.: TIB-71), was cultured in DMEM containing 0.5% FBS, 100 U/ml penicillin, and 100 \u0026mu;g/ml streptomycin. For stimulation with lipopolysaccharide (LPS), RAW 264.7 cells (1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/ml) were seeded into 6-well plates in DMEM with exosome-free FBS and stimulated with 10 ng/ml LPS for 6 h. EVs in the supernatant (650 \u0026mu;l/well) were collected using size exclusion chromatography column. EVs were then concentrated by ultracentrifugation (100,000\u0026times;g, 4\u0026deg;C, 70 min), dissolved in RIPA buffer, and evaluated by blotting. RAW 264.7 cells were fixed and stained with Diff-Quick stain (Sysmex, Kobe, Japan).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eProteomics for discovery of novel biomarkers for sarcoidosis.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo discover a novel biomarker for sarcoidosis, we performed quantitative high-throughput proteomics using LC-MS/MS, followed by SRM verification (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). EVs were isolated by SEC from serum samples of control subjects and patients with sarcoidosis (Table S1).\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The isolation of EVs was confirmed according to the MISEV2018 guidelines.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e EVs from both groups expressed the EV marker protein CD9 and were similar in shape and size, less than 100 nm (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). EVs were positive for flotillin-1, CD63, and CD9 and negative for calnexin and haptoglobin (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). In the nanoparticle tracking analysis, serum EVs from both groups were indistinguishable in size and number (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD, E and F).\u003c/p\u003e\n\u003cp\u003eThe non-targeted proteomics analysis of EVs identified 2,292 proteins. Of those, 42 proteins were significantly upregulated in patients with sarcoidosis, and 324 were downregulated (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and Table S3). A principal component analysis of EV protein abundance partially separated control subjects from patients with sarcoidosis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Identified proteins were present in the cytoplasm (51%), plasma membrane (29%), and extracellular space (6%; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Notably, the Ingenuity Pathway Analysis (IPA) of the protein signature of sarcoidosis EVs revealed factors involved in antigen presentation, immune response, and inflammatory response (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Both tumor necrosis factor-\u0026alpha; and transforming growth factor \u0026beta;1 pathways ranked highly as upstream signaling factors, suggesting that the protein fingerprints of serum EVs from patients with sarcoidosis reflect not only disease characteristics, but also its pathogenesis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). We visualized the network of functions and pathways of the 42 upregulated proteins using the Clue GO/Clue Pedia plugin from Cytoscape. The nominal significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) pathways and associated proteins are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF. Protein-protein interaction analyses revealed that the 42 upregulated proteins were clustered in six functional groups including transfer of LPS from the LBP carrier to CD14 (37.5%) and antigen processing cross-presentation (12.5%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e \u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSignificantly Upregulated Proteins in Extracellular Vesicles From Patients With Sarcoidosis Compared to Those From Healthy Subjects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUniprot ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFold change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP36222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChitinase-3-like protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO60704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein-tyrosine sulfotransferase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9NZM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyoferlin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ7RTS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreas transcription factor 1 subunit alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9Y230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRuvB-like 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ8TAY7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein FAM11 D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ92522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistone H1x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP01040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystatin-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP06732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatine kinase M-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP98171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRho GTPase-activating protein 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP36269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutathione hydrolase 5 proenzyme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA8MVU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePutative neutrophil cytosol factor 1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP14598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil cytosol factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6NI72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePutative neutrophil cytosol factor 1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ8NCG7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSn1-specific diacylglycerol lipase beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ8N5C1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein FAM26E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO15357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ75V66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnoctamin-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ14406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChorionic somatomammotropin hormone-like 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5YKK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCR4-NOT