Dendritic cells (DCs) are the most potent APCs. Whereas immature DCs downregulate T-cell responses to induce/ maintain immunologic tolerance, mature DCs promote immunity. To amplify their functions, DCs communicate with neighboring DCs through soluble mediators, cell-to-cell contact, and vesicle exchange. Transfer of nanovesicles (< 100 nm) derived from the endocytic pathway (termed exosomes) represents a novel mechanism of DC-to-DC communication. The facts that exosomes contain exosomeshuttle miRNAs and DC functions can be regulated by exogenous miRNAs, suggest that DC-to-DC interactions could be mediated through exosome-shuttle miRNAs, a hypothesis that remains to be tested. Importantly, the mechanism of transfer of exosome-shuttle miRNAs from the exosome lumen to the cytosol of target cells is unknown. Here, we demonstrate that DCs release exosomes with different miRNAs depending on the maturation of the DCs. By visualizing spontaneous transfer of exosomes between DCs, we demonstrate that exosomes fused with the target DCs, the latter followed by release of the exosome content into the DC cytosol. Importantly, exosome-shuttle miRNAs are functional, because they repress target mRNAs of acceptor DCs. IntroductionCellular miRNAs are released membrane free 1 or packaged inside microvesicles (0.1-1 m) shed by the plasma membrane 2,3 or within nanovesicles (Ͻ 100nm) derived from the endocytic pathway known as exosomes. 4,5 Exosomes are generated as intraluminal vesicles by reverse budding of the membrane of multivesicular bodies (MVBs). Release of exosomes occurs when MVBs fuse their limiting membrane with the plasma membrane. [6][7][8][9] Dendritic cells (DCs) are APCs with the ability to regulate adaptive immunity. Whereas immature DCs down-regulate T-cell responses, mature DCs promote activation, proliferation, and differentiation of effector T cells. 10 Communication between DCs is essential to amplify their tolerogenic and immunogenic functions. 11,12 This DC-to-DC interaction is mediated through cell-tocell contact, soluble mediators, exchange of plasma membrane patches, 13,14 nanotubules, 15 and interaction with apoptotic cellderived vesicles 16 and exosomes. 17,18 Although the mechanisms have not been elucidated, it has been reported that DCs acquire proteins/peptides from other cells via exosomes. [17][18][19] Recently, it has been suggested that transfer of exosome-shuttle miRNAs might constitute a mechanism of cell-tocell communication that regulates mRNA translation 20 or, alternatively, a way to dispose of "unwanted" miRNAs. 21 An important unanswered question in the field is how exosome-shuttle miRNAs, transported inside the vesicles, are delivered into the cytosol of the acceptor cells, a problem we have investigated in this study with the use of DCs. Addressing this point has been challenging because (1) the composition of DC exosomes depends on the maturation of the DC of origin 22,23 ; (2) there is limited information on intercellular communication via "endogenous" (instead of exogenously added...

Objective. Pulmonary complications, including pulmonary fibrosis (PF) and pulmonary arterial hypertension (PAH), are the leading cause of mortality in patients with systemic sclerosis (SSc). The aim of this study was to compare the molecular fingerprint of lung tissue and matching primary fibroblasts from patients with SSc with that of lung tissue and fibroblasts from normal donors, patients with idiopathic pulmonary fibrosis (IPF), and patients with idiopathic pulmonary arterial hypertension (IPAH).Methods. Lung tissue samples were obtained from 33 patients with SSc who underwent lung transplantation. Tissues and cells from a subgroup of SSc patients with predominantly PF or PAH were compared to those from normal donors, patients with IPF, and patients with IPAH. Microarray data were analyzed using efficiency analysis for determination of the optimal data-processing methods. Real-time polymerase chain reaction and immunohistochemistry were used to confirm differential levels of messenger RNA and protein, respectively.Results. Consensus efficiency analysis identified 242 and 335 genes that were differentially expressed in lungs and primary fibroblasts, respectively. SSc-PF and IPF lungs shared enriched functional groups in genes implicated in fibrosis, insulin-like growth factor signaling, and caveolin-mediated endocytosis. Gene functional groups shared by SSc-PAH and IPAH lungs included those involved in antigen presentation, chemokine activity, and interleukin-17 signaling.Conclusion. Using microarray analysis on carefully phenotyped SSc and comparator lung tissues, we demonstrated distinct molecular profiles in tissues and fibroblasts from patients with SSc-associated lung disease compared to idiopathic forms of lung disease. Unique molecular signatures were generated that are disease specific (SSc) and phenotype specific (PF versus PAH). These signatures provide new insights into the pathogenesis and potential therapeutic targets of SScrelated lung disease.

