A major issue in the widespread controversy about the legality of poker and the appropriate taxation of winnings is whether poker should be considered a game of skill or a game of chance. To inform this debate we present an analysis into the role of skill in the performance of online poker players, using a large database with hundreds of millions of player-hand observations from real money ring games at three different stakes levels. We find that players whose earlier profitability was in the top (bottom) deciles perform better (worse) and are substantially more likely to end up in the top (bottom) performance deciles of the following time period. Regression analyses of performance on historical performance and other skill-related proxies provide further evidence for persistence and predictability. Simulations point out that skill dominates chance when performance is measured over 1,500 or more hands of play. Poker is the most popular card game in the world. Every day, hundreds of thousands of people play poker for real money on the Internet (Online Poker Traffic Reports). In 2013, online poker rooms generated approximately €2.8 billion in gross win . The popularity of the game is also evidenced by the many TV reports of major poker tournaments and the number of participants in these tournaments. In 2014, for example, 6,683 people paid $10,000 to participate in the most renowned poker tournament, the Main Event of the World Series of Poker in Las Vegas.At the same time, there is a widespread controversy about the legality of poker and the appropriate taxation of winnings. A key issue in the debate is whether poker is to be considered a game of chance or a game of skill. Unlike with games of skill, organizing or playing a game of chance is prohibited or restricted in many countries. Also, many countries have a separate gam(bl)ing tax for games of chance, while money won in a game of skill is generally subject to regular income tax. Kelly, Dhar and Verbiest  map legiAuthorities often have a less permissive stance towards online poker than towards live poker. In the US, for example, the Unlawful Internet Gambling Enforcement Act (UIGEA) that was adopted in 2006 had a major impact: although the act did not forbid online gambling, it did prohibit the transfer of funds to and from online gambling businesses. As depositing money is necessary for playing online poker, this act effectively declared online poker illegal. If poker were to be considered as a skill game and not as gambling, this could be an argument to exempt the online poker business from the UIGEA .et al. [Two different research tracks have examined the skill component in poker. One track focuses on developing and calculating measures of skill, and can be traced back to Kadane . Borm anet al.  propose et al.  reviews et al. [The second track of studies takes a more empirically oriented approach. Likewise, these papers suggest that poker involves a skill component. Larkey et al.  and Caboet al.  ran larget al.  carried et al.  analyzesa priori classified as being especially skilled indeed outperformed the other players.In the same spirit as some of the analyses in the present paper, Croson, Fishman and Pope  and LeviIn what follows, we analyze the role of skill in the performance of online poker players, using a large database with 456 million player-hand observations from real money ring games at three different stakes levels. Online poker seems to be the most obvious data source, because the chance-skill debate is especially oriented towards issues regarding the legality of internet poker and the taxation of winnings from online play. Moreover, the vast amount of data that are available allow for powerful analyses.We define skill as anything that affects a player’s performance other than chance. In a pure game of chance, each player’s expected winnings are zero (in absence of costs) and there normally is no persistence or positive autocorrelation in their performance: players’ performance over a given period is independent of that over any other period. If performance is predictable, the game involves elements of skill.Our results indicate that skill is an important factor. When we split our sample into subperiods, we find that players whose performance was in the top (bottom) deciles of the previous period perform better (worse) and are more likely to end up in the top (bottom) deciles of the current period. Regression analyses of performance on past performance and other skill proxies reinforce this evidence of persistence in performance.From a legal viewpoint, the key question is whether skill dominates chance, that is, whether poker is more a game of skill than a game of chance. The answer to this question heavily depends on the duration and intensity of play, as the effect of chance diminishes with the number of hands and eventually cancels out in the long run.  Our simulations point out that skill predominates after approximately 1,500 hands.For our analyses we use data on real money ring games (“cash games”) played at one of the major online poker sites. We consider No Limit (NL) Texas Hold’em only because this variant is by far the most popular form of poker worldwide. Our data come from an online service called HHDealer. In recent years, several companies have specialized in gathering and trading so-called “hand histories” from online poker rooms. With software applications they continuously collect information on hands played at online poker tables. Many players buy these data to have information on the playing styles of others. Because of limited resources, hand history providers are unable to store data on every hand that is played online. Out of the websites that responded to our inquiries, HHDealer was able to provide the largest number of hands for an uninterrupted period of twelve months. We purchased all available data for the games that had been played at three particular stakes levels in the period October 2009 – September 2010. In poker, stakes levels are distinguished by the size of the small and the big blind bet. To ground our analysis on distinct stakes levels, we selected data from so-called “low”, “medium” and “high stakes” games, with big blind sizes of $0.25, $2 and $10, respectively. For the medium stakes level, the data that we received also contained hands played in September 2009; we treat these as if they were played in October 2009.The resulting raw data set contains a total of 76.9 million different hands. The average number of players participating in a hand is 5.9, yielding 456.1 million different player-hand observations. Of these, 190.6 million (41.8%) are from the low stakes games, 229.1 million (50.2%) are from the medium stakes, and 36.4 million (8.0%) are from the high stakes. The smallest number of observations recorded in a month was in February 2010 , and relates to a software change that temporarily made data mining more difficult. The peak was in January 2010 .Players who participated in the high stakes game played on average 1,085 hands at that particular level. For the medium and small stakes this number is 996 and 417, respectively. The average number of hands played at the three levels combined is 746. There is much variation across players in the number of hands that they played at the stakes we have selected. One exceptional player was involved in approximately 765,000 hands (0.17% of our sample), while 58.9 percent of all players participated in less than one hundred hands. The degree of concentration is high: the one percent most active players played 58.5 percent of all hands, and 12.0 percent played 90 percent. Most likely, many of the infrequent players were active at other stakes levels than the three included here.On average, players lost 104 bb/100 after charging of rake. This average win rate is much worse than the ratio of the average total number of big blinds lost (49) and hands played (746), or 6.6 bb/100. The difference is explained by a positive relation between a player’s profitability and the number of hands that she played. This relation may reflect the effect of experience, but can also be a consequence of budget constraints becoming an obstacle after losses.Rake substantially affects players’ winnings. If a hand is not finished in the first betting round (“pre-flop”), the operator takes a fixed percentage (5% for our data) from the pot with a fixed nominal cap that depends on the number of players at the table. Only 32 percent of all players in our sample achieved a positive overall result after the deduction of rake. In reality, this percentage is slightly higher: players can easily participate in reward schemes and receive deposit bonuses that partly make up for it, and they can enter into so-called “rakeback deals” with affiliates of the operator.For our analyses of the role of skill in performance we correct for rake, because we do not want our findings to be conditional on the rake structure that is employed by the operator. Rake is not an intrinsic element of the game, and the percentages and caps differ across sites. Moreover, as explained above, the amount of rake that a player effectively pays is not observable.To correct players’ winnings for rake, we add back the rake in