transcription complex subunit 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9BYX4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterferon-induced helicase C domain-containing protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ6A163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKeratin, type I cytoskeletal 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP20963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-cell surface glycoprotein CD3 zeta chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ15256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReceptor-type tyrosine-protein phosphatase R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO14815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalpain-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP42574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaspase-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ10588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA0A075B6K4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmunoglobulin lambda variable 3\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ92928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePutative Ras-related protein Rab-1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3MII6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBC1 domain family member 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP24941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCyclin-dependent kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ8IU81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterferon regulatory factor 2-binding protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ96JQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtocadherin-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9Y5X9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndothelial lipase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP58166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInhibin beta E chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP15291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-1,4-galactosyltransferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP26572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlpha-1,3-mannosyl-glycoprotein\u003c/p\u003e\n \u003cp\u003e2-beta-N-acetylglucosaminyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP18428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipopolysaccharide-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP02766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransthyretin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP08571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte differentiation antigen CD14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP04003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4b-binding protein alpha chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9UK55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein Z-dependent protease inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSubsequently, we selected 25 proteins, taking into consideration of previous reports, in the validation cohort using SRM (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table S2, and Table S4). Targeted proteomics enables the efficient and specific verification of biomarker candidates without requiring antibodies and is, thus, a powerful tool for biomarker validation.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Among 25 biomarker candidates, the expression levels of LBP and the monocyte differentiation antigen CD14 were markedly elevated in patients with sarcoidosis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Consistent with these findings, LPS signaling is highly ranked in the IPA upstream analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF), suggesting that these proteins are involved in sarcoidosis pathogenesis. Although serum levels of CD14 were elevated in patients with sarcoidosis,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e we did not observe this difference in free serum levels of both CD14 and LBP, in contrast to the EV levels, measured by ELISA (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Analyses in KeyMolnet revealed that both CD14 and LBP were closely linked upstream and downstream to key molecules related to granuloma formation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003eThe presence and upregulation of these proteins were subsequently confirmed by western blotting (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) and immunoelectron microscopy (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), respectively. Furthermore, we confirmed the presence of CD14 and LBP in serum EVs isolated by both ultracentrifugation and the phosphatidylserine affinity method (data not shown).\u003c/p\u003e\n\u003ch2\u003eThe expression of CD14 and LBP in vivo and in vitro.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe also assessed the expression levels of these proteins \u003cem\u003ein vivo\u003c/em\u003e by immunostaining of tissue samples. CD14 and LBP were weakly expressed in mononuclear cells in the lung and lymph nodes of healthy control subjects. By contrast, the expression of CD14 and LBP was strikingly increased in granulomatous lesions, especially in multinucleated giant cells (MGCs) and surrounding mononuclear cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). MGCs, the hallmarks of granuloma, are generated by monocytes in response to various stimuli, including LPS.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e To observe the dynamic changes of CD14 and LBP during MGC formation \u003cem\u003ein vitro\u003c/em\u003e, we stimulated RAW 264.7 cells with LPS. Consistent with the \u003cem\u003ein vivo\u003c/em\u003e results showing that CD14 was upregulated in macrophages, CD14 levels in EVs from cell culture supernatants were significantly elevated (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, the LBP levels in EVs were also elevated, although these levels were unaltered in LPS-stimulated macrophages. Taken together, both CD14 and LBP were upregulated in the process of granuloma formation \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Moreover, given that in macrophages, the tetraspanin CD9 is closely colocalized with CD14, thereby inhibiting LPS-induced signaling,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and that CD9 is strongly involved in macrophage fusion into MGCs,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e CD14 in macrophages and EVs might actively participate in the pathogenesis of granulomatous diseases (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\n\u003ch2\u003eDiagnostic potential of CD14 and LBP in serum EVs for sarcoidosis.