Identification and functional characterization of maturation-dependent changes in dendritic cell exosome-shuttle targetome

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DCs release high levels of exosomes (DCEs), containing miRNAs, which orchestrate their tolerogenic or immunogenic functions. This study aimed to identify the exosomes-shuttle miRNAs that are differentially expressed between the mature and immature states of DCs, and to assign functional enrichments to the targets of these miRNAs. A GEO data series comparing miRNA expression in mature and immature DCEs was analyzed and all miRNAs significantly dysregulated between mature and immature states of DCEs were identified. The interactions and targets were mapped separately for the upregulated and down-regulated miRNAs, and interaction networks and functional enrichments of the targets were generated and visualized. 24 miRNAs were found upregulated and 19 miRNAs were found down-regulated in the exosomes of mature DCs over exosomes of immature DCs with 1949 and 1186 targets involved in 131 and 32 pathways, respectively. Further, the functional enrichment of the targets revealed miRNA-targeted changes in expression of biomolecules involved in cytoskeletal remodeling and energy metabolism as key maturation-dependent processes. The results present salient miRNA signatures for identifying DC maturation state and uncover miRNA targets that may serve as therapeutic options in the treatment of various immune dysfunctions. Immunology Cell Communication and Signaling Systems Biology dendritic cell maturation exosome shuttle miRNA target Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Despite a growing number of cells being recognized as Antigen-Presenting Cells (APCs), only three types of cells viz . dendritic cells (DCs), macrophages, and B cells are distinguished by their ability to present exogenous antigens on MHC class II molecules to CD4 + T-helper cells, along with co-stimulatory molecules. Such APCs that can provide all three signals viz. antigen presentation via MHC molecules, expression of co-stimulatory molecules, and cytokine secretion, needed together to activate naïve T-cells, are classified as professional APCs. Among the professional APCs, DCs are the most specialized and effective type, playing a key role in immune homeostasis and the adaptive immune response (Mellman et al., 1998 ; Zanna et al., 2021 ). The process of maturation is a hallmark of DC biology. DCs exist in two distinct developmental stages, determined by pathogen exposure, with different functional characteristics. Under physiological conditions, DCs maintain an immature or steady state to induce immune tolerance and maintain immune homeostasis. These immature DCs (iDCs), acting as sentinels of the immune system to detect pathogens, are specialized for taking up antigens by phagocytosis or macropinocytosis and processing internalized antigen. In response to infection or injury, the accompanying inflammatory stimuli trigger downstream signaling pathways that induce molecular reprogramming of the iDCs. Specifically, toll-like receptor (TLR) stimulation causes DCs to undergo a transition from an immature state to a mature state, which is characterized by markedly upregulated membrane molecules, MHC-II, and costimulatory molecules, needed for efficient T cell priming. In distinct contrast to iDCs, mature DCs (mDCs) exhibit low capacity for antigen uptake and processing (Nam et al., 2021 ; Ness et al., 2021 ; Yin et al., 2021 ). Besides other modes of communication within themselves via direct cell-to-cell contact, soluble mediators, exchange of plasma membrane patches, and nanotubules, DCs prominently rely on exosomes to orchestrate their tolerogenic or immunogenic functions (Montecalvo et al., 2011 ; Kowal and Tkach, 2019 ). Exosomes are small (usually < 100 nm in size) membrane-bound vesicles, generated in the endocytic compartment that are released to the extracellular milieu by living cells. Exosomes appear to serve intercellular communication through the horizontal transfer of proteins, antigens, prions, morphogens, mRNA, and non-coding regulatory RNAs, notably, microRNAs (miRNAs). These miRNAs, termed exosome-shuttle miRNAs, are believed to constitute both a means of intercellular communication for post-transcriptional regulation as well as a mechanism for disposing off unwanted miRNAs. DCs release relatively high levels of exosomes and also interact with free exosomes present in the extracellular space (Kowal and Tkach, 2019 ; Waqas et al., 2022 ). Therefore, DCs have come to be recognized as good models for the analysis of exosome-shuttle miRNAs and their horizontal propagation between cells (Montecalvo et al., 2013 ; Ovchinnikova et al., 2021 ). This study aimed to identify the exosomes-shuttle miRNAs that are differentially expressed between the mature and immature states of DCs, and to ascribe functional enrichments to the targets of these miRNAs in terms of biomolecular interactions and pathways. 2 Methods 2.1 Data sources and groups The GEO data series GSE33179 (Morelli and Montecalvo, 2011 ) was analyzed with GEO2R (Barrett et al., 2012 ). For analysis by GEO2R, the mature dendritic cell exosome (mDCEs) group, comprising of datasets GSM821401, GSM821402, GSM821403 and GSM821410, was defined first followed by the immature dendritic cell exosome (iDCEs) group, comprising of datasets GSM821405, GSM821406, GSM821407 and GSM821411, as per GEO2R convention (Davis and Meltzer, 2007 ). All analysis settings were initially kept at their default configuration. 2.2 Identification of differentially expressed miRNAs Boxplot analysis of the selected datasets was performed to view the distribution of values within datasets and check cross-comparability. Subsequently, force normalization and log transformation were invoked for improving the cross-comparability of the mDCEs and iDCEs groups, and the GEO2R analysis was repeated without multiple-testing corrections. 