proportion to their contributions to the pot. On average, rake reduces players’ performance by 16 bb/100 in our sample. As a result of the fixed nominal cap, the effect of rake on players’ win rates is larger for games with smaller stakes. In the absence of rake, 37.5 percent of all players would have made a profit. The extreme values for the best and worst win rate in the table were recorded for lucky and unlucky players who played only one or two hands.Under the null hypothesis that poker is a game of chance alone, there is no relation between a player’s performance scores across different subperiods. Alternatively, if skill plays a material role in the game of poker, we would expect a player’s performance in one particular subperiod to be indicative of her performance in later subperiods. In this section we subdivide players into deciles based on their performance in the first six months of our sample period and examine how the players in these deciles fared in the last six months. In the next section we look at the persistence and predictability of performance through regression analysis.Our sample period covers twelve consecutive months. We split up our data into the subsamples October 2009 March 2010 and April September 2010, and rank the different players into deciles according to the average number of big blinds they have won per hand across the first period (the “ranking” period). Because small collections of hands are likely to yield very noisy indicators, we filter out players who have played less than 1,000 hands during this ranking period. This leaves a sample of 17,257 players for the small stakes, 16,435 for the medium stakes, and 2,725 for the high stakes. A total of 36,570 players participated in 1,000 or more hands at the three levels combined. On average, they played 5,706 hands each . Next, we examine the average performance of the various deciles of players over the second period of six months (the “measurement” period). To prevent selection effects, we impose no restriction on the number of hands in this measurement period. As explained in the previous section, hand outcomes are corrected for rake and scaled by the size of the big blind.In a nutshell, the results in t = 3.12; p = 0.002). Across all deciles, the Spearman rank correlation between the average decile performances in the ranking period and those in the measurement period is marginally significant . At the individual stakes levels, the correlation coefficient is not significantly different from zero for the small stakes, and marginally significant for both the medium and the high stakes.We first discuss the results where measurement-period decile performance is calculated as the unweighted average performance across players. In general, players from higher-ranked deciles outperform players from lower-ranked deciles. For example, the average player from the top decile for the three stakes levels combined lost 23.0 bb/100, while the average player from the bottom decile lost 42.5 bb/100; the difference of 19.5 bb/100 is statistically significant  recorded a score of −79.9 bb/100, while the others (81.1%) recorded −4.6 bb/100 on average.The substantial share of players who played relatively few hands in the measurement period may also explain why the decile-level correlation between the average performances in the ranking and measurement period is only marginally significant. Performance measurements for infrequent players are relatively noisy, and their widely varying scores consequently distort the strength of the correlation. In fact, players who played only a few hands are given a questionably large weight when decile performance is expressed as a straight average across players. Using a weighted average with players’ number of hands as weights would avoid this problem, and we therefore propose this measure as an alternative indicator. This weighted average is identical to the average profitability per hand across all hands played by the players in a decile combined. Because players who played only infrequently are hardly reflected in this alternative measure, we also consider a compromise weighting method that uses the square roots of players’ numbers of hands as weights.t = 12.36; p < 0.001). The Spearman rank correlations across the deciles between performances in the two periods are higher with weighted than with unweighted average scores and always statistically significant – both for the three individual stakes levels and for the three levels combined . Note that the measurement-period performance is positive for most deciles when players’ numbers of hands are used as weights. This is striking, because, by definition, the average winnings per hand are zero across all hands in our unfiltered sample. Apparently, players who played 1,000 or more hands in the prior six months (and thus satisfied our selection criterion) played more profitably than others. This, in itself, might indicate that experience pays off in this game.Evaluating on the basis of the two weighted average performance measures strengthens the pattern observed. Players from higher-ranked deciles again outperform players from lower-ranked deciles in Period 2. For example, hands played by players in the top decile yielded a profit of 5.1 bb/100 across all stakes levels, while hands played by bottom decile players lead to a loss of 5.9 bb/100 .At the individual player level, the strength of the correlation between players’ Period 1 and Period 2 ranks is rather moderate. The correlation coefficient ranges between 0.074  and 0.104 (for the medium stakes). The relatively low degree of correlation as compared to the correlation coefficients at the decile level reflects the relevance of variance in performance at the individual level—in particular of the variance for players who played only few hands in Period 2. Statistically, however, the rank correlation at the individual player level is highly significant for every (sub)sample . Regardless of the pair of successive quarters that we use for ranking and measuring, we observe the same pattern of persistence as before: higher-ranked deciles generally outperform lower-ranked deciles. Again, the correlations are stronger when we reduce the influence of relatively infrequent players by calculating performance as a weighted average, and at the individual player level the rank correlation is always highly significant.ni): the greater the number of observations, the less likely that the mean takes an extreme value. For example, if we consider two players with equal ability from a larger population, the player who participates in a smaller number of hands is more likely to be classified in one of the top or bottom deciles if players are ranked by their average winnings per hand. Similarly, the previous approach does not account for differences in playing style or the standard deviation of winnings (si): when two players are equally profitable, the more adventurous player is more likely to end up in one of the two extremes of the ranking.Thus far we have ranked players on the basis of their average performance in big blinds. Though simple and natural, this approach ignores the importance of differences between players in the number of hands that they played. Few would share the view that a player who has won 500 big blinds over 1,000 hands (50 bb/100) is to be considered a better performing player than someone who has won 40,000 big blinds over 100,000 hands (40 bb/100). One of the drawbacks of the previous approach is that it does not account for the basic statistical rule that the sampling distribution of the mean depends on the sample size (i):BBi is the sum of big blinds won (before deduction of rake), si is the standard deviation of big blinds won, and ni is the number of hands played. We label this measure the “performance robustness measure” (PRM). In fact, PRMi equals the t-value of a test of a player’s observed performance against the null-hypothesis of zero expected performance.We therefore propose an alternative measure to rank players that accounts for the number of hands and playing style of an individual player  and 0.148 (medium stakes). Additional analyses with three-month periods yielded similar results.Another way to look at the persistence of performance is through transition probabilities. p < 0.001). At the same time, however, there is some evidence that the likelihood of ending up at the opposite extreme is also greater than 10 percent. For example, the chance of transitioning from the very best (worst) category to the very worst (best) is 12.3 (11.2) percent. This pattern is symptomatic of the inadequacy of the ranking measure used here: players with a higher variance of their average winnings due to adventurous or infrequent play are more likely to end up in the extreme win rate categories. Ranking players on the basis of our alternative performance robustness measure controls for this variance effect.In Panel A, the performance measure that is used to rank players is the standard performance measure (bb/100) after correction for rake. The fraction of players in the top decile of Period 1 who end up in the top decile in Period 2 is 13.6 percent; players who are in the worst decile end up in the worst decile 19.6 percent of the time. These empirical probabilities are substantially greater than the value of 10 percent that would be expected under the null hypothesis of no performance persistence : the very best one percent of players in Period 1 rank among the very best one percent in Period 2 11.4 percent of the time, and among the best ten percent 32.8 percent of the time (11.4 and 3.3 times the base rate). They are among the worst ten percent only 3.4 percent of the time. Similarly, the least successful players from Period 1 often keep performing poorly: the worst percentile stay in that category 10.2 percent of the time, and belong to the worst decile in 32.0 percent of the cases. They rarely outperform: the best decile is reached only 2.7 percent of the time.In Panel B, players are ranked on the basis of their To further analyze the role of skill we regress performance over the final six months of our sample on performance over the first six months, and on other measures that may serve as skill proxies. We consider the following explanatory variables:SPM: the standard performance measure or “win rate”, defined as the average number of big blinds won per hundred hands after correction for rake.- PRM: the performance robustness measure, defined as the average number of big blinds won per hand after correction for rake divided by the estimated standard error. The estimated standard error is the sample standard deviation of the rake-corrected winnings per hand divided by the square root of the number of hands.- Hands (log): the natural logarithm of the number of hands played. This variable is a proxy for the experience of players and thus a possible indicator of skill.- Tightness: one minus the proportion of hands in which a player voluntarily wagered money in the first betting round . The degree of tightness is one of the two simple measures that are typically used to broadly categorize players’ playing styles. Generally, tighter play is thought to be indicative of a better player. Common mistakes in poker are to impatiently look for “action” and to overestimate the profitability of playing a given hand.- Aggressiveness: the number of times a player led the betting (“bet” or “raised”) as a proportion of the total number of times the player voluntarily wagered money . This factor is the other of the two simple playing style measures. Aggressive play is generally thought to yield a higher expected performance than passive play, because increasing the cost of playing at the right times can pressure other players to give up stronger cards or to wager more with weaker ones.- Tournaments: a player’s tournament ability rating according to SharkScope, a website that tracks virtually all online poker tournament results. The worst possible rating is 50 and the best possible rating is 100. The exact calculation is not disclosed by SharkScope. Tournament performance is a possible indicator of skill, because of the many similarities between tournament and cash game play.- The last three variables are standardized such that they have a mean of zero and a standard deviation of one. To avoid endogeneity issues, all six explanatory variables are solely based on data from before Period 2: the first five cover the prior six months (Period 1), and the tournament ability rating is determined over the prior twelve months. The tournament ability rating was available for 79 percent of the players who played 1,000 or more ring game hands in Period 1.ni) in Period 2. We therefore apply weighted least squares (WLS) to estimate these regression models, where the weighing factor is the inverse of the variance of the error term . Not only the historical performance measure (Model 1), but also the number of hands played (Model 2), the two style measures (Models 3 and 4) and the tournament ability variable (Model 5) predict performance to a modest but statistically significant extent. Players who participated in more hands in the previous period perform better, as do players who adopted a tight or aggressive playing style and players who did well in tournaments. Combined, the measures explain 3.3 percent of the variance in performance (Model 6). The smaller-than-unity coefficient in Model 1 indicates that there is regression in players’ performance over time.Panel A of We obtain qualitatively similar results when we use our performance robustness measure (Panel B), but the explanatory power is higher now. The percentage of variance explained by the joint skill proxies is 8.1 percent, which is about 2.5 times as high as the empirical fit of the previous multivariate specification. We have also performed the regression analyses for the three stakes levels separately. The results and conclusions are all similar to the results for the aggregate sample.Although these results reinforce our earlier findings of performance persistence and the role of skill in poker, the major part of variance in performance remains unexplained by the models and appears to be attributable to chance. An issue that we have not yet explicitly addressed so far is the problem of errors in variables. In the ideal situation we would know every player’s precise skill level, but given the lack of this information we have to use noisy proxies. When explanatory variables are mismeasured, coefficients estimated via standard regression methods are biased towards zero and the true explanatory power is underestimated. The low empirical fit of the regression models indicates that measurement error is a serious issue for the historical performance measures: if a random factor explains much of the variation in performance, any measurement of previous performance is likely to be subject to a large degree of randomness as well. attenuation or regression dilution. Although measurement error is not a problem for predictive modeling, it can give an unjust impression of the size of the effect of skill on performance here and may falsely suggest that a player’s skill is not a stable quality over time. To account for error in both the dependent and the independent variable we therefore also run a so-called Deming regression [p < 0.001). When the performance robustness measure is used for the dependent and for the independent variable, the coefficient is 1.156 (p < 0.001). These alternative coefficients are not only substantially higher and closer to unity than the values of 0.167 and 0.229 reported before, but also significantly greater than unity (p < 0.001). Taken at face value, this suggests that the disparity in performance between players increases over time. Our robustness analysis at the end of this section, however, indicates that the two coefficients are somewhat inflated as a consequence of players’ budget constraints.The bias of an estimated coefficient towards zero as a consequence of measurement error is known as gression –19 , but for this measure an increase in the number of hands leads to more distinctive differences between players with a different expected win rate, reducing the relative size of measurement errors.R2 is 66.7 percent, and when the performance robustness measure is used the R2 is 80.1 percent.To illustrate the effect of the number of observations per player on the empirical fit, we run regressions for the pooled results of “teams” of players. More precisely, we first rank players on the basis of their performance in Period 1. We then group the players into percentiles, where the best one percent of players form a group, the second-best one percent form another group, et cetera. Next, for each percentile we calculate Period 1 and Period 2 performance across all hands of the players in the group combined. Last, we regress the pooled Period 2 performance on the pooled Period 1 performance. The average hypothetical “player” has now played about 2.1 million hands in Period 1  and 1.0 million in Period 2 . The results are remarkable. When performance is expressed as the win rate the We conclude this section with a robustness analysis. To make sure that the documented persistence of performance truly reflects the role of skill, we need to verify that the results are not driven by differences in budget constraints between players. As already explained in the previous section, a budget constraint can force a player to stop playing when losses accumulate, and, consequently, a negative performance is less likely to be cancelled out or diluted by subsequent hands than a positive performance. The stronger a player’s budget constraint at the start of a given period, the greater the likelihood that she needs to stop early after losses, and the lower her expected SPM and PRM over this period. Differences in budget constraints across players can be both exogenously and endogenously determined: some players may simply have smaller fixed budgets for playing than others in each period, and players who have lost in a previous period have less funds available in their accounts than players who have won. In both cases, the contemporaneous relation between the strength of a budget constraint and performance can lead to spurious correlation in players’ performance through time.n = 1,000, 5,000 and 10,000, we select all players who have played at least 2n hands over our entire sample period, and test whether performance over the first n hands is predictive of performance over the following n hands.To avoid the possible influence of budget constraints, we use hand samples of a fixed size for every player. For n and regardless of which of the two performance measures is being used, performance over the second n hands is significantly related to performance over the first n hands .The regression results are in n = 1,000). Raising n solves this issue, but comes at the cost of the inclusion of fewer players. Second, if losing players play less (because of budget constraints or a lost appetite to play), the selection criteria lead to a more homogeneous set of players in terms of their performance. Together with the exclusion of a possible spurious effect of budget constraints, these effects may explain why the R2 values for the two present Model 1 specifications (0.5% for SPM and 0.6% for PRM) are remarkably lower than before  cancel out with a further increase to 10,000. Furthermore, the larger regression coefficients for n = 5,000 than for n = 1,000 again underline the nature and role of measurement error, and the larger constants for larger n confirm the selection effect that occurs here.There are also two downsides to this alternative approach. First, there is more measurement error because in many cases fewer hands are being used to proxy for skill  and only two are statistically significantly different from unity. This result suggests that the Deming coefficients found before were larger than unity due to a spurious effect from budget constraints that is now eliminated, and, more importantly, it points out that skill differences between poker players are close to constant over time.dominates chance, that is, whether poker is more a game of skill than a game of chance. The answer to this question critically depends on the number of hands “the game of poker” is supposed to constitute. The role of chance diminishes with the number of hands, and the law of large numbers implies that it eventually cancels out when the number of hands grows large enough.The previous analyses demonstrate that there is persistence in the performance of poker players. Based on the results, we can confidently rule out that we are dealing with a game of pure chance. Skill is a factor of importance, but the key question left unanswered is whether skill also h hands from their combined sample of hands recorded for the second six months, and we compute their total winnings (in big blinds) across these h hands. We do the same for the worst performers, and then compare the total winnings of the two player categories. For each different value of h, we repeat this procedure 25,000 times and determine the proportion of times that the supposedly more skilled players do better than their supposedly less skilled counterparts. To circumvent technical limitations, we draw from a representative subset of one million hands when the actual sample size is greater than one million (we have verified that the results are insensitive to this approximation).In the present section we use simulations to approximate the number of hands above which skill predominates. More specifically, in accordance with the decile analyses, we first rank all players who have played 1,000 hands or more over the first six months of our sample period according to their performance during that first subperiod. Next, for the best performers, we randomly draw (with replacement) a given number of Tightness and Aggressiveness (see previous section). For the other half, he employs the obtained regression coefficients to compute players’ predicted performance. Heeb’s simulations point out that players who rank high according to this self-constructed skill measure are ahead of lower-ranked players more than 75 percent of the time after only a few hundred hands. A weakness of Heeb’s analysis is that he measures players’ characteristics and their performance over the same set of hands. This is likely to lead to spurious correlation between skill and performance scores, because both scores are contemporaneously co-determined by the same chance elements. For example, players who are dealt a greater fraction of strong hands or hands that connect well with the community cards are more likely to score high on the dimension of aggressiveness (and thus relatively high on skill) and to record a strong performance. Consequently, his analysis is likely to produce an underestimation of the critical number of hands above which skill predominates.A similar approach was used by Randal D. Heeb in his expert report for a U.S. Federal court case in New York in 2012 (Case 1:11-cr-00414-JBW). For one half of the players in his sample, Heeb estimates a regression model that links performance to hundreds of playing style characteristics, including many different variants of πh, as the probability that skill determines the more profitable player across h different hands. Accordingly, 1 - πh is the chance factor, or the probability that chance determines the more profitable player. When skill alone determines the winner (πh = 1), the more skilled player always wins; when chance alone determines the winner (πh = 0), the more skilled player wins half the time. More generally, the overall probability that the more skilled player is ahead after h hands is equal to ph = πh · 1 + (1 - πh) · 0.5. Skill predominates when πh > 0.5, implying ph > 0.75.In our simulations, skill predominates if the more skilled players outperform their less skilled counterparts more than 75 percent of the time. This threshold follows from a simple model where we define the skill factor, The accuracy of our simulation approach depends on the formation of two distinct groups of players. With each draw of hands, we want to simulate and compare the winnings of a relatively skilled and a relatively unskilled player. Every time, the former is thus assumed to be the better player, with a higher expected performance than the latter. Because we cannot observe a player’s true skill and need to rely on an imperfect proxy, we cannot exclude that our simulations sometimes confuse the two types. To limit this risk, we draw from the hands of players who ended up in either the very best or the very worst performance percentile of the first six months. We use our performance robustness measure to rank players, given our earlier evidence that this measure is more accurate than the standard performance measure. For the sake of completeness, we also run the simulations with the standard performance measure and with the top and bottom deciles of players instead of the top and bottom percentiles.h increases, the empirical win proportion converges to the win proportion that would result from performance being normally distributed, as predicted by the central limit theorem.) As indicated by the solid black line, the critical point where the best percentile of players (according to the performance robustness measure) is ahead 75 percent of the times is reached after 1,471 hands. As expected, this number is larger when the best and worst percentiles are being selected on the basis of the standard performance measure , and even larger when deciles instead of percentiles are being used .h = 10, 100, 1,000 and 10,000, and shows histograms for the distribution of the difference in win rate (number of big blinds won per hundred hands) between the two groups. While the previous figure only shows the proportion of times this difference is positive at a given h, the histograms also show the magnitude of the difference in profitability between the higher-ranked and lower-ranked players. Upon visual inspection, the distribution is widely but symmetrically distributed around zero when h = 10. The distribution gradually becomes more centered around its mean when h increases, and, consequently, with a greater h the positive mean win-rate difference of 21.2 bb/100 becomes more apparent . At h = 10, 100, 1,000 and 10,000, the fractions where the higher-ranked players are behind amount to 47, 43, 29 and 4 percent, respectively. Their chances of underperforming by more than 10 bb/100 shrink from 44 to 39, 21 and 0.5 percent, respectively. At the critical number of approximately 1,500 hands where skill dominates chance, the frequency of underperformance by more than 10 bb/100 amounts to 16 percent.Our study shows that there is a significant skill factor in online ring game poker, and that this factor dominates the luck factor after a moderate duration of play. In the first sections we have examined whether possible skill differences between online poker players explain differences in their performance. The results in these sections provide strong evidence against the hypothesis that poker is a game of pure chance. For a game of pure chance there would be no correlation in the winnings of players across successive time intervals.The decile analyses demonstrate that players who rank higher (lower) in profitability over the previous six months generally continue to perform better (worse) than others during the present six months. For example, players from the best decile earn about 20 to 25 big blinds per 100 hands more during the subsequent six months than players from the worst decile. In line with diminishing returns to skill the differences between consecutive deciles are greatest for the ninth and tenth decile. When we rank players on the basis of how well they did according to our alternative performance robustness measure, we find that top ten percent players rank among the top ten percent of the next six months approximately twice as often as others, and among the worst ten percent approximately half as often. The results are even more pronounced if we look at the best one percent. Similarly, those who perform the worst hardly ever end up in the top category.Our regression results reinforce these findings, and show that current performance is not only related to historical performance but also in some extent to simple measures of playing style. Players who are characterized by a tight or aggressive style generally perform better than their loose or passive opponents. Performance is also related to the number of hands that players have played over the previous period: more frequent or experienced players achieve better results. This finding can indicate that better players choose to play more and that players learn from playing. Both interpretations conflict with the pure-chance hypothesis.important degree of skill, but, more specifically, whether skill predominates. At the same time, no legislator seems to prescribe how this should be tested. The key complication is that the extent to which skill differences explain differences in performance depends on the number of hands over which performance is measured. If sufficiently many hands are played, skill explains practically all variation in performance. This is nicely illustrated by the high explanatory power of our regressions with the pooled performance scores of percentiles of players, and by the decile analyses, where the (rank) correlation between the past and the current performance of large groups is near-perfect or even perfect. A definite answer to the predominance question thus calls for a definition of the relevant measurement interval. The possible extremes are a single hand and a player’s life time, and intermediate options include an average session, a month, and a  year.Given these results we believe that we can legitimately conclude that skill is an important factor in online ring game poker. However, most jurisdictions do not ask whether a game involves an Instead of predetermining one particular interval ourselves, we have employed simulations to estimate the number of hands where skill and chance are equally important. In the previous section we basically run horse races of different durations between a relatively skilled player and a relatively unskilled player who are playing independently from each other. These simulations point out that skill dominates chance when performance is measured over 1,500 or more hands of play. To put this number into perspective: at a rate of 60–80 hands per hour per table, playing 1,500 hands takes people who play only one table at a time about 19 to 25 hours (four to six evenings) of play. Participating on multiple tables simultaneously—which is what many experienced players do—effectively reduces this duration to one or two sessions.As with any empirical estimate, the exact outcome depends on the specific approach. Our estimate that skill predominates after 1,500 hands should be seen in this light. The simulations do for example not account for possible serial dependence in hand results, which may have led to an underestimation of the critical number of hands. However, we believe that we have taken a conservative approach, because the two types of players in our simulations were not playing the game against each other, and because of selection effects.other players than their counterparts in our comparison. The hands were played at different tables and at different moments in time. We are thus not analyzing how well a selection of strong players fare against a selection of weak ones, but comparing how well they did against a cross-section of others, including players from their own category. Would higher-ability players be directly playing against lower-ability players, skill should be expected to predominate substantially quicker.With few exceptions, the series of hand outcomes that we compare consist of hands that have been played at tables where the relatively skilled and the relatively unskilled players  sat down with To avoid extremely noisy historical skill estimates, we have required a minimum data history per player. Unintentionally, this approach is likely to have generated a selection effect: because intensity of play and experience are  related, relatively inexperienced players are underrepresented in our analyses. Every player in our final sample will normally be well informed about the rules of the game and master some basic strategic concepts. The emphasis on relatively skilled players in our data is probably stronger towards the end of our sample period, because relatively poor players are more likely to lose and quit early, and if they continue to play they have more potential to learn from experience and improve their game. If we would also observe complete beginners playing the game, the differences in performance across players would presumably be greater, and the critical number of hands where skill starts to predominate would consequently be more quickly reached.The relative homogeneity of our sample is probably strengthened by players’ self-selection into stakes levels on the basis of their perception of their skill level. Better players are more likely to play for larger stakes, while worse or beginning players may feel more comfortable at smaller stakes. This self-selection into the game is not unique for poker. In many games, people play against opponents of relatively similar ability. When such self-selection occurs, the influence of randomness on the outcome of a contest increases, and it will take a longer series of events before skill differences materialize—even with professional sports and with games like chess and bridge.We conclude with a brief discussion of the generalizability of our findings. Our study has been limited to online play. Due to a lack of readily available data, it is practically impossible to execute an analogous, large-scale analysis for offline play. Nevertheless, given that skill is important in the online variant, we conjecture that it is likely to be even more important for brick-and-mortar play. One reason is that offline play also involves body language and other subtle forms of communication. Players are sitting face-to-face and need to carefully control their behavior to not reveal the strength of their cards, and by observing others they can sometimes discover useful “tells” about their play. At the same time, body language can also be used to deliberately mislead opponents. Furthermore, players’ patience is put to the test more in live play than in online play because fewer hands are dealt per hour. In live poker, skill will probably dominate chance at fewer hands, but because of the slower pace of play and the impossibility to play on multiple tables it may take more hours to reach the critical number.Another limitation is that we have looked at cash game play only, while both online and offline poker are also frequently played in tournament form. This focus was deliberate, because the value of a given amount of chips wagered in a tournament hand depends on the phase of the tournament and on players’ chip stack size relative to the chip stacks of their opponents. This issue greatly complicates the analysis of performance using hand-level data. A more straightforward approach for tournaments would be to analyze players’ finishes, which is precisely what Croson, Fishman and Pope  and LeviS1 Appendix(PDF)Click here for additional data file.


Reactivation of androgen receptor (AR) may drive recurrent prostate cancer in castrate patients. Ack1 tyrosine kinase is overexpressed in prostate cancer and promotes castrate resistant xenograft tumor growth and enhances androgen target gene expression and AR recruitment to enhancers. Ack1 phosphorylates AR at Tyr-267 and possibly Tyr-363, both in the N-terminal transactivation domain. In this study, the role of these phosphorylation sites was investigated by characterizing the phosphorylation site mutants in the context of full length and truncated AR lacking the ligand-binding domain. Y267F and Y363F mutants showed decreased transactivation of reporters. Expression of wild type full length and truncated AR in LNCaP cells increased cell proliferation in androgen-depleted conditions and increased colony formation. However, the Y267F mutant of full length and truncated AR was defective in stimulating cell proliferation. The Y363F mutant was less severely affected than the Y267F mutant. The full length AR Y267F mutant was defective in nuclear translocation induced by androgen or Ack1 kinase. The truncated AR was constitutively localized to the nucleus. Chromatin immunoprecipitation analysis showed that it was recruited to the target enhancers without androgen. The truncated Y267F AR mutant did not exhibit constitutive nuclear localization and androgen enhancer binding activity. These results support the concept that phosphorylation of Tyr-267, and to a lesser extent Tyr-363, is required for AR nuclear translocation and recruitment and DNA binding and provide a rationale for development of novel approaches to inhibit AR activity. Prostate cancer remains the second leading cause of cancer death among American men due to the progression after androgen deprivation therapy of initially hormone dependent prostate cancer. Current evidence indicates that reactivation of androgen receptor (AR) in tumor cells may play a critical role in the development of castration resistant prostate cancer (CRPC) . MultiplIn addition, activation of AR through crosstalk with kinase signaling pathways has been postulated as a potential mechanism for recurrent tumor growth in the castrate environment . AR protLNCaP cells  was init5 cells per well) in 12-well plates the day before transfection. 150 ng of the AR construct (wild type or mutant) and 150 ng of the AR-dependent reporter construct (MMTV- or ARR2-PB-luciferase) were co-transfected using Effectene . Next day, medium was replaced with phenol-red free, serum free media and incubated for 8 hours. Cells were then treated with 10 nM dihydrotestosterone (DHT) (Sigma-Aldrich) or vehicle in serum free media for 16 hrs. Luciferase activity was measured, as previously described [For construction of AR mutants, the QuikChange Site-Directed Mutagenesis Kit  was used according to the manufacturer’s protocol. A stop codon was inserted after amino acid 660 in the wild type AR plasmid to generate the truncated AR construct. All mutations were confirmed by sequencing. LNCaP cells were plated , supplemented with protease inhibitors to cytoplasmic extraction reagent I (CER I) and nuclear extraction reagent (NER) as described in their protocol. The protein content of cytoplasmic and nuclear extracts was determined with the Bradford assay reagent  and an equal amount of protein was used in immunoblotting.4 cells were plated in each well of two well chamber slides . Next day, cells were transiently transfected with AR (100 ng) or vector (100 ng) along with constitutively active Ack1 (100 ng) or vector (100 ng) expression constructs, using FuGENE 6  and incubated for overnight. Media was changed to serum free medium and cells were serum starved for 8 hrs. After serum starvation, cells were either treated with DHT (10 nM) or vehicle (1% ethanol) for 2 hours. Next, media was removed from the cells and cells were rinsed with cold phosphate buffered saline (PBS) once for 5 minutes. The fixative  was added to the cells and incubated at room temperature for 15 minutes. At the end of the fixation, cells were washed with cold PBS twice, and the blocking solution (0.3% Triton X and 5% donkey serum) was added and incubated for 30 minutes at room temperature. At the end of the incubation, blocking solutions were removed from the wells except the blank samples. Primary antibody, mouse anti-androgen receptor  made in 1:100 dilution in the blocking solution, was added on the cells and incubated at room temperature for two hour. At the end of incubation, primary antibody was removed from the cells and the cells were washed three times with cold PBS (5 minutes each wash). Secondary antibody, FITC-conjugated donkey anti mouse antibody, was prepared in 1:250 dilution in the blocking solution and added to the cells. The cells were incubated in the dark at 37°C for one hour. The secondary antibody was removed and the cells were washed with cold PBS three times. At the end of the wash, cover slips were placed with the mounting media which includes 4',6-diamidino-2-phenylindole (DAPI)  for nuclear staining. After cover slips were mounted, slides were kept in the dark cold room until images were captured.COS-7 cells were counted and 3x10Chromatin immunoprecipitation analysis for recruitment of FLAG-tagged AR protein to the androgen response element III enhancer of canonical AR target genes, prostate specific antigen (PSA) and kallikrein-related peptidase 2 (KLK2), was performed as previously described . mRNA leTotal RNA from LNCaP cells expressing vector, TR-WT-AR, and TR-AR-Y267F growing in androgen deprived conditions were isolated by using RNeasy Mini kit  according to the manufacturer’s instructions. Two biological replicates were used per each cell line. RNA integrity was evaluated using the RNA 6000 Nano LabChip kit and a Bioanalyzer . RNA samples from TR-AR-WT or TR-AR-Y267F were assayed versus vector control as reference sample. The samples were hybridized onto the Agilent Whole Human Genome Oligonucleotide Microarray (4X44K) according to the manufacturer’s protocol at the UNC Genomics Core Facility. The microarrays were scanned on an Agilent scanner and the raw data extracted and deposited into the UNC Microarray Database. Individual channel values were background corrected and lowess normalized. The resulting log ratios were median centered by gene to produce the values for further gene expression analysis. From microarray gene expression data, a score representing AR transcription pathway activity was calculated. The previously published androgen response signature was used . This siThe two-group t-test was used for the pair-wise group comparisons of wt to Y267F and wt to Y363F. These t-test p values have been adjusted using the Bonferroni method to account for multiple testing. Statistical analyses were performed with SAS statistical software . p value <0.05 was considered statistically significant.AR-dependent reporter assays showed impaired ligand-dependent and Ack1-induced activation of full length AR when Tyr-267 and Tyr-363 residues were mutated to phenylalanine . To asseOverexpression of AR in androgen-dependent LNCaP cells enhances cell proliferation at suboptimal androgen concentrations ,30. The The effect of AR overexpression on cell proliferation at various androgen concentrations was characterized. Proliferation of vector control cells was androgen-dependent and required the full dose (10 nM) of DHT for optimal proliferation . Full leBinding of AR to its ligand causes translocation into the nucleus and binding to the promoter and enhancer regions of its target genes. A previous report indicated that AR nuclear translocation was induced by phosphorylation of AR at Tyr-534 site by Src tyrosine kinase . The effIn order to confirm the role of Tyr-267 in cells that express endogenous AR, we investigated subcellular localization of exogenous AR protein stably overexpressed in LNCaP cells by immunoprecipitation with the FLAG epitope antibody and immunoblotting with an AR antibody. Overexpression of both full length and truncated AR wt induced predominantly nuclear localization of AR even without DHT treatment . The extTo characterize the effect of Y267F and Y363F mutations on recruitment and binding of AR to the regulatory sequences of target genes, chromatin immunoprecipitation analysis for AR binding to the enhancers of the canonical AR target genes, PSA and KLK2, was performed using the FLAG antibody for detecting the ectopically expressed AR. Truncated AR wt protein was constitutively bound to the PSA and KLK2 enhancers in the absence of androgen. The Y267F mutant did not bind to the PSA and KLK2 enhancers . The Y36In this study, the potential functional significance of the N-terminal phosphorylation sites Tyr-267 and Tyr-363 in ligand-dependent and—independent activation of AR was investigated by characterizing the effect of tyrosine to phenylalanine substitution mutants in the context of full length and truncated AR. Expression of wild type full length and truncated AR led to increased cell proliferation in the androgen-depleted condition and increased soft agar colony formation. However, the Y267F mutant of full length and truncated AR was defective in stimulating cell proliferation in both androgen-depleted and androgen-supplemented conditions. The Y363F mutant was less severely affected than the Y267F mutant. The full length AR Y267F mutant was defective in nuclear translocation induced by androgen or Ack1 kinase activity. The truncated AR protein used in this study translocated to the nucleus and bound to the androgen enhancers of target genes in the androgen-depleted condition. However, the truncated Y267F AR mutant protein did not exhibit constitutive nuclear localization and enhancer binding activity. Similar to full length AR , the truATM gene and enhance its expression [in vitro [Since the N-terminal transactivation domain of the AR protein is unstructured and disordered , the detpression . Sites spression . Ser-81 pression ,37. Chenpression . The ligpression . The pubpression . Proteinpression . Interesin vitro . This inin vitro ,43.v567es, a major splice variant commonly expressed in clinical tumor specimens, does contains the bipartite NLS [There has been much interest in the AR splice variants expressed in tumor cells that are similar in structure to the truncated AR used in this study. These AR splice variants may play a role in progression to castrate resistance and acquired resistance to new therapeutic agents. AR splice variants retain the N-terminal transactivation domain and the DNA binding domain but lack the ligand-binding domain –12. Howetite NLS . PresentTwo recently approved prostate cancer drugs abiraterone and enzalutamide target the ligand-binding domain of AR by decreasing the ligand availability or inhibiting binding of the ligand. These agents extend the overall survival of CRPC patients by approximately four or five months ,46. Whil


Long non-coding RNAs (lncRNA) play an important role in carcinogenesis; knowledge on lncRNA expression in renal cell carcinoma is rudimental. As a basis for biomarker development, we aimed to explore the lncRNA expression profile in clear cell renal cell carcinoma (ccRCC) tissue.Microarray experiments were performed to determine the expression of 32,183 lncRNA transcripts belonging to 17,512 lncRNAs in 15 corresponding normal and malignant renal tissues. Validation was performed using quantitative real-time PCR in 55 ccRCC and 52 normal renal specimens. Computational analysis was performed to determine lncRNA-microRNA (MiRTarget2) and lncRNA-protein (catRAPID omics) interactions. We identified 1,308 dysregulated transcripts  in ccRCC tissue. Among these, aberrant expression was validated using PCR: lnc-BMP2-2 (mean expression change: 37-fold), lnc-CPN2-1 (13-fold), lnc-FZD1-2 (9-fold), lnc-ITPR2-3 (15-fold), lnc-SLC30A4-1 (15-fold), and lnc-SPAM1-6 (10-fold) were highly overexpressed in ccRCC, whereas lnc-ACACA-1 (135-fold), lnc-FOXG1-2 (19-fold), lnc-LCP2-2 (2-fold), lnc-RP3-368B9 (19-fold), and lnc-TTC34-3 (314-fold) were downregulated. There was no correlation between lncRNA expression with clinical-pathological parameters. Computational analyses revealed that these lncRNAs are involved in RNA-protein networks related to splicing, binding, transport, localization, and processing of RNA. Small interfering RNA (siRNA)-mediated knockdown of lnc-BMP2-2 and lnc-CPN2-1 did not influence cell proliferation.We identified many novel lncRNA transcripts dysregulated in ccRCC which may be useful for novel diagnostic biomarkers.The online version of this article (doi:10.1186/s13148-015-0047-7) contains supplementary material, which is available to authorized users. Renal cell carcinoma (RCC) is one of the most common malignancies; its incidence is varying substantially worldwide: RCC incidence is high in Europe and North America and low in Asia and South America . Today, Nucleic acids are under discussion as potential biomarkers for patients with RCC . Long noA gene expression microarray was used to identify dysregulated lncRNAs in 15 corresponding tumor and normal renal tissue samples as a discovery cohort. Among the 32,183 analyzed lncRNA transcripts, we observed differential expression (fold change >2) in 1,308 transcripts: 568 lncRNA transcripts were upregulated and 740 were downregulated in ccRCC samples. The 20 most differentially expressed lncRNAs in ccRCC and normal renal tissue are listed in Table n = 55) and normal (n = 52) renal tissue samples. The selection of representative lncRNAs for validation was based on the degree of dysregulation in the microarray . As expected, lnc-BMP2-2 (mean: 37-fold), lnc-CPN2-1 (13-fold), lnc-FZD1-2 (9-fold), lnc-ITPR2-3 (15-fold), lnc-SLC30A4-1 (15-fold), and lnc-SPAM1-6  were highly overexpressed in RCC, whereas lnc-ACACA-1 (135-fold), lnc-FOXG1-2 (19-fold), lnc-LCP2-2 (2-fold), lnc-RP3-368B9 (19-fold), and lnc-TTC34-3 (314-fold) were downregulated . Both, lnc-ERCC5-1 (p = 0.401) and lnc-RP11-480I12.4.1-1 (p = 0.731) were—as observed in the microarray studies—not dysregulated in tumor samples. The lncRNA expression levels allowed highly sensitive and specific discrimination between RCC and control subjects as determined by using ROC analyses: the area under the curve was >0.9 for all dysregulated samples, especially lnc-CPN2-1 overexpression allowed molecular identification of RCC tissue . See Figure In order to confirm aberrant lncRNA expression, we determined the expression of 13 lncRNAs in an independent validation cohort of ccRCC . None of the lncRNAs was significantly correlated with staging nor grading. However, there was a tendency towards lower lnc-ERCC5-1 (p = 0.034) and RP3-368B9 (p = 0.016) levels in Fuhrman grade 3/4 tumors, lower lnc-ERCC5-1 (p = 0.026) expression in vascular invasive tumors, and advanced AJCC stage in samples with low lnc-RP3-368B9 (p = 0.011). lncRNAs were not associated with progression-free survival, overall survival or cancer-specific survival , as determined using Cox regression analysis.We also analyzed whether lncRNA expression levels were correlated with poor prognostic parameters; the Bonferroni method was applied to correct for multiple hypothesis testing  decreased in siRNA-treated Caki-1 and Caki-2 (only siRNA #2) cells; it was basically unexpressed in A-498 cells. lnc-CPN2-1 was also decreased in Caki-2 cells (p < 0.05). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression was significantly reduced in all cell lines (not shown); the negative controls did not change gene expression. However, the EZ4U test did not show any changes in cellular proliferative activity after siRNA-mediated lncRNA knockdown. See Figure We next treated Caki-1, Caki-2, and A-498 RCC cell lines with small interfering RNA (siRNA) targeting lnc-BMP2-2 and lnc-CPN2-1; see Figure p > 0.1). MicroRNA expression was similar in RCC and normal renal tissue .The MiRTarget2 algorithm  was usedn = 91) are predicted for all transcripts . Thereby, they are involved in carcinogenesis of many human malignancies , but so n = 4 ; n = 6 [ [n = 11 ), thereb [n = 11 . During  [n = 11  re-analy [n = 11 . EarlierSERPINC1 ,14; aHIFSERPINC1 ; MALAT1 SERPINC1 ); we coulncRNAs could serve as diagnostic and prognostic biomarkers: The distinct overexpression of lncRNAs in RCC could be used for the development of a non-invasive diagnostic biomarker. We demonstrated distinct (>10-fold) expression level differences for a number of lncRNAs including lnc-ACACA-1, lnc-BMP2-2, lnc-CPN2-1, lnc-FOXG1-2, lnc-FZD1-2, lnc-ITPR2-3, lnc-RP3-368B9, lnc-SLC30A4-1, lnc-SPAM1-6, and lnc-TTC34-3; if expression differences are validated by independent researchers, these lncRNAs could represent diagnostic biomarkers. lncRNAs are detectable in bodily fluids and may thereby serve for cancer diagnosis: The PROGENSA® PCA3 Assay utilizes the detection of the lncRNA PCA3 in urine as diagnostic biomarker for prostate cancer . Furthern = 16) and the lack of a validation cohort limit the informative value of the study [i) a microarray study not powered for the identification of expression differences in localized (n = 5) and advanced RCC (n = 10), (ii) the selection of lncRNAs for validation based on the differential expression of tumor and normal tissue samples, and (iii) the high number of patients with localized ccRCC (56% AJCC stage 1 or 2) in the validation cohort. We expect that future studies designed to identify prognostic relevant lncRNAs in RCC patients will be able to present some candidates, because altered expression of specific lncRNAs was predictive of cancer-specific survival in former studies on other human malignancies; i.e. upregulation of HOTAIR in breast cancer [In addition, lncRNAs may be of prognostic relevance: Fachel et al.  reportedhe study . Malouf he study  identifit cancer  and SChLt cancer  was assoin silico analyses to point out putative functions of dysregulated lncRNAs: (i) lncRNA may act as miRNA sponge (e.g. miR-372 expression is downregulated by the lncRNA HULC [ii) Furthermore, lncRNA may function via lncRNA-protein interactions [in silico predictions is necessary in future studies.As interest in lncRNA research has increased a short time ago, the function of lncRNAs remains largely unknown, but involvement in many fundamental cellular processes is assumed . The shoRNA HULC ). Howeveractions . Using cractions , we idenSomewhat surprisingly, neither siRNA-mediated knockdown of lncRNA-CPN2-2 nor lnc-BMP2-2 resulted in a significant reduction of cell proliferation. However, the lncRNA knockdown was of moderate success , and thus the toxicity of the transfection reagent may inhibit determining the functional relevance of both lncRNAs. Many lncRNAs regulate nuclear events and must therefore be localized in the nucleus . As is kIn summary, our study reveals that dysregulation of approximately 4% of the lncRNA transcripts occurs in ccRCC, and altered lncRNA expression may modulate fundamental cellular processes. lncRNA profiles allow to accurately distinguish ccRCC and normal renal tissue. Thus, lncRNAs may be used as non-invasive biomarker for RCC patients.Biomaterials including fresh-frozen tissues are prospectively collected in the Biobank at the CIO Köln Bonn at the Universitätsklinikum Bonn according to standard operating procedures. Tissue from patients undergoing radical nephrectomy or nephron-sparing surgery was snap frozen, and samples from tumor and normal renal tissue were stored at liquid nitrogen. Hematoxylin- and eosin-stained sections were performed to confirm the histology of the samples. The final histological diagnosis was made on the formalin-fixed paraffin-embedded tissue samples. All cases used in this study were reviewed by an experienced uropathologist; the TNM classification (7th edition from 2009) was applied. We used a discovery cohort with 15 patients  