The Role of MicroRNA Regulation of Cardiac Ion Channel in Arrhythmia

La fibrillation auriculaire (FA) est le trouble du rythme le plus fréquemment observé en pratique clinique. Elle constitue un risque important de morbi-mortalité. Le traitement de la FA reste un défi majeur en lien avec les nombreux effets secondaires associés aux approches thérapeutiques actuelles. Dans ce contexte, une meilleure compréhension des mécanismes sous-jacents à la FA est essentielle pour le développement de nouvelles thérapies offrant un meilleur rapport bénéfice/risque pour les patients. La FA est caractérisée par i) un remodelage électrique délétère associé le plus souvent ii) à un remodelage structurel du myocarde favorisant la récurrence et le maintien de l’arythmie. La diminution de la période réfractaire effective au sein du tissu auriculaire est un élément clef du remodelage électrique. Le remodelage structurel, quant à lui, se manifeste principalement par une fibrose tissulaire qui altère la propagation de l’influx électrique dans les oreillettes. Les mécanismes moléculaires impliqués dans la mise en place de ces deux substrats restent mal connus. Récemment, le rôle des microARNs (miARNs) a été pointé du doigt dans de nombreuses pathologies notamment cardiaques. Dans ce contexte les objectifs principaux de ce travail ont été i) d'acquérir une compréhension approfondie du rôle des miARNs dans la régulation de l’expression des canaux ioniques et ii) de mieux comprendre le rôle de ces molécules dans l’installation d’un substrat favorable a la FA. Nous avons, dans un premier temps, effectué une analyse bio-informatique combinée à des approches expérimentales spécifiques afin d’identifier clairement les miARNs démontrant un fort potentiel de régulation des gènes codant pour l’expression des canaux ioniques cardiaques humains. Nous avons identifié un nombre limité de miARNs cardiaques qui possédaient ces propriétés. Sur la base de ces résultats, nous avons démontré que l’altération de l'expression des canaux ioniques, observée dans diverse maladies cardiaques (par exemple, les cardiomyopathies, l’ischémie myocardique, et la fibrillation auriculaire), peut être soumise à ces miARNs suggérant leur implication dans l’arythmogénèse. La régulation du courant potassique IK1 est un facteur déterminant du remodelage électrique auriculaire associée à la FA. Les mécanismes moléculaires sous-jacents sont peu connus. Nous avons émis l’hypothèse que l'altération de l’expression des miARNs soit corrélée à l’augmentation de l’expression d’IK1 dans la FA. Nous avons constaté que l’expression de miR-26 est réduite dans la FA et qu’elle régule IK1 en modulant l’expression de sa sous-unité Kir2.1. Nous avons démontré que miR-26 est sous la répression transcriptionnelle du facteur nucléaire des lymphocytes T activés (NFAT) et que l’activité accrue de NFATc3/c4, aboutit à une expression réduite de miR-26. En conséquence IK1 augmente lors de la FA. Nous avons enfin démontré que l’interférence in vivo de miR-26 influence la susceptibilité à la FA en régulant IK1, confirmant le rôle prépondérant de miR-26 dans le remodelage auriculaire électrique. La fibrose auriculaire est un constituant majeur du remodelage structurel associé à la FA, impliquant l'activation des fibroblastes et l’influx cellulaire du Ca2 +. Nous avons cherché à déterminer i) si le canal perméable au Ca2+, TRPC3, jouait un rôle dans la fibrose auriculaire en favorisant l'activation des fibroblastes et ii) étudié le rôle potentiel des miARNs dans ce contexte. Nous avons démontré que les canaux TRPC3 favorisent l’influx du Ca2 +, activant la signalisation Ca2 +-dépendante ERK et en conséquence activent la prolifération des fibroblastes. Nous avons également démontré que l’expression du TRPC3 est augmentée dans la FA et que le blocage in vivo de TRPC3 empêche le développement de substrats reliés à la FA. Nous avons par ailleurs validé que miR-26 régule les canaux TRPC3 en diminuant leur expression dans les fibroblastes. Enfin, nous avons montré que l'expression réduite du miR-26 est également due à l’activité augmentée de NFATc3/c4 dans les fibroblastes, expliquant ainsi l’augmentation de TRPC3 lors de la FA, confirmant la contribution de miR-26 dans le processus de remodelage structurel lié à la FA. En conclusion, nos résultats mettent en évidence l'importance des miARNs dans la régulation des canaux ioniques cardiaques. Notamment, miR-26 joue un rôle important dans le remodelage électrique et structurel associé à la FA et ce, en régulant IK1 et l’expression du canal TRPC3. Notre étude démasque ainsi un mécanisme moléculaire de contrôle de la FA innovateur associant des miARNs. miR-26 en particulier représente apres ces travaux une nouvelle cible thérapeutique prometteuse pour traiter la FA.

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Binaural prediction of speech intelligibility in reverberant rooms with multiple noise sources

When speech is in competition with interfering sources in rooms, monaural indicators of intelligibility fail to take account of the listener’s abilities to separate target speech from interfering sounds using the binaural system. In order to incorporate these segregation abilities and their susceptibility to reverberation, Lavandier and Culling [J. Acoust. Soc. Am. 127, 387–399 (2010)] proposed a model which combines effects of better-ear listening and binaural unmasking. A computationally efficient version of this model is evaluated here under more realistic conditions that include head shadow, multiple stationary noise sources, and real-room acoustics. Three experiments are presented in which speech reception thresholds were measured in the presence of one to three interferers using real-room listening over headphones, simulated by convolving anechoic stimuli with binaural room impulse-responses measured with dummy-head transducers in five rooms. Without fitting any parameter of the model, there was close correspondence between measured and predicted differences in threshold across all tested conditions. The model’s components of better-ear listening and binaural unmasking were validated both in isolation and in combination. The computational efficiency of this prediction method allows the generation of complex “intelligibility maps” from room designs. © 2012 Acoustical Society of America

Automatic prediction of functional site regions in low-resolution protein structures

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.

The potential of 1 h refractivity changes from an operational C-band magnetron-based radar for numerical weather prediction validation and data assimilation

Refractivity changes (ΔN) derived from radar ground clutter returns serve as a proxy for near-surface humidity changes (1 N unit ≡ 1% relative humidity at 20 °C). Previous studies have indicated that better humidity observations should improve forecasts of convection initiation. A preliminary assessment of the potential of refractivity retrievals from an operational magnetron-based C-band radar is presented. The increased phase noise at shorter wavelengths, exacerbated by the unknown position of the target within the 300 m gate, make it difficult to obtain absolute refractivity values, so we consider the information in 1 h changes. These have been derived to a range of 30 km with a spatial resolution of ∼4 km; the consistency of the individual estimates (within each 4 km × 4 km area) indicates that ΔN errors are about 1 N unit, in agreement with in situ observations. Measurements from an instrumented tower on summer days show that the 1 h refractivity changes up to a height of 100 m remain well correlated with near-surface values. The analysis of refractivity as represented in the operational Met Office Unified Model at 1.5, 4 and 12 km grid lengths demonstrates that, as model resolution increases, the spatial scales of the refractivity structures improve. It is shown that the magnitude of refractivity changes is progressively underestimated at larger grid lengths during summer. However, the daily time series of 1 h refractivity changes reveal that, whereas the radar-derived values are very well correlated with the in situ observations, the high-resolution model runs have little skill in getting the right values of ΔN in the right place at the right time. This suggests that the assimilation of these radar refractivity observations could benefit forecasts of the initiation of convection.

Why the hand motion proceeds the target in tracking experiment?

In the present study, to shed light on a role of positional error correction mechanism and prediction mechanism in the proactive control discovered earlier, we carried out a visual tracking experiment, in which the region where target was shown, was regulated in a circular orbit. Main results found in this research were following. Recognition of a time step, obtained from the environmental stimuli, is required for the predictive function. The period of the rhythm in the brain obtained from environmental stimuli is shortened about 10%, when the visual information is cut-off. The shortening of the period of the rhythm in the brain accelerates the motion as soon as the visual information is cut-off, and lets the hand motion precedes the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand precedes in average the target when the predictive mechanism dominates the error-corrective mechanism.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection

Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0), and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection candidates). Lower correlations between genomic-based estimates of breeding values (GEBV) and true breeding values (TBV) were obtained when using the Bottom strategy. For Random, Extreme, and Less Related strategies, the correlation between GEBV and TBV became slightly larger as selection intensity decreased and was largest when no selection occurred. These 3 strategies were better than the Top approach. In addition, the Extreme, Random, and Less Related strategies had smaller predictive mean squared errors (PMSE) followed by the Top and Bottom methods. Overall, the Extreme genotyping strategy led to the best predictive ability of breeding values, indicating that animals with extreme yield deviations values in a reference population are the most informative when training genomic selection models.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

MicroRNA 21 is a homeostatic regulator of macrophage polarization and prevents prostaglandin E-2-mediated M2 generation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Prediction of apparent protein digestibility of ingredients and diets by in vitro pH-stat degree of protein hydrolysis with species-specific enzymes for juvenile Pacific white shrimp Litopenaeus vannamei

Aquafeed production faces global issues related to availability of feed ingredients. Feed manufacturers require greater flexibility in order to develop nutritional and cost-effective formulations that take into account nutrient content and availability of ingredients. The search for appropriate ingredients requires detailed screening of their potential nutritional value and variability at the industrial level. In vitro digestion of feedstuffs by enzymes extracted from the target species has been correlated with apparent protein digestibility (APD) in fish and shrimp species. The present study verified the relationship between APD and in vitro degree of protein hydrolysis (DH) with Litopenaeus vannamei hepatopancreas enzymes in several different ingredients (n = 26): blood meals, casein, corn gluten meal, crab meal, distiller`s dried grains with solubles, feather meal, fish meals, gelatin, krill meals, poultry by-product meal, soybean meals, squid meals and wheat gluten. The relationship between APD and DH was further verified in diets formulated with these ingredients at 30% inclusion into a reference diet. APD was determined in vivo (30.1 +/- 0.5 degrees C, 32.2 +/- 0.4%.) with juvenile L vannamei (9 to 12 g) after placement of test ingredients into a reference diet (35 g kg(-1) CP: 8.03 g kg(-1) lipid; 2.01 kcal g(-1)) with chromic oxide as the inert marker. In vitro DH was assessed in ingredients and diets with standardized hepatopancreas enzymes extracted from pond-reared shrimp. The DH of ingredients was determined under different assay conditions to check for the most suitable in vitro protocol for APD prediction: different batches of enzyme extracts (HPf5 or HPf6), temperatures (25 or 30 degrees C) and enzyme activity (azocasein): crude protein ratios (4 U: 80 mg CP or 4 U: 40 mg CP). DH was not affected by ingredient proximate composition. APD was significantly correlated to DH in regressions considering either ingredients or diets. The relationships between APD and DH of the ingredients could be suitably adjusted to a Rational Function (y = (a + bx)/(1 + cx + dx2), n = 26. Best in vitro APD predictions were obtained at 25 degrees C, 4 U: 80 mg CP both for ingredients (R(2) = 0.86: P = 0.001) and test diets (R(2) = 0.96; P = 0.007). The regression model including all 26 ingredients generated higher prediction residuals (i.e., predicted APD - determined APD) for corn gluten meal, feather meal. poultry by-product meal and krill flour. The remaining test ingredients presented mean prediction residuals of 3.5 points. A model including only ingredients with APD>80% showed higher prediction precision (R(2) = 0.98: P = 0.000004; n = 20) with average residual of 1.8 points. Predictive models including only ingredients from the same origin (e.g., marine-based, R(2) = 0.98; P = 0.033) also displayed low residuals. Since in vitro techniques have been usually validated through regressions against in vivo APD, the DH predictive capacity may depend on the consistency of the in vivo methodology. Regressions between APD and DH suggested a close relationship between peptide bond breakage by hepatopancreas digestive proteases and the apparent nitrogen assimilation in shrimp, and this may be a useful tool to provide rapid nutritional information. (C) 2009 Elsevier B.V. All rights reserved.

Swimming Training in Rats Increases Cardiac MicroRNA-126 Expression and Angiogenesis

DA SILVA, N. D. JR, T. FERNANDES, U. P. R. SOCI, A. W. A. MONTEIRO, M. I. PHILLIPS, and E. M. DE OLIVEIRA. Swimming Training in Rats Increases Cardiac MicroRNA-126 Expression and Angiogenesis. Med. Sci. Sports Exerc., Vol. 44, No. 8, pp. 1453-1462, 2012. Purpose: MicroRNA (miRNA)-126 is angiogenic and has two validated targets: Sprouty-related protein 1 (Spred-1) and phosphoinositol-3 kinase regulatory subunit 2 (PI3KR2), negative regulators of angiogenesis by VEGF pathway inhibition. We investigated the role of swimming training on cardiac miRNA-126 expression related to angiogenesis. Methods: Female Wistar rats were assigned to three groups: sedentary (S), training 1 (T1, moderate volume), and training 2 (T2, high volume). T1 consisted of 60 min.d(-1) of swimming, five times per week for 10 wk with 5% body overload. T2 consisted of the same protocol of T1 until the eighth week; in the ninth week, rats trained for two times a day, and in the 10th week, rats trained for three times a day. MiRNA and PI3KR2 gene expression analysis was performed by real-time polymerase chain reaction in heart muscle. We assessed markers of training, the cardiac capillary-fiber ratio, cardiac protein expression of VEGF, Spred-1, Raf-1/ERK 1/2, and PI3K/Akt/eNOS. Results: The cardiac capillary-fiber ratio increased in T1 (58%) and T2 (101%) compared with S. VEGF protein expression was increased 42% in T1 and 108% in T2. Cardiac miRNA-126 expression increased 26% (T1) and 42% (T2) compared with S, correlated with angiogenesis. The miRNA-126 target Spred-1 protein level decreased 41% (T1) and 39% (T2), which consequently favored an increase in angiogenic signaling pathway Raf-1/ERK 1/2. On the other hand, the gene expression of PI3KR2, the other miRNA-126 target, was reduced 39% (T1) and 78% (T2), and there was an increase in protein expression of components of the PI3K/Akt/eNOS signaling pathway in the trained groups. Conclusions: This study showed that aerobic training promotes an increase in the expression of miRNA-126 and that this may be related to exercise-induced cardiac angiogenesis, by indirect regulation of the VEGF pathway and direct regulation of its targets that converged in an increase in angiogenic pathways, such as MAPK and PI3K/Akt/eNOS.

Stage, Grade and Behavior of Bladder Urothelial Carcinoma Defined by the MicroRNA Expression Profile

Purpose: We identified miRNA expression profiles in urothelial carcinoma that are associated with grade, stage, and recurrence-free and disease specific survival. Materials and Methods: The expression of 14 miRNAs was evaluated by quantitative reverse transcriptase-polymerase chain reaction in surgical specimens from 30 patients with low grade, noninvasive (pTa) and 30 with high grade, invasive (pT2-3) urothelial carcinoma. Controls were normal bladder tissue from 5 patients who underwent surgical treatment for benign prostatic hyperplasia. Endogenous controls were RNU-43 and RNU-48. miRNA profiles were compared and Kaplan-Meier curves were constructed to analyze disease-free and disease specific survival. Results: miR-100 was under expressed in 100% of low grade pTa specimens (p <0.001) and miR-10a was over expressed in 73.3% (p <0.001). miR-21 and miR-205 were over expressed in high grade pT2-3 disease (p = 0.02 and <0.001, respectively). The other miRNAs were present at levels similar to those of normal bladder tissue or under expressed in each tumor group. miR-21 over expression (greater than 1.08) was related to shorter disease-free survival in patients with low grade pTa urothelial carcinoma. Higher miR-10a levels (greater than 2.30) were associated with shorter disease-free and disease specific survival in patients with high grade pT2-3 urothelial carcinoma. Conclusions: Four miRNAs were differentially expressed in the 2 urothelial carcinoma groups. miR-100 and miR-10a showed under expression and over expression, respectively, in low grade pTa tumors. miR-21 and miR-205 were over expressed in pT2-3 disease. In addition, miR-10a and miR-21 over expression was associated with shorter disease-free and disease specific survival. miRNAs could be incorporated into the urothelial carcinoma molecular pathway. These miRNAs could also serve as new diagnostic or prognostic markers and new target drugs.