2.3 Analysis of targets, interactions and functions All miRNA sequences showing significant (p ≤ 0.01) changes in expression between the mDCEs and iDCEs groups in the GEO2R output were selected for downstream targetome analysis. The miRNA sequences with missing nomenclature were queried in miRBase (Kozomara et al., 2019 ). The mapping of interactions and targets was performed separately for the upregulated and for the down-regulated miRNAs using miRNet (Chang et al., 2020 ); the targets were queried against the miRTarBase v8.0 (Huang et al., 2020 ). Further, the interaction networks and functional enrichments of the targets were generated using StringDB v11.5 (Szklarczyk et al., 2021 ). Finally, the interaction networks generated with StringDB were visualized in Cytoscape v3.8.2 (Doncheva et al., 2018 ). 3 Results Boxplot analysis of the data series revealed that the values in the datasets GSM821410 and GSM821411 were not cross-comparable with other dataset values. This was corrected when force normalization and log-transformation were applied (Fig. 1). In all, the expression levels of 43 miRNAs were found to be significantly (p ≤ 0.01) altered. Of these 43 miRNAs, 24 miRNAs were upregulated and 19 miRNAs were down-regulated (Fig. 2, Supplementary File 1) in mDCEs over iDCEs. The miRNAs upregulated in mDCEs showing greatest fold-changes were mmu-miR-672, mmu-miR-335-3p, and mmu-miR-124. The miRNAs down-regulated in mDCEs showing greatest fold-changes were mmu-miR-1249-3p, mmu-miR-805, and mmu-miR-467f. As shown in Fig. 3 (Supplementary File 1), the highest number of targets was ascribed to mmu-mir-9-5p, followed by mmu-mir-124-3p and mmu-mir-34b-5p among the upregulated miRNAs. 930013L23Rik , ankrd28 , arrdc3 , bcl6 , bloc1s3 , cd93 , cenpl , cercam , cnnm3 , ctdsp2 , ctsa , cxcl12 , dnase2a , dusp11 , epb4 .2, fam118a , foxp1 , fyco1 , klhl21 , mapre1 , myo10 , phc3 , pofut1 , prex2 , rab11p , rgs17 , rnmtl1 , sco1 , sema4b , sgk3 , shisa7 , slc14a2 , slc35e2 , smco1 , snx27 , tbc1d2 , trmt10a , vcl , wipf2 , ywhag , zfp317 , zfp446 , and zfp704 were the most promiscuous targets of the upregulated miRNAs. Amongst the down-regulated miRNAs, the highest number of targets was ascribed to mmu-mir-466f-3p, followed by mmu-mir-467f. For the down-regulated miRNAs, adamts9 , ap1g1 , fam160b2 , neu3 , pappa , pgm2l1 , and zfand2a were the most promiscuous targets. StringDB was able to map the interactions for 1546 of the 1949 unique targets of the miRNAs upregulated in mDCEs. The network is shown in Fig. 4 and the particulars of the interaction network are given in Table 1 . Similarly, StringDB mapped the interactions for only 625 of the 1186 unique targets of the miRNAs down-regulated in mDCEs (Fig. 4, Table 1 , Supplementary File 1). Table 1 Network statistics of the interactions of targets of upregulated and down-regulated shuttle miRNAs Network of targets of upregulated miRNAs Network of targets of down-regulated miRNAs number of nodes number of edges average node degree avg. local clustering coefficient expected number of edges enrichment P-Value 1536 11822 15.4 0.278 9171 < 1.0e-16 619 1353 4.37 0.324 1135 1.94e-10 The pathways enriched in the interaction networks of the targets of the upregulated and down-regulated shuttle miRNAs are shown in Fig. 5 (Supplementary File 1). Mapk14 and casp3 were found to be the central nodes of the two networks, respectively. 4 Discussion The pioneering study on murine dendritic cell exosome-shuttle miRNAs by Montecalvo et al. ( 2011 ) forms an important part of our understanding of the transfer of functional microRNAs between dendritic cells via exosomes. However, this study focused primarily on the mechanistics of transfer of the shuttle miRNAs. In the present communication, the data of Montecalvo et al. ( 2011 ) was re-analyzed with particular emphasis on the differential and functional profiling of mature versus immature DC exosome-shuttle miRNAs. Specifically, the miRNAs that were dysregulated (upregulated or down-regulated) in mature DC exosomes compared to immature DC exosomes were identified along with their targets, which were further enriched functionally. mmu-miR-672, mmu-miR-335-3p, and mmu-miR-124 showed greatest fold-changes among the 24 miRNAs that were found upregulated in mDCEs over iDCEs. Interestingly, phb2 , one of the major targets of mmu-miR-672 (Garbacki et al., 2011 ), cooperates with CD86 to mediate CD86-signaling in B cells that regulates the level of IgG1 produced through the activation of distal signaling intermediates. Further, upon CD40 engagement, phb2 is required to activate NF-κB signaling pathway via phospholipase C and protein kinase C activation (Lucas et al., 2013 ). Remodeling of the actin cytoskeleton is required in mDCs to meet maturation-associated changes such as down-regulation of endocytosis, increased migratory behavior, and prime T cells (Blumenthal et al., 2020 ). Enah , a major target of mmu-miR-335-3p, induces the formation of F-actin rich outgrowths and acts synergistically with BAIAP2-alpha and downstream of NTN1 to promote filipodia formation (Zhou et al., 2012 ). Amongst others, mmu-miR124 targets CD55b, which is known to be essential for tolerogenic dendritic cell responses (Strainic et al., 2019 ). Among the upregulated miRNAs, the maximum number of targets were assigned to mmu-mir-9-5p; miR-9-5p has been shown to activate NF-κB in microglial cells and to promote the production of proinflammatory cytokines by targeting MCPIP1 (Yao et al., 2014 ). Notably, and true to the observations of Montecalvo et al. ( 2011 ), mmu-miR-155 was not found to be