Exosomes, secreted microvesicles transporting microRNAs (miRNAs), mRNAs, and proteins through bodily fluids, facilitate intercellular communication and elicit immune responses. Exosomal contents vary depending on the source and the physiological conditions of cells and can provide insights into how cells and systems cope with physiological perturbations. Previous analysis of circulating miRNAs in patients with complex regional pain syndrome (CRPS), a debilitating chronic pain disorder, revealed a subset of miRNAs in whole blood that are altered in the disease. To determine functional consequences of alterations in exosomal biomolecules in inflammation and pain, we investigated exosome-mediated information transfer in vitro, in a rodent model of inflammatory pain and in exosomes from patients with CRPS. Mouse macrophage cells stimulated with lipopolysaccharides (LPS) secrete exosomes containing elevated levels of cytokines and miRNAs that mediate inflammation. Transcriptome sequencing of exosomal RNA revealed global alterations in both innate and adaptive immune pathways. Exosomes from LPS-stimulated cells were sufficient to cause NF-kappaB activation in naïve cells, indicating functionality in recipient cells. A single injection of exosomes attenuated thermal hyperalgesia in a mouse model of inflammatory pain, suggesting an immunoprotective role for macrophage-derived exosomes. We also show that circulating miRNAs altered in patients with complex regional pain syndrome are trafficked by exosomes. Macrophage-derived exosomes carry a protective signature that is altered when secreting cells are exposed to an inflammatory stimulus. With their systemic signaling capabilities, exosomes can induce pleiotropic effects potentially mediating the multifactorial pathology underlying chronic pain and should be explored for their therapeutic utility.

BackgroundAberrant expression of small noncoding RNAs called microRNAs (miRNAs) is a common feature of several human diseases. The objective of the study was to identify miRNA modulation in patients with complex regional pain syndrome (CRPS) a chronic pain condition resulting from dysfunction in the central and/or peripheral nervous systems. Due to a multitude of inciting pathologies, symptoms and treatment conditions, the CRPS patient population is very heterogeneous. Our goal was to identify differentially expressed miRNAs in blood and explore their utility in patient stratification.MethodsWe profiled miRNAs in whole blood from 41 patients with CRPS and 20 controls using TaqMan low density array cards. Since neurogenic inflammation is known to play a significant role in CRPS we measured inflammatory markers including chemokines, cytokines, and their soluble receptors in blood from the same individuals. Correlation analyses were performed for miRNAs, inflammatory markers and other parameters including disease symptoms, medication, and comorbid conditions.ResultsThree different groups emerged from miRNA profiling. One group was comprised of 60% of CRPS patients and contained no control subjects. miRNA profiles from the remaining patients were interspersed among control samples in the other two groups. We identified differential expression of 18 miRNAs in CRPS patients. Analysis of inflammatory markers showed that vascular endothelial growth factor (VEGF), interleukin1 receptor antagonist (IL1Ra) and monocyte chemotactic protein-1 (MCP1) were significantly elevated in CRPS patients. VEGF and IL1Ra showed significant correlation with the patients reported pain levels. Analysis of the patients who were clustered according to their miRNA profile revealed correlations that were not significant in the total patient population. Correlation analysis of miRNAs detected in blood with additional parameters identified miRNAs associated with comorbidities such as headache, thyroid disorder and use of narcotics and antiepileptic drugs.ConclusionsmiRNA profiles can be useful in patient stratification and have utility as potential biomarkers for pain. Differentially expressed miRNAs can provide molecular insights into gene regulation and could lead to new therapeutic intervention strategies for CRPS.

