Ahmet Sacan

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.

CellTrack: an open-source software for cell tracking and motility analysis

MOTIVATION Cell motility is a critical part of many important biological processes. Automated and sensitive cell tracking is essential to cell motility studies where the tracking results can be used for diagnostic or curative decisions and where mathematical models can be developed to deepen our understanding of the mechanisms underlying cell motility. RESULTS We have developed CellTrack: a self-contained, extensible, and cross-platform software package for cell tracking and motility analysis. Besides the general purpose image enhancement, object segmentation and tracking algorithms, we have implemented a novel edge-based method for sensitive tracking of the cell boundaries, and constructed an ensemble of methods that achieves refined tracking results even under large displacements or deformations of the cells. AVAILABILITY CellTrack is an Open Source project and is freely available at http://db.cse.ohio-state.edu/CellTrack.

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.

Comparing the yeast retrograde response and NF-κB stress responses: implications for aging.

The mitochondrial retrograde response has been extensively described in Saccharomyces cerevisiae, where it has been found to extend life span during times of mitochondrial dysfunction, damage or low nutrient levels. In yeast, the retrograde response genes (RTG) convey these stress responses to the nucleus to change the gene expression adaptively. Similarly, most classes of higher organisms have been shown to have some version of a central stress‐mediating transcription factor, NF‐κB. There have been several modifications along the phylogenetic tree as NF‐κB has taken a larger role in managing cellular stresses. Here, we review similarities and differences in mechanisms and pathways between RTG genes in yeast and NF‐κB as seen in more complex organisms. We perform a structural homology search and reveal similarities of Rtg proteins with eukaryotic transcription factors involved in development and metabolism. NF‐κB shows more sophisticated functions when compared to RTG genes including participation in immune responses and induction of apoptosis under high levels of ROS‐induced mitochondrial and nuclear DNA damage. Involvement of NF‐κB in chromosomal stability, coregulation of mitochondrial respiration, and cross talk with the TOR (target of rapamycin) pathway points to a conserved mechanism also found in yeast.

Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method

Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure data sets that span various structural folds and represent different protein similarity levels. A web-based alignment tool, a downloadable executable, and detailed alignment results for the data sets used here are available at http://sacan.biomed. drexel.edu/Smolign and http://bio.cse.ohio-state.edu/Smolign.

Integrated search and alignment of protein structures

MOTIVATION Identification and comparison of similar three-dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here, we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The contact hits obtained from a distance-based indexing method are extended to obtain high-scoring segment pairs, which are then used to generate structural alignments. RESULTS Vorometric is the first to address both search and alignment problems in the protein structure databases. The experimental results show that Vorometric is simultaneously effective in retrieving similar protein structures, producing high-quality structure alignments, and identifying cross-fold similarities. Vorometric outperforms current structure retrieval methods in search accuracy, while requiring com-parable running times. Furthermore, the structural superpositions produced are shown to have better quality and coverage, when compared with those of the popular structure alignment tools. AVAILABILITY Vorometric is available as a web service at http://bio.cse.ohio-state.edu/Vorometric

LFM-Pro: a tool for detecting significant local structural sites in proteins

MOTIVATION The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features. RESULTS We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features. AVAILABILITY The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.

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.