significantly upregulated. Among the miRNAs down-regulated in mDCEs showing greatest fold-changes, mmu-miR-1249-3p is recognized as a pro-differentiation miRNA (Polesskaya et al., 2013 ); it is apparent that mDCs would not require such modulators. Similar to the findings of this study, Pang et al. ( 2019 ) also reported lower levels of mmu-miR-805 in exosomes from bone marrow-derived mDCs as compared to those from iDCs. The maximum number of targets was ascribed to mmu-mir-466f-3p. Intriguingly, although low levels of mmu-mir-466f-3p have been associated with the maintenance of a mesenchymal state (Besharat et al., 2018 ), its low levels have also been recorded upon exposure to pathogens (Kumar and Nerurkar, 2014 ) and antigens (Atherton et al., 2019 ). Among the down-regulated miRNAs, mmu-mir-467f showed third highest fold-change besides having the second greatest number of targets assigned to it. Importantly, low levels of mmu-miR-467f may be a signature of proliferating T-helper cells (Sommers et al., 2013 ). The most promiscuous targets of the upregulated miRNAs revealed an over-representation of RNA polymerase II transcription and rho GTPase signaling reactomes, and of the Slit/Robo pathway. The activation of the Slit/Robo pathway has previously been implicated in inhibition of dendritic cell migration (Guan et al., 2003 ). It can be reasoned that the Slit/Robo pathway is repressed by miRNAs in mDCs to allow migratory behavior. The glycogenolysis and galactose catabolism reactomes were over-represented among the most promiscuous targets of the down-regulated miRNAs; achieving continuing glycogenolysis through the down-regulation of repressor miRNAs may be critical for mDC functioning (Thwe et al., 2017 ) and glucose being inhibitory to DC functions (Lawless et al., 2017 ), energy metabolism in DCs may shift to galactose given the down-regulation of repressor miRNAs. Conversely, it may be stated that the tolerogenic state of iDCs is maintained under the repression of glycogenolysis and galactose catabolism by miRNAs. The top pathways enriched in the networks of the upregulated miRNAs included pathways in cancer, micrornas in cancer, (human) papillomavirus infection, Salmonella infection, Epstein-Barr virus infection, proteoglycans in cancer, MAPK signaling pathway, focal adhesion, Rap1 signaling pathway, and cellular senescence; in all 131 such pathways were identified. Similarly, 32 pathways were found enriched in the interactions of the targets of the down-regulated miRNAs, of which the topmost pathways were B cell receptor signaling pathway, (human) cytomegalovirus infection, natural killer cell mediated cytotoxicity, Kaposi sarcoma-associated herpesvirus infection, colorectal cancer, ErbB signaling pathway, axon guidance, apelin signaling pathway, signaling pathways regulating pluripotency of stem cells, and hepatitis C pathway. 28 pathways viz . AGE-RAGE signaling pathway in diabetic complications, apelin signaling pathway, axon guidance, B cell receptor signaling pathway, breast cancer, cellular senescence, chemokine signaling pathway, colorectal cancer, EGFR tyrosine kinase inhibitor resistance, endometrial cancer, ErbB signaling pathway, glioma, hepatitis B, hepatitis C, HIF-1 signaling pathway, (human) cytomegalovirus infection, (human) immunodeficiency virus 1 infection, influenza A, Kaposi sarcoma-associated herpesvirus infection, microRNAs in cancer, NF-kappa B signaling pathway, osteoclast differentiation, prostate cancer, proteoglycans in cancer, T cell receptor signaling pathway, thyroid hormone signaling pathway, TNF signaling pathway, and VEGF signaling pathway were common to the networks of the targets of both upregulated and down-regulated shuttle miRNAs. Mapk14 was found to be the central node in network of targets of the upregulated miRNAs. Mapk14 , an important component of two dominant pathways involved in DC maturation viz . toll-like receptor signaling and the leukocyte transendothelial migration, has been shown to down-regulated by at least two-folds at both the RNA and protein level (Buschow et al., 2010 ). Casp3 was found to be the central node in network of targets of the down-regulated miRNAs. Casp3 , reported to be induced in maturing DCs, may be a signature of mDCs (Jin et al., 2010 ) and its overexpression has been found to promote DC maturation and T cell activation (Liu et al., 2019 ). 5 Conclusion This study identified a number of shuttle miRNAs, and their targets, which are differentially expressed in the exosomes of dendritic cells in a maturation-dependent manner. Besides forming signatures of DC maturation state, these differentially expressed exosome-shuttle miRNAs and their targets may serve as important therapeutic candidates for various immune dysfunctions. Declarations Conflict of Interest The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The study did not receive any funding from any source. Acknowledgments The authors of all computational tools used in the study are thankfully acknowledged for making them available for free to the public. Data Availability Statement The datasets analysed in this study can be found in online repositories. The names of the repositories and accession numbers have been cited at the appropriate places in the article. References Atherton LJ, Jorquera PA, Bakre AA, Tripp RA (2019) Determining immune and miRNA biomarkers related to respiratory syncytial virus (RSV) vaccine types. 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Supplementary Files SupplementaryFile1.xlsx Supplementary File 1 Cite Share Download PDF Status: Published Journal Publication published 20 Jul, 2025 Read the published version in Medinformatics → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4589825","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315019540,"identity":"b8304bd5-6cf7-46fe-956a-a852228dde06","order_by":0,"name":"Bhaskar Ganguly","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3PsQrCMBCA4cDBdbnSNaLoK1QCVdCHEQSnFnwA6eioqyDoKzg5K8G6BOfugQ5OQqGjmAoOLrVugvmHC4H7IGHMZvvNEMzwGYODOXmrhoAXwVFJ6BtCfnn9TPzzMtHTWSy8tcqv6axPzJGnXSVR0hGrRAb8Eu0HYWIeRpNJWkUaqzE2CQ9Dpty9CNEQTkE12WpD7vGwoygT4b0G8Thg051D4CsCHc3rEBr3hLuQoqswgGjBCT/9BZ1jpqmIuxsFOg+LuO05Mqkkb5w/Z931Mrh9s22z2Wz/0wNWRT8XNEGMqQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4224-1887","institution":"Indian Immunologicals Limited","correspondingAuthor":true,"prefix":"","firstName":"Bhaskar","middleName":"","lastName":"Ganguly","suffix":""}],"badges":[],"createdAt":"2024-06-16 13:13:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4589825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4589825/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.47852/bonviewMEDIN52024310","type":"published","date":"2025-07-21T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58652377,"identity":"a8b17875-cec9-4271-b0c4-9453865e1444","added_by":"auto","created_at":"2024-06-19 10:26:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":633980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot analysis of the datasets.\u003c/strong\u003e Boxplot analysis of the datasets GSM821401, GSM821402, GSM821403 and GSM821410 (green) and GSM821405, GSM821406, GSM821407 and GSM821411 (blue) showed that the datasets GSM821410 and GSM821411 were not cross-comparable with other dataset values (left). This was corrected when force normalization and log-transformation were applied (right).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/f2d613fe05c1eb28a8f057c7.jpg"},{"id":58652376,"identity":"6e4afbb1-b0d9-41ff-9fbb-436598587e27","added_by":"auto","created_at":"2024-06-19 10:26:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":323842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShuttle\u003c/strong\u003e \u003cstrong\u003emiRNAs differentially-expressed in exosomes of mature DC origin over exosomes of immature DC origin. A.\u003c/strong\u003e Volcano plot from the GEO2R showing 43 differentially-expressed miRNAs, of which 24 miRNAs were upregulated (red dots) and 19 miRNAs were down-regulated (blue dots). \u003cstrong\u003eB.\u003c/strong\u003eThe sequences, nomenclature, and log fold-changes (bars) of the 43 differentially-expressed miRNAs have been shown; 03 of the down-regulated miRNAs (X, Y, Z) could not be assigned nomenclature.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/74751eef994a2b7c3581d5bb.jpg"},{"id":58652378,"identity":"c4a34d8f-06df-4d86-971e-73b0561c73c4","added_by":"auto","created_at":"2024-06-19 10:26:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22767609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargets of the shuttle\u003c/strong\u003e \u003cstrong\u003emiRNAs differentially-expressed in exosomes of mature DC origin over exosomes of immature DC origin.\u003c/strong\u003e The targets of the up-regulated miRNAs (upper half) and down-regulated miRNAs (lower half) determined by miRTarBase have been shown; the weights of the edges represent the strength of the evidence for the interaction.\u003c/p\u003e","description":"","filename":"Fig32.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/549d43203e4808f48ff558e7.jpg"},{"id":58652379,"identity":"f1f2d1df-10fb-4f14-9979-770f4998855d","added_by":"auto","created_at":"2024-06-19 10:26:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2203268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction network of the targets of the shuttle miRNAs differentially-expressed in exosomes of mature DC origin over exosomes of immature DC origin.\u003c/strong\u003e The mutual interactions of targets of the up-regulated miRNAs have been shown in the upper half and those of the down-regulated miRNAs have been shown in the lower half. The interactions were predicted by STRING and visualized in Cytoscape.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/4d9c457332b4fc6fcfdd320a.jpg"},{"id":58652380,"identity":"6531cf0a-a5a2-4a84-9eb8-97c5b6567114","added_by":"auto","created_at":"2024-06-19 10:26:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":811515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional pathways enriched in the interactions of the targets of the differentially-expressed shuttle miRNAs.\u003c/strong\u003e Peacock plot of KEGG pathways involved in the interactions of targets of the up-regulated miRNAs (left) and the down-regulated miRNAs (right) were determined by STRING. The color represents the strength of the enrichment and the size of the nodes represents the false-discovery rate from the network involved in a pathway.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/97940fcf47e0c10e3572b7e6.jpg"},{"id":87609646,"identity":"5953d298-73be-4d97-ae8a-524914d45bee","added_by":"auto","created_at":"2025-07-25 20:00:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27385697,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/14eb8f3a-239c-43af-b9b9-c4cf126de641.pdf"},{"id":58652375,"identity":"d1a8c65d-7379-4b5f-bf43-cc32644a295e","added_by":"auto","created_at":"2024-06-19 10:26:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":207040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary File 1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4589825/v1/951acc4d0da9de5e5715f776.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIdentification and functional characterization of maturation-dependent changes in dendritic cell exosome-shuttle targetome\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDespite a growing number of cells being recognized as Antigen-Presenting Cells (APCs), only three types of cells \u003cem\u003eviz\u003c/em\u003e. dendritic cells (DCs), macrophages, and B cells are distinguished by their ability to present exogenous antigens on MHC class II molecules to CD4\u0026thinsp;+\u0026thinsp;T-helper cells, along with co-stimulatory molecules. Such APCs that can provide all three signals \u003cem\u003eviz.\u003c/em\u003e antigen presentation \u003cem\u003evia\u003c/em\u003e MHC molecules, expression of co-stimulatory molecules, and cytokine secretion, needed together to activate na\u0026iuml;ve T-cells, are classified as professional APCs. Among the professional APCs, DCs are the most specialized and effective type, playing a key role in immune homeostasis and the adaptive immune response (Mellman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zanna et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe process of maturation is a hallmark of DC biology. DCs exist in two distinct developmental stages, determined by pathogen exposure, with different functional characteristics. Under physiological conditions, DCs maintain an immature or steady state to induce immune tolerance and maintain immune homeostasis. These immature DCs (iDCs), acting as sentinels of the immune system to detect pathogens, are specialized for taking up antigens by phagocytosis or macropinocytosis and processing internalized antigen. In response to infection or injury, the accompanying inflammatory stimuli trigger downstream signaling pathways that induce molecular reprogramming of the iDCs. Specifically, toll-like receptor (TLR) stimulation causes DCs to undergo a transition from an immature state to a mature state, which is characterized by markedly upregulated membrane molecules, MHC-II, and costimulatory molecules, needed for efficient T cell priming. In distinct contrast to iDCs, mature DCs (mDCs) exhibit low capacity for antigen uptake and processing (Nam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ness et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides other modes of communication within themselves \u003cem\u003evia\u003c/em\u003e direct cell-to-cell contact, soluble mediators, exchange of plasma membrane patches, and nanotubules, DCs prominently rely on exosomes to orchestrate their tolerogenic or immunogenic functions (Montecalvo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kowal and Tkach, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Exosomes are small (usually\u0026thinsp;\u0026lt;\u0026thinsp;100 nm in size) membrane-bound vesicles, generated in the endocytic compartment that are released to the extracellular \u003cem\u003emilieu\u003c/em\u003e by living cells. Exosomes appear to serve intercellular communication through the horizontal transfer of proteins, antigens, prions, morphogens, mRNA, and non-coding regulatory RNAs, notably, microRNAs (miRNAs). These miRNAs, termed exosome-shuttle miRNAs, are believed to constitute both a means of intercellular communication for post-transcriptional regulation as well as a mechanism for disposing off unwanted miRNAs. DCs release relatively high levels of exosomes and also interact with free exosomes present in the extracellular space (Kowal and Tkach, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Waqas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, DCs have come to be recognized as good models for the analysis of exosome-shuttle miRNAs and their horizontal propagation between cells (Montecalvo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ovchinnikova et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aimed to identify the exosomes-shuttle miRNAs that are differentially expressed between the mature and immature states of DCs, and to ascribe functional enrichments to the targets of these miRNAs in terms of biomolecular interactions and pathways.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 Data sources and groups\u003c/p\u003e\n\u003cp\u003eThe GEO data series GSE33179 (Morelli and Montecalvo, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) was analyzed with GEO2R (Barrett et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). For analysis by GEO2R, the mature dendritic cell exosome (mDCEs) group, comprising of datasets GSM821401, GSM821402, GSM821403 and GSM821410, was defined first followed by the immature dendritic cell exosome (iDCEs) group, comprising of datasets GSM821405, GSM821406, GSM821407 and GSM821411, as per GEO2R convention (Davis and Meltzer, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). All analysis settings were initially kept at their default configuration.\u003c/p\u003e\n\u003cp\u003e2.2 Identification of differentially expressed miRNAs\u003c/p\u003e\n\u003cp\u003eBoxplot analysis of the selected datasets was performed to view the distribution of values within datasets and check cross-comparability. Subsequently, force normalization and log transformation were invoked for improving the cross-comparability of the mDCEs and iDCEs groups, and the GEO2R analysis was repeated without multiple-testing corrections.\u003c/p\u003e\n\u003cp\u003e2.3 Analysis of targets, interactions and functions\u003c/p\u003e\n\u003cp\u003eAll miRNA sequences showing significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) changes in expression between the mDCEs and iDCEs groups in the GEO2R output were selected for downstream targetome analysis. The miRNA sequences with missing nomenclature were queried in miRBase (Kozomara et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mapping of interactions and targets was performed separately for the upregulated and for the down-regulated miRNAs using miRNet (Chang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); the targets were queried against the miRTarBase v8.0 (Huang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further, the interaction networks and functional enrichments of the targets were generated using StringDB v11.5 (Szklarczyk et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, the interaction networks generated with StringDB were visualized in Cytoscape v3.8.2 (Doncheva et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eBoxplot analysis of the data series revealed that the values in the datasets GSM821410 and GSM821411 were not cross-comparable with other dataset values. This was corrected when force normalization and log-transformation were applied (Fig.\u0026nbsp;1). In all, the expression levels of 43 miRNAs were found to be significantly (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) altered. Of these 43 miRNAs, 24 miRNAs were upregulated and 19 miRNAs were down-regulated (Fig.\u0026nbsp;2, Supplementary File 1) in mDCEs over iDCEs.\u003c/p\u003e \u003cp\u003eThe miRNAs upregulated in mDCEs showing greatest fold-changes were mmu-miR-672, mmu-miR-335-3p, and mmu-miR-124. The miRNAs down-regulated in mDCEs showing greatest fold-changes were mmu-miR-1249-3p, mmu-miR-805, and mmu-miR-467f.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;3 (Supplementary File 1), the highest number of targets was ascribed to mmu-mir-9-5p, followed by mmu-mir-124-3p and mmu-mir-34b-5p among the upregulated miRNAs. \u003cem\u003e930013L23Rik\u003c/em\u003e, \u003cem\u003eankrd28\u003c/em\u003e, \u003cem\u003earrdc3\u003c/em\u003e, \u003cem\u003ebcl6\u003c/em\u003e, \u003cem\u003ebloc1s3\u003c/em\u003e, \u003cem\u003ecd93\u003c/em\u003e, \u003cem\u003ecenpl\u003c/em\u003e, \u003cem\u003ecercam\u003c/em\u003e, \u003cem\u003ecnnm3\u003c/em\u003e, \u003cem\u003ectdsp2\u003c/em\u003e, \u003cem\u003ectsa\u003c/em\u003e, \u003cem\u003ecxcl12\u003c/em\u003e, \u003cem\u003ednase2a\u003c/em\u003e, \u003cem\u003edusp11\u003c/em\u003e, \u003cem\u003eepb4\u003c/em\u003e.2, \u003cem\u003efam118a\u003c/em\u003e, \u003cem\u003efoxp1\u003c/em\u003e, \u003cem\u003efyco1\u003c/em\u003e, \u003cem\u003eklhl21\u003c/em\u003e, \u003cem\u003emapre1\u003c/em\u003e, \u003cem\u003emyo10\u003c/em\u003e, \u003cem\u003ephc3\u003c/em\u003e, \u003cem\u003epofut1\u003c/em\u003e, \u003cem\u003eprex2\u003c/em\u003e, \u003cem\u003erab11p\u003c/em\u003e, \u003cem\u003ergs17\u003c/em\u003e, \u003cem\u003ernmtl1\u003c/em\u003e, \u003cem\u003esco1\u003c/em\u003e, \u003cem\u003esema4b\u003c/em\u003e, \u003cem\u003esgk3\u003c/em\u003e, \u003cem\u003eshisa7\u003c/em\u003e, \u003cem\u003eslc14a2\u003c/em\u003e, \u003cem\u003eslc35e2\u003c/em\u003e, \u003cem\u003esmco1\u003c/em\u003e, \u003cem\u003esnx27\u003c/em\u003e, \u003cem\u003etbc1d2\u003c/em\u003e, \u003cem\u003etrmt10a\u003c/em\u003e, \u003cem\u003evcl\u003c/em\u003e, \u003cem\u003ewipf2\u003c/em\u003e, \u003cem\u003eywhag\u003c/em\u003e, \u003cem\u003ezfp317\u003c/em\u003e, \u003cem\u003ezfp446\u003c/em\u003e, and \u003cem\u003ezfp704\u003c/em\u003e were the most promiscuous targets of the upregulated miRNAs. Amongst the down-regulated miRNAs, the highest number of targets was ascribed to mmu-mir-466f-3p, followed by mmu-mir-467f. For the down-regulated miRNAs, \u003cem\u003eadamts9\u003c/em\u003e, \u003cem\u003eap1g1\u003c/em\u003e, \u003cem\u003efam160b2\u003c/em\u003e, \u003cem\u003eneu3\u003c/em\u003e, \u003cem\u003epappa\u003c/em\u003e, \u003cem\u003epgm2l1\u003c/em\u003e, and \u003cem\u003ezfand2a\u003c/em\u003e were the most promiscuous targets.\u003c/p\u003e \u003cp\u003eStringDB was able to map the interactions for 1546 of the 1949 unique targets of the miRNAs upregulated in mDCEs. The network is shown in Fig.\u0026nbsp;4 and the particulars of the interaction network are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Similarly, StringDB mapped the interactions for only 625 of the 1186 unique targets of the miRNAs down-regulated in mDCEs (Fig.\u0026nbsp;4, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary File 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNetwork statistics of the interactions of targets of upregulated and down-regulated shuttle miRNAs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetwork of targets of upregulated miRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNetwork of targets of down-regulated miRNAs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enumber of nodes\u003c/p\u003e \u003cp\u003enumber of edges\u003c/p\u003e \u003cp\u003eaverage node degree\u003c/p\u003e \u003cp\u003eavg. local clustering coefficient\u003c/p\u003e \u003cp\u003eexpected number of edges\u003c/p\u003e \u003cp\u003eenrichment P-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1536\u003c/p\u003e \u003cp\u003e11822\u003c/p\u003e \u003cp\u003e15.4\u003c/p\u003e \u003cp\u003e0.278\u003c/p\u003e \u003cp\u003e9171\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.0e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619\u003c/p\u003e \u003cp\u003e1353\u003c/p\u003e \u003cp\u003e4.37\u003c/p\u003e \u003cp\u003e0.324\u003c/p\u003e \u003cp\u003e1135\u003c/p\u003e \u003cp\u003e1.94e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe pathways enriched in the interaction networks of the targets of the upregulated and down-regulated shuttle miRNAs are shown in Fig.\u0026nbsp;5 (Supplementary File 1). \u003cem\u003eMapk14\u003c/em\u003e and \u003cem\u003ecasp3\u003c/em\u003e were found to be the central nodes of the two networks, respectively.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe pioneering study on murine dendritic cell exosome-shuttle miRNAs by Montecalvo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) forms an important part of our understanding of the transfer of functional microRNAs between dendritic cells \u003cem\u003evia\u003c/em\u003e exosomes. However, this study focused primarily on the mechanistics of transfer of the shuttle miRNAs. In the present communication, the data of Montecalvo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was re-analyzed with particular emphasis on the differential and functional profiling of mature \u003cem\u003eversus\u003c/em\u003e immature DC exosome-shuttle miRNAs. Specifically, the miRNAs that were dysregulated (upregulated or down-regulated) in mature DC exosomes compared to immature DC exosomes were identified along with their targets, which were further enriched functionally.