BackgroundMicroRNAs (miRNAs) are short non-coding RNA molecules that regulate mRNA transcript levels and translation. Deregulation of microRNAs is indicated in a number of diseases and microRNAs are seen as a promising target for biomarker identification and drug development. miRNA expression is commonly measured by microarray or real-time polymerase chain reaction (RT-PCR). The findings of RT-PCR data are highly dependent on the normalization techniques used during preprocessing of the Cycle Threshold readings from RT-PCR. Some of the commonly used endogenous controls themselves have been discovered to be differentially expressed in various conditions such as cancer, making them inappropriate internal controls.MethodsWe demonstrate that RT-PCR data contains a systematic bias resulting in large variations in the Cycle Threshold (CT) values of the low-abundant miRNA samples. We propose a new data normalization method that considers all available microRNAs as endogenous controls. A weighted normalization approach is utilized to allow contribution from all microRNAs, weighted by their empirical stability.ResultsThe systematic bias in RT-PCR data is illustrated on a microRNA dataset obtained from primary cutaneous melanocytic neoplasms. We show that through a single control parameter, this method is able to emulate other commonly used normalization methods and thus provides a more general approach. We explore the consistency of RT-PCR expression data with microarray expression by utilizing a dataset where both RT-PCR and microarray profiling data is available for the same miRNA samples.ConclusionsA weighted normalization method allows the contribution of all of the miRNAs, whether they are highly abundant or have low expression levels. Our findings further suggest that the normalization of a particular miRNA should rely on only miRNAs that have comparable expression levels.

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Low dose computed tomography (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign (BN) or malignant (MN) involves either repeated scans or invasive procedures that sample the lung tissue. Non-invasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. MN samples were predominantly Stage 1 by pathologic diagnosis and 97% of BN were confirmed by four years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data was then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC) which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed 3 clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, non-invasive method for the diagnosis of pulmonary nodules in non-small cell lung cancer patients.

MicroRNAs (miRNAs) remain stable in circulation and have been identified as potential biomarkers for a variety of conditions. We report miRNA changes in blood from multiple rodent models of pain, including spinal nerve ligation and spared nerve injury models of neuropathic pain; a complete Freund's adjuvant (CFA) model of inflammatory pain; and a chemotherapy-induced model of pain using the histone deacetylase inhibitor JNJ-26481585. The effect of celecoxib, a cyclooxygenase-2-selective nonsteroidal anti-inflammatory drug, was investigated in the CFA model as proof of principle for assessing the utility of circulating miRNAs as biomarkers in determining treatment response. Each study resulted in a unique miRNA expression profile. Despite differences in miRNAs identified from various models, computational target prediction and functional enrichment have identified biological pathways common among different models. The Wnt signaling pathway was affected in all models, suggesting a crucial role for this pathway in the pathogenesis of pain. Our studies demonstrate the utility of circulating miRNAs as pain biomarkers and suggest the potential for rigorous forward and reverse translational approaches. Evaluating alterations in miRNA fingerprints under different pain conditions and after administering therapeutic agents may be beneficial in evaluating clinical trial outcomes, predicting treatment response, and developing correlational outcomes between preclinical and human studies.

BackgroundSets of genes that are known to be associated with each other can be used to interpret microarray data. This gene set approach to microarray data analysis can illustrate patterns of gene expression which may be more informative than analyzing the expression of individual genes. Various statistical approaches exist for the analysis of gene sets. There are three main classes of these methods: over-representation analysis, functional class scoring, and pathway topology based methods.MethodsWe propose weighted hypergeometric and weighted chi-squared methods in order to assign a rank to the degree to which each gene participates in the enrichment. Each gene is assigned a weight determined by the absolute value of its log fold change, which is then raised to a certain power. The power value can be adjusted as needed. Datasets from the Gene Expression Omnibus are used to test the method. The significantly enriched pathways are validated through searching the literature in order to determine their relevance to the dataset.ResultsAlthough these methods detect fewer significantly enriched pathways, they can potentially produce more relevant results. Furthermore, we compare the results of different enrichment methods on a set of microarray studies all containing data from various rodent neuropathic pain models.DiscussionOur method is able to produce more consistent results than other methods when evaluated on similar datasets. It can also potentially detect relevant pathways that are not identified by the standard methods. However, the lack of biological ground truth makes validating the method difficult.

In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R.  SVM-RCE-R, further enhances the capabilities of  SVM-RCE by the addition of  a novel user specified ranking function. This ranking function enables the user to  stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area  under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify microRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics.