Rehman Qureshi

MicroRNA modulation in complex regional pain syndrome

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.

Functional significance of macrophage-derived exosomes in inflammation and pain

Summary Macrophage‐derived exosomes attenuated complete Freunds adjuvant‐induced thermal hyperalgesia in mice. Exosomal microRNA signature from patients with complex regional pain syndrome suggests a potential therapeutic and biomarker utility for exosomes. ABSTRACT 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 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 lipopolysaccharide‐stimulated cells were sufficient to cause nuclear factor‐&kgr;B activation in naive cells, indicating functionality in recipient cells. A single injection of exosomes attenuated thermal hyperalgesia in a murine model of inflammatory pain, suggesting an immunoprotective role for macrophage‐derived exosomes. Macrophage‐derived exosomes carry a protective signature that is altered when secreting cells are exposed to an inflammatory stimulus. We also show that circulating miRNAs altered in patients with complex regional pain syndrome are trafficked by exosomes. 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.

A novel method for the normalization of microRNA RT-PCR data

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.

Weighted set enrichment of gene expression data

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.

Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression

Time-series microarray data can capture dynamic genomic behavior not available in steady-state expression data, which has made time-series analysis especially useful in the study of dynamic cellular processes such as the circadian rhythm, disease progression, drug response, and the cell cycle. Using the information available in the time-series data, we address three related computational problems: the prediction of gene expression levels from previous time steps, the simulation of an entire time-series microarray dataset, and the reconstruction of gene regulatory networks. We model the gene expression levels using a linear model, due to its simplicity and the ability to interpret the coefficients as interactions in the underlying regulatory network. A stepwise multiple linear regression method is applied to fit the parameters of the linear model to a given training dataset. The learned model is utilized in predicting and replicating the time course of the expression levels and in identifying the regulatory interactions. Each predicted interaction is also associated with a statistical significance to provide a confidence measure that can guide prioritization in further costly manual or experimental verification. We demonstrate the performance of our approach on several yeast cell-cycle datasets and show that it provides comparable or greater accuracy than existing methods and provides additional quantitative information about the interactions not available from the other methods.

Analgesic Response to Intravenous Ketamine Is Linked to a Circulating microRNA Signature in Female Patients With Complex Regional Pain Syndrome

Although ketamine is beneficial in treating complex regional pain syndrome (CRPS), a subset of patients respond poorly to therapy. We investigated treatment-induced microRNA (miRNA) changes and their predictive validity in determining treatment outcome by assessing miRNA changes in whole blood from patients with CRPS. Blood samples from female patients were collected before and after 5 days of intravenous ketamine administration. Seven patients were responders and 6 were poor responders. Differential miRNA expression was observed in whole blood before and after treatment. In addition, 33 miRNAs differed between responders and poor responders before therapy, suggesting the predictive utility of miRNAs as biomarkers. Investigation of the mechanistic significance of hsa-miR-548d-5p downregulation in poor responders showed that this miRNA can downregulate UDP-glucuronosyltransferase UGT1A1 mRNA. Poor responders had a higher conjugated/unconjugated bilirubin ratio, indicating increased UGT1A1 activity. We propose that lower pretreatment levels of miR-548d-5p may result in higher UDP-GT activity, leading to higher levels of inactive glucuronide conjugates, thereby minimizing the therapeutic efficacy of ketamine in poor responders. Differences in miRNA signatures can provide molecular insights distinguishing responders from poor responders. Extending this approach to other treatment and outcome assessments might permit stratification of patients for maximal therapeutic outcome. Perspective: This study suggests the usefulness of circulating miRNAs as potential biomarkers. Assessing miRNA signatures before and after treatment demonstrated miRNA alterations from therapy; differences in miRNA signature in responders and poor responders before therapy indicate prognostic value. Mechanistic studies on altered miRNAs can provide new insights into disease.

Reconstruction of gene regulatory networks by stepwise multiple linear regression from time-series microarray data

Gene regulatory networks provide a powerful abstraction of the complex interactions among genes involved in functional pathways. Experimental determination of these interactions using a classical experimental method, although of extreme value, is laborious and prohibitive at large scales. Over the last decade, a number of computational approaches have been developed to infer gene regulatory networks from high-throughput experimental data. In this study, we introduce a new algorithm for regulatory network inference, based on stepwise multiple regression of time-series microarray data. Compared to other existing methods, our regression-based method provides a clear interpretation of the inferred interactions. The statistical significance associated with each prediction can be utilized to rank the interactions, which is important in prioritization of predictions for further experimental verification. We demonstrate the performance of our approach on a well-known yeast cell cycle pathway and show that it makes more accurate predictions than existing methods.

A weighted hypergeometric statistic for the enrichment of gene sets

Microarray data can be analyzed by observing the activity of groups of genes; this approach can provide more relevant information than the analysis of individual gene expression. There are numerous statistical methods available for the enrichment of gene sets; however, these methods often fail to identify relevant gene sets due to the noisy nature of the data. We present weighted hypergeometric and weighted chi-squared methods that rank each genes contribution to enrichment by the absolute value of the logarithm of its fold change. We demonstrate that these methods can produce more biologically relevant results than the standard hypergeometric test, despite being more conservative and enriching fewer pathways with significant p-values.

Analysis of Paired miRNA-mRNA Microarray Expression Data Using a Stepwise Multiple Linear Regression Model

MicroRNAs are small endogenous RNAs that play important roles in gene regulation. With the accumulation of expression data, numerous approaches have been proposed to infer miRNA-mRNA regulation from paired miRNA-mRNA expression profiles. These mainly focus on discovering and validating the structure of regulatory networks, but do not address the prediction and simulation tasks. Furthermore, functional annotation of miRNAs relies on miRNA target prediction, which is problematic since miRNA-gene interactions are highly tissue-specific. Thus a different approach to functional annotation of miRNA-mRNA regulation that can generate context-specific expression levels is needed. In this study, we analyzed paired miRNA-mRNA expressions from breast cancer studies. The expression of mRNAs is modeled as a multiple linear function of the expression of miRNAs and the parameters are estimated using stepwise multiple linear regression (SMLR). We demonstrate that the SMLR model can predict mRNA expression patterns from miRNA expressions alone and that the predicted gene expression levels preserve differentially regulated gene sets, as well as the functional categories of these genes. We show that our quantitative approach can determine affected biological activities better than the traditional target-prediction based methods.