\u003c/p\u003e \u003cp\u003emmu-miR-672, mmu-miR-335-3p, and mmu-miR-124 showed greatest fold-changes among the 24 miRNAs that were found upregulated in mDCEs over iDCEs. Interestingly, \u003cem\u003ephb2\u003c/em\u003e, one of the major targets of mmu-miR-672 (Garbacki et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), cooperates with CD86 to mediate CD86-signaling in B cells that regulates the level of IgG1 produced through the activation of distal signaling intermediates. Further, upon CD40 engagement, \u003cem\u003ephb2\u003c/em\u003e is required to activate NF-κB signaling pathway \u003cem\u003evia\u003c/em\u003e phospholipase C and protein kinase C activation (Lucas et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Remodeling of the actin cytoskeleton is required in mDCs to meet maturation-associated changes such as down-regulation of endocytosis, increased migratory behavior, and prime T cells (Blumenthal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eEnah\u003c/em\u003e, a major target of mmu-miR-335-3p, induces the formation of F-actin rich outgrowths and acts synergistically with BAIAP2-alpha and downstream of NTN1 to promote filipodia formation (Zhou et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Amongst others, mmu-miR124 targets CD55b, which is known to be essential for tolerogenic dendritic cell responses (Strainic et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among the upregulated miRNAs, the maximum number of targets were assigned to mmu-mir-9-5p; miR-9-5p has been shown to activate NF-κB in microglial cells and to promote the production of proinflammatory cytokines by targeting MCPIP1 (Yao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Notably, and true to the observations of Montecalvo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), mmu-miR-155 was not found to be significantly upregulated.\u003c/p\u003e \u003cp\u003eAmong the miRNAs down-regulated in mDCEs showing greatest fold-changes, mmu-miR-1249-3p is recognized as a pro-differentiation miRNA (Polesskaya et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); it is apparent that mDCs would not require such modulators. Similar to the findings of this study, Pang et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also reported lower levels of mmu-miR-805 in exosomes from bone marrow-derived mDCs as compared to those from iDCs. The maximum number of targets was ascribed to mmu-mir-466f-3p. Intriguingly, although low levels of mmu-mir-466f-3p have been associated with the maintenance of a mesenchymal state (Besharat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), its low levels have also been recorded upon exposure to pathogens (Kumar and Nerurkar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and antigens (Atherton et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among the down-regulated miRNAs, mmu-mir-467f showed third highest fold-change besides having the second greatest number of targets assigned to it. Importantly, low levels of mmu-miR-467f may be a signature of proliferating T-helper cells (Sommers et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most promiscuous targets of the upregulated miRNAs revealed an over-representation of RNA polymerase II transcription and rho GTPase signaling reactomes, and of the Slit/Robo pathway. The activation of the Slit/Robo pathway has previously been implicated in inhibition of dendritic cell migration (Guan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It can be reasoned that the Slit/Robo pathway is repressed by miRNAs in mDCs to allow migratory behavior. The glycogenolysis and galactose catabolism reactomes were over-represented among the most promiscuous targets of the down-regulated miRNAs; achieving continuing glycogenolysis through the down-regulation of repressor miRNAs may be critical for mDC functioning (Thwe et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and glucose being inhibitory to DC functions (Lawless et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), energy metabolism in DCs may shift to galactose given the down-regulation of repressor miRNAs. Conversely, it may be stated that the tolerogenic state of iDCs is maintained under the repression of glycogenolysis and galactose catabolism by miRNAs.\u003c/p\u003e \u003cp\u003eThe top pathways enriched in the networks of the upregulated miRNAs included pathways in cancer, micrornas in cancer, (human) papillomavirus infection, \u003cem\u003eSalmonella\u003c/em\u003e infection, Epstein-Barr virus infection, proteoglycans in cancer, MAPK signaling pathway, focal adhesion, Rap1 signaling pathway, and cellular senescence; in all 131 such pathways were identified. Similarly, 32 pathways were found enriched in the interactions of the targets of the down-regulated miRNAs, of which the topmost pathways were B cell receptor signaling pathway, (human) cytomegalovirus infection, natural killer cell mediated cytotoxicity, Kaposi sarcoma-associated herpesvirus infection, colorectal cancer, ErbB signaling pathway, axon guidance, apelin signaling pathway, signaling pathways regulating pluripotency of stem cells, and hepatitis C pathway. 