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo further evaluate the diagnostic potential of the identified biomarkers, we analyzed their AUC values. The AUC values for CD14 and LBP were 0.81 and 0.84, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Considering that most of the previously reported biomarkers appear to have some limitations in terms of sensitivity and specificity, it is intriguing to investigate different combinations. By combining the novel biomarkers with ACE, sIL-2R, and both, the AUC values could be increased further to 0.96, 0.96, and 0.98, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB and Figure S1). Although LBP was weakly correlated with clinical parameters such as ACE (r: 0.45; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Krebs von den Lungen-6 (r: 0.35; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and C-reactive protein (r: 0.60; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), the level of CD14 was not correlated with any parameters including monocyte number (Table S5). Although 59.6% of patients were ACE-negative, as many as 45.1% and 48.3% of ACE-negative patients could be further diagnosed by CD14 and LBP, respectively. Similarly, 46.8% of patients were sIL-2R-negative, and 40.8% and 45.5% of sIL-2R-negative patients could be further diagnosed by CD14 and LBP, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). These findings suggest that our novel biomarkers possess distinct properties in comparison to conventional biomarkers.\u003c/p\u003e\n\u003cp\u003eTo further assess the potential of the novel biomarkers to monitor treatment response, we compared their levels in three patients with sarcoidosis before and after steroid administration. The CD14 and LBP levels in serum EVs of these patients decreased remarkably after therapy (Figure S2), suggesting that they could also serve as therapeutic biomarkers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBecause both ACE and sIL-2R have several limitations such as insufficient sensitivity and specificity, there is an urgent need to develop better biomarkers for sarcoidosis. Although peripheral blood may be regarded as an ideal source of biomarkers, MS-based serum proteomics is exceptionally challenging due to its broad dynamic range with abundant proteins such as albumin.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e To overcome these problems, we focused on serum EVs and identified new biomarkers for sarcoidosis by employing both non-targeted, label-free proteomics and targeted proteomics. Although the importance of EVs, especially exosomes, has been increasingly demonstrated in cancer and immune diseases,\u003csup\u003e11,26\u0026minus;28\u003c/sup\u003e proteomics-based discovery of biomarkers for inflammatory diseases is in its infancy. Considering that isolating EVs can reduce the complexity of serum protein analysis and that EVs reflect both immune response and granuloma formation, serum EVs could become an ideal source of novel biomarkers, providing liquid biopsy samples for personalized medicine approaches. While EVs have advantages such as stability and accessibility, they also have disadvantages such as difficulties in both isolation and quantitation.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Although ultracentrifugation is widely regarded as the gold standard, SEC-based EV isolation yields much higher purity through MS analysis than ultracentrifugation.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e For this reason, we isolated EVs using SEC instead of ultracentrifugation. Previous studies leveraging MS-based proteomics to examine bronchoalveolar lavage fluid, alveolar macrophages, plasma, and EVs in sarcoidosis identified several differentially expressed proteins, including pulmonary surfactant A2, vitamin D-binding protein, and amyloid P.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Regarding serum EVs, the vitamin D-binding protein levels in EVs derived from plasma samples or bronchoalveolar lavage fluid are elevated in patients with sarcoidosis.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Although we detected a higher number of proteins, we could not confirm this finding, which was presumably caused by differences in isolation and MS methods.\u003c/p\u003e \u003cp\u003eThe verification of many biomarker candidates by conventional immunoblotting would have been time-consuming and less specific; the targeted proteomics approach allowed us to verify biomarker candidates specifically and efficiently without using any antibody. Combining non-targeted and targeted proteomics approaches, we identified CD14 and LBP as potential biomarkers of sarcoidosis, followed by their confirmation in western blots and immunohistochemical tissue staining.\u003c/p\u003e \u003cp\u003eCD14 is a myeloid differentiation antigen mainly expressed on monocytes and macrophages. Both CD14 and LBP are required for recognition of LPS by Toll-like receptor (TLR)4.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e LPS is a major component of the gram-negative bacterial cell wall and a typical example of a pathogen-associated molecular pattern. To facilitate microbial recognition and to amplify cellular responses, certain TLRs require additional proteins, such as LBP, CD14, CD36, and high mobility group box 1. As several infectious organisms like viruses, \u003cem\u003eMycobacterium spp.\u003c/em\u003e, and \u003cem\u003ePropionibacterium acnes\u003c/em\u003e have been implicated in the pathogenesis of sarcoidosis,\u003csup\u003e13132\u003c/sup\u003e the newly identified biomarkers CD14 and LBP may support an infectious etiology of sarcoidosis. Previous studies also showed that the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway was more activated in patients with sarcoidosis, and particularly STAT1 and STAT3 played a role in granuloma formation.