Large-scale proteomics will play a critical role in the rapid display, identification and validation of new protein targets, and elucidation of the underlying molecular events that are associated with disease development, progression and severity. However, because the proteome of most organisms are significantly more complex than the genome, the comprehensive analysis of protein expression changes will require an analytical effort beyond the capacity of standard laboratory equipment. We describe the first high-throughput proteomic analysis of human breast infiltrating ductal carcinoma (IDCA) using OCT (optimal cutting temperature) embedded biopsies, two-dimensional difference gel electrophoresis (2-D DIGE) technology and a fully automated spot handling workstation. Total proteins from four breast IDCAs (Stage I, IIA, IIB and IIIA) were individually compared to protein from non-neoplastic tissue obtained from a female donor with no personal or family history of breast cancer. We detected differences in protein abundance that ranged from 14.8% in stage I IDCA versus normal, to 30.6% in stage IIB IDCA versus normal. A total of 524 proteins that showed > or = three-fold difference in abundance between IDCA and normal tissue were picked, processed and identified by mass spectrometry. Out of the proteins picked, approximately 80% were unambiguously assigned identities by matrix-assisted laser desorbtion/ionization-time of flight mass spectrometry or liquid chromatography-tandem mass spectrometry in the first pass. Bioinformatics tools were also used to mine databases to determine if the identified proteins are involved in important pathways and/or interact with other proteins. Gelsolin, vinculin, lumican, alpha-1-antitrypsin, heat shock protein-60, cytokeratin-18, transferrin, enolase-1 and beta-actin, showed differential abundance between IDCA and normal tissue, but the trend was not consistent in all samples. Out of the proteins with database hits, only heat shock protein-70 (more abundant) and peroxiredoxin-2 (less abundant) displayed the same trend in all the IDCAs examined. This preliminary study demonstrates quantitative and qualitative differences in protein abundance between breast IDCAs and reveals 2-D DIGE portraits that may be a reflection of the histological and pathological status of breast IDCA.

Matrix metalloproteinase (MMP) 2 and 9 are involved in cancer invasion and metastasis, and increased levels occur in serum and plasma of breast cancer (BC) patients. It is, however, unclear whether changes in serum levels can be exploited for early detection or classification of patients into different risk/disease categories. In our study, we measured concentration and activity of MMP2/9 in sera of 345 donors classified as low risk (Gail score < 1.7), high risk (HR) (Gail score 1.7), benign disease or BC. Kruskal-Wallis and Mann-Whitney nonparametric tests showed that total-MMP2 concentration is higher in HR compared to control (p 5 0.012), benign (p 5 0.001) and cancer (p 5 0.007). Active MMP2 (aMMP2) concentration is higher in control than benign and cancer (p < 0.001, respectively). Total and aMMP9 concentrations are higher in cancer than benign (p < 0.001, p 5 0.002, respectively). Total-MMP2 and total-MMP9 activities are lower in control than benign (p < 0.001, p 5 0.002, respectively) and cancer (p < 0.001, respectively). Total-MMP2 and MMP9 activities are also higher in cancer than benign (p 5 0.004, p < 0.001) and HR (p 5 0.008, p 5 0.007, respectively). These results were not affected by age or inclusion/exclusion of donors with noninvasive cancer or atypical hyperplasia. Linear discriminant analysis revealed that HR donors are characterized by lower total-MMP2 and higher aMMP2. Overall group classification accuracy was 64.5%. Independent validation based on the leave-one-out cross validation approach gave an overall classification of 63%. Our study provides evidence supporting the potential role of serum MMP2/9 as biomarkers for breast disease classification. ' 2006 Wiley-Liss, Inc.Key words: serum profiling; matrix metalloproteinase 2 and 9; breast cancer; breast disease; high risk; Gail model Matrix metalloproteinases (MMPs) are a family of highly homologous, zinc-and calcium-dependent extracellular enzymes classified into 5 groups (collagenases, gelatinases, stromelysin, matrilysin and the membrane-type MMP) based on substrate specificity, protein domain structure, sequence homology and ability/ inability to be secreted.

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In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The ‘best’ test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the ‘best’ test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

Correct identification of peptides and proteins in complex biological samples from proteomic mass-spectra is a challenging problem in bioinformatics. The sensitivity and specificity of identification algorithms depend on underlying scoring methods, some being more sensitive, and others more specific. For high-throughput, automated peptide identification, control over the algorithms' performance in terms of trade-off between sensitivity and specificity is desirable. Combinations of algorithms, called 'consensus methods', have been shown to provide more accurate results than individual algorithms. However, due to the proliferation of algorithms and their varied internal settings, a systematic understanding of relative performance of individual and consensus methods are lacking. We performed an in-depth analysis of various approaches to consensus scoring using known protein mixtures, and evaluated the performance of 2310 settings generated from consensus of three different search algorithms: Mascot, Sequest, and X!Tandem. Our findings indicate that the union of Mascot, Sequest, and X!Tandem performed well (considering overall accuracy), and methods using 80-99.9% protein probability and/or minimum 2 peptides and/or 0-50% minimum peptide probability for protein identification performed better (on average) among all consensus methods tested in terms of overall accuracy. The results also suggest method selection strategies to provide direct control over sensitivity and specificity.