28 pathways \u003cem\u003eviz\u003c/em\u003e. AGE-RAGE signaling pathway in diabetic complications, apelin signaling pathway, axon guidance, B cell receptor signaling pathway, breast cancer, cellular senescence, chemokine signaling pathway, colorectal cancer, EGFR tyrosine kinase inhibitor resistance, endometrial cancer, ErbB signaling pathway, glioma, hepatitis B, hepatitis C, HIF-1 signaling pathway, (human) cytomegalovirus infection, (human) immunodeficiency virus 1 infection, influenza A, Kaposi sarcoma-associated herpesvirus infection, microRNAs in cancer, NF-kappa B signaling pathway, osteoclast differentiation, prostate cancer, proteoglycans in cancer, T cell receptor signaling pathway, thyroid hormone signaling pathway, TNF signaling pathway, and VEGF signaling pathway were common to the networks of the targets of both upregulated and down-regulated shuttle miRNAs. \u003cem\u003eMapk14\u003c/em\u003e was found to be the central node in network of targets of the upregulated miRNAs. \u003cem\u003eMapk14\u003c/em\u003e, an important component of two dominant pathways involved in DC maturation \u003cem\u003eviz\u003c/em\u003e. toll-like receptor signaling and the leukocyte transendothelial migration, has been shown to down-regulated by at least two-folds at both the RNA and protein level (Buschow et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eCasp3\u003c/em\u003e was found to be the central node in network of targets of the down-regulated miRNAs. \u003cem\u003eCasp3\u003c/em\u003e, reported to be induced in maturing DCs, may be a signature of mDCs (Jin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and its overexpression has been found to promote DC maturation and T cell activation (Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study identified a number of shuttle miRNAs, and their targets, which are differentially expressed in the exosomes of dendritic cells in a maturation-dependent manner. Besides forming signatures of DC maturation state, these differentially expressed exosome-shuttle miRNAs and their targets may serve as important therapeutic candidates for various immune dysfunctions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study did not receive any funding from any source.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors of all computational tools used in the study are thankfully acknowledged for making them available for free to the public.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe datasets analysed in this study can be found in online repositories. The names of the repositories and accession numbers have been cited at the appropriate places in the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtherton LJ, Jorquera PA, Bakre AA, Tripp RA (2019) Determining immune and miRNA biomarkers related to respiratory syncytial virus (RSV) vaccine types. Front Immunol 10:2323\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A (2012) NCBI GEO: archive for functional genomics data sets\u0026mdash;update. 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Comb Chem High Throughput Screen 15(3):202\u0026ndash;215\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"dendritic cell, maturation, exosome, shuttle, miRNA, target","lastPublishedDoi":"10.21203/rs.3.rs-4589825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4589825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDendritic cells (DCs) are the most professional antigen-presenting cells, which undergo a hallmark transition from an immature to a mature state. DCs release high levels of exosomes (DCEs), containing miRNAs, which orchestrate their tolerogenic or immunogenic functions. This study aimed to identify the exosomes-shuttle miRNAs that are differentially expressed between the mature and immature states of DCs, and to assign functional enrichments to the targets of these miRNAs. A GEO data series comparing miRNA expression in mature and immature DCEs was analyzed and all miRNAs significantly dysregulated between mature and immature states of DCEs were identified. The interactions and targets were mapped separately for the upregulated and down-regulated miRNAs, and interaction networks and functional enrichments of the targets were generated and visualized. 24 miRNAs were found upregulated and 19 miRNAs were found down-regulated in the exosomes of mature DCs over exosomes of immature DCs with 1949 and 1186 targets involved in 131 and 32 pathways, respectively. Further, the functional enrichment of the targets revealed miRNA-targeted changes in expression of biomolecules involved in cytoskeletal remodeling and energy metabolism as key maturation-dependent processes. The results present salient miRNA signatures for identifying DC maturation state and uncover miRNA targets that may serve as therapeutic options in the treatment of various immune dysfunctions.\u003c/p\u003e","manuscriptTitle":"Identification and functional characterization of maturation-dependent changes in dendritic cell exosome-shuttle targetome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 10:26:50","doi":"10.21203/rs.3.rs-4589825/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":"5f038b88-2beb-4a7e-b8fb-96fbdcc9eceb","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33313325,"name":"Immunology"},{"id":33313326,"name":"Cell Communication and Signaling"},{"id":33313327,"name":"Systems Biology"}],"tags":[],"updatedAt":"2025-07-25T20:00:36+00:00","versionOfRecord":{"articleIdentity":"rs-4589825","link":"https://doi.org/10.47852/bonviewMEDIN52024310","journal":{"identity":"medinformatics","isVorOnly":true,"title":"Medinformatics"},"publishedOn":"2025-07-21 00:00:00","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2024-06-19 10:26:50","video":"","vorDoi":"10.47852/bonviewMEDIN52024310","vorDoiUrl":"https://doi.org/10.47852/bonviewMEDIN52024310","workflowStages":[]},"version":"v1","identity":"rs-4589825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4589825","identity":"rs-4589825","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}