\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Some EV proteins detected in the discovery phase were correlated with CD14 and LBP through TLRs and the STAT pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Given that CD14 and LBP were upregulated in the process of granuloma formation \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and that CD14-positive monocytes promote MGC formation,\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e both CD14 and LBP in serum EVs could be involved in the pathophysiology of sarcoidosis.\u003c/p\u003e \u003cp\u003eConsidering the limited sensitivity and specificity of the conventional biomarkers ACE and sIL-2R and the positive correlations between various biomarkers, different combinations might be warranted. Our novel biomarkers possess both distinct properties and higher diagnostic potential in comparison to conventional biomarkers. Although LBP levels were weakly correlated with some clinical parameters, the CD14 levels were not correlated with any parameter. Several studies have demonstrated that a combination of different biomarkers increases both sensitivity and specificity.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Hence, a combination of our novel biomarkers with either ACE or sIL-2R may strikingly improve their diagnostic potential in patients with sarcoidosis.\u003c/p\u003e \u003cp\u003eDespite the great advantages of SRM to verify many biomarkers efficiently, our approach has a couple of limitations. First, we examined our new biomarkers regarding organ specificity and disease severity but observed no significant correlation (Figure S3). This might have been caused by the relatively small sample size, thus further studies are warranted. Moreover, the expression levels of the novel biomarkers were not increased in inflammatory lung disease, including COPD, bronchial asthma, and lung fibrosis (data not shown), implying their disease specificity. Next, given that genome, transcriptome, and metabolome networks are also important for precision medicine, it would be intriguing to aggregate our data by integrating multiple omics approaches.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e A system biology platform using clinical data, omics, and bioinformatics may lead to a better understanding of sarcoidosis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe thank Hiroko Omori and Rie Taniguchi for their technical support.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eY.F., Y.T., and A.K. contributed to study design, data analysis, data interpretation, and manuscript writing. K. U. performed label-free proteomics and analyzed data. R.N., M.I., J. A., and T.T. performed targeted proteomics and analyzed data. Y. N. and M. I. contributed to bioinformatics analysis. And all authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThere are no conflicts of interest to declare.\u003c/p\u003e\n\u003ch2\u003eFunding \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP18H05282 to A.K., JP19K08650 to Y.T., JP18K15924 to T.K., and JP19K17636 to Y.F.), a grant from the Uehara Memorial Foundation, and a grant from the Japanese Respiratory Foundation (to Y.T.). The funding sources had no involvement in the study design or conduct; the collection, analysis, and interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHunninghake, G. W. \u003cem\u003eet al.\u003c/em\u003e Statement on sarcoidosis. \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e, \u003cb\u003e160\u003c/b\u003e, 736\u0026ndash;755 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpagnolo, P. \u003cem\u003eet al.\u003c/em\u003e Pulmonary sarcoidosis. \u003cem\u003eLancet Respir. Med\u003c/em\u003e, \u003cb\u003e6\u003c/b\u003e, 389\u0026ndash;402 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrouser, E. D. \u003cem\u003eet al.\u003c/em\u003e Diagnosis and detection of sarcoidosis. 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Proteomics\u003c/em\u003e, \u003cb\u003e128\u003c/b\u003e, 375\u0026ndash;387 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcoidosis , inflammatory granulomatous multi-organ disease, CD14 and LBP , novel biomarkers, extracellular vesicles","lastPublishedDoi":"10.21203/rs.3.rs-793224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-793224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSarcoidosis is an inflammatory granulomatous multi-organ disease of unknown cause. The granulomatous inflammation in sarcoidosis is driven by the interplay between T cells and macrophages. Extracellular vesicles (EVs) play important roles in intercellular communication. Isolated serum EVs from seven patients with sarcoidosis and five control subjects were subjected to non-targeted proteomics analysis. Then 2,292 proteins were detected; 42 proteins were upregulated in patients with sarcoidosis relative to control subjects, and 324 proteins were downregulated. The protein signature of EVs from patients with sarcoidosis reflected disease characteristics such as antigen presentation and immunological disease. Candidate biomarkers were further verified by targeted proteomics analysis in 46 patients and 10 control subjects. CD14 and lipopolysaccharide-binding protein (LBP) were validated by targeted proteomics analysis and upregulation of them was confirmed by immunoblotting. Their expression was also\u0026nbsp;upregulated in macrophages of lung granulomatous lesions. Consistent with these findings, CD14 levels were increased in lipopolysaccharide-stimulated macrophages during multinucleation, concomitant with increased levels of CD14 and LBP in EVs. The area under the curve values of CD14 and LBP were 0.81 and 0.84, respectively. Thus CD14 and LBP in serum EVs, which are associated with granulomatous pathogenesis, can improve the diagnostic accuracy in patients with sarcoidosis.\u003c/p\u003e","manuscriptTitle":"CD14 and LBP as Novel Biomarkers for Sarcoidosis by Proteomics of Extracellular Vesicles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-19 13:47:48","doi":"10.21203/rs.3.rs-793224/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":"5bcdde86-3287-4a5d-be6b-05aa52d94e05","owner":[],"postedDate":"August 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6560732,"name":"Scientific Communication"},{"id":6560733,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2021-11-02T06:14:06+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-19 13:47:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-793224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-793224","identity":"rs-793224","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}