for screening 32,183 lncRNA transcripts; the independent validation cohort consisted of 55 ccRCC and 52 normal renal tissue samples. The detailed clinical-pathological parameters are reported in Table 2 in RPMI 1640 culture medium, supplemented with 10% heat-inactivated fetal calf serum and 2 mM of glutamine . The knockdown of lnc-BMP2-2  and lnc-CPN2-1  was performed with individually designed Ambion Silencer Select siRNAs ; two different siRNAs were designed for each target. In addition, negative controls  and a positive control (Silencer GAPDH Positive Control siRNA) were used. The cell lines were seeded at 2.5 × 105 cells/well (6-well plate), and a forward transfection using the Screenfect Transfection Kit  with a siRNA concentration of 20 pmol was performed according to the manufacturer’s recommendations. The transfection complex was replaced after 24 h with RPMI medium and incubated for up to 72 h. siRNA experiments were confirmed in three independent experiments.The cell lines Caki-1, Caki-2, and A-498 were obtained from the DSMZ . All cell lines were maintained at 37°C, 5% CO4 cells per well into a 96-well plate. After forward transfection (siRNA concentration 1.2 pmol), the cells were cultured up to 72 h to determine the proliferative activity after siRNA-mediated knockdown of lnc-BMP2-2 and lnc-CPN2-1. The EZ4U assay  was used to determine cell viability using a 340 ATTC Spectra Thermo SLT photometer  according to the manufacturer’s protocol.The cell lines were seeded at a concentration of 1.5 × 10Fresh-frozen tissue samples (approximately 50 mg) were cut on liquid nitrogen and homogenized with Yttrium stabilized Zirkonoxid-Beads  using a Precellys®24 tissue homogenizer . Total RNA was then isolated using the mirVana miRNA Isolation Kit  according to the manufacturer’s recommendation. To remove residual DNA fragments, we treated the isolate twice with DNase . RNA quantity was determined with a NanoDrop 2000 spectrophotometer . RNA integrity was measured in samples used for microarray experiments using the Bioanalyzer 2100 with a RNA 6000 Nano Kit ; only samples with a RIN >6 were used. RNA degradation was excluded by agarose gel electrophoresis in samples used for PCR.The microarray experiments were performed by Biogazelle  as a contract service. RNA isolates from 15 corresponding normal renal and ccRCC tissues  were sent on dry ice to Biogazelle. A custom microarray (Agilent SurePrint G3 technology) based on LNCipedia 2.1 [U test, Cox regression analysis).Quantitative real-time PCR was performed to validate the microarray experiments using an independent cohort of 55 ccRCC and 52 normal renal tissue samples and to confirm the success of siRNA-mediated lncRNA knockdown. Complementary DNA (cDNA) was synthesized with 1 μg RNA using the PrimeScript RT Reagent Kit with gDNA Eraser. Real-time PCR was performed with 5 ng cDNA template using the 1× SYBR Premix Ex Taq II with ROX Plus and 10 pmol/μl PCR primers : 1,000 ng total RNA was reverse transcribed and real-time PCR was performed with 5 ng cDNA template. Pre-designed miScript Primer Assays for miR-519a, miR-519b, miR-519c, miR-548, miR-3120, miR-4284, miR-4658, and SNORD43 were used. PCR was performed using the ABIPrism 7900 HT Fast Real-Time PCR System. Data analysis was performed using Qbase + with SNORD43 as reference genes in the 2-∆∆CT algorithm. IBM SPSS Statistics v21 was used for statistical analyses (Pearson correlation test).The MiRTarget2 algorithm  was usedThe microarray data sets supporting the results of this article are available in the Gene Expression Omnibus repository (data entry: GSE61763).


MK 9.8 µM; transport studies). The drug is present in perinuclear granules that are mostly positive for Rab7 and LAMP1 (microscopy). Both drug uptake and retention are extensively inhibited by treatments with the V-ATPase inhibitor bafilomycin A1. The H+ ionophore monensin also prevented quinacrine concentration by fibroblasts. However, inhibition of plasma membrane transporters or of the autophagic process with spautin-1 did not alter quinacrine transport parameters. Ancillary experiments did not support that low micromolar concentrations of quinacrine are substrates for organic cation transporters-1 to -3 or P-glycoprotein. The secondary autophagy induced by quinacrine in cells may derive from the accumulation of incompetent autophagolysosomes, as judged from the accumulation of p62/SQSTM1 and LC3 II (immunoblots). Accordingly, protracted lysosomogenesis is evidenced by increased expression of LAMP1 and LAMP2 in quinacrine-treated fibroblasts , a response that follows the nuclear translocation of the lysosomal genesis transcription factor TFEB and upregulation of LAMP1 and −2 mRNAs (24 h). Quinacrine administration to live mice evidenced variable distribution to various organs and heterogeneous accumulation within the lung . Dose-dependent in vivo autophagic and lysosomal accumulation was observed in the lung (immunoblots). No evidence has been found for transport or extrusion mechanisms modulating the cellular uptake of micromolar quinacrine at the plasma membrane level. As shown in vitro and in vivo, V-ATPase-mediated cation sequestration is associated, above a certain threshold, to autophagic flux inhibition and feed-back lysosomogenesis.The proton pump vacuolar (V)-ATPase is the driving force that mediates the concentration of cationic drugs (weak bases) in the late endosome-lysosome continuum; secondary cell reactions include the protracted transformation of enlarged vacuoles into autophagosomes. We used the inherently fluorescent tertiary amine quinacrine in murine models to further assess the accumulation and signaling associated with cation trapping. Primary fibroblasts concentrate quinacrine ∼5,000-fold from their culture medium ( The concentration of cationic drugs (weak bases) in acidic cell compartments is well documented, at least in cultured cells  and fresWhile the ion trapping model of the cellular concentration of organic amines has been verified in a number of cell types, many issues in this model remain uncertain or not widely verified:(1)It is not known whether the autophagic process originates from organelle damage, from cell starvation The influence of the presumably secondary autophagic process on the upstream cationic drug uptake has not been studied.(3)If the concentration of cationic drugs inactivates in part the lysosomal function via pH buffering , a possi(4)in vitro, macrophages concentrate more amiodarone than do smooth muscle cells To document tissue and subcellular distribution of the drug, as assessed by its intrinsic fluorescence in a murine model, and possible tissue specific modulation of reservoir formation by plasma membrane transporters.(2)To further characterize the autophagic accumulation in cells that concentrate quinacrine.(3)To evidence the possible lysosomogenesis in response to the buffering of acidic organelles by quinacrine concentrated from the extracellular fluid.(4)in vivo the general model derived from in vitro experiments .To confirm in vivo quinacrine administration . Following euthanasia with a combination of isoflurane and CO2, dermal fibroblasts were cultured from multiple explants in Dulbecco’s Modified Eagle Medium (Life Technologies) supplemented with 10% fetal bovine serum (FBS) and antibiotics and propagated, up to passage 6, in the same medium. Several primary fibroblast lines were obtained from various mice. Macrophages from broncho-alveolar lavage were isolated from mice as a suspension in PBS containing 0.2% EDTA (2). Cell dishes were further treated with drugs for 4 h (37 °C), rinsed with PBS and observed (fluorescence and transmission microscopy).A local ethics committee approved the use of C57BL/6 male mice , 8 weeks old, for the isolation of cells and tissues and .2% EDTA , centrifIn vivo treatment with quinacrine was applied to some mice under the form of 2 intraperitoneal injections . Control mice received the drug vehicle . These dose levels are documented and tolerated in mice .Bafilomycin A1 was purchased from LC Laboratories . Other drugs, from Sigma-Aldrich , included the fluorescent agents quinacrine dihydrochloride and doxorubicin hydrochloride. Other significant drugs from the same source were the macroautophagy inhibitor spautin-1 , the H+ monensin , the inhmonensin , elacridmonensin  and promFirst, the sequence corresponding to the human LAMP1 protein was amplified from the previously described LAMP1-GFP vector  or the signaling induced by quinacrine treatment. Thus, vectors encoding Rab5, Rab7 and Rab7-GTP-locked fused to the mCherry fluorescent protein were gifts from Drs. Robert Lodge  and Michel J. Tremblay . Endoplasmic reticulum-targeted red fluorescent protein ER-RFP;  was a geα-actin  and the subcellular distribution of myc-tagged TFEB . Cells were photographed using an Olympus BX51 microscope coupled to a CoolSnap HQ digital camera .In live cultured murine fibroblasts, the subcellular distribution of quinacrine was monitored after various treatments at 37 °C (30 min-4 h) using its intrinsic green epifluorescence. The colocalization of red fluorescent proteins was monitored in some experiments. Mitochondrial staining was applied as 25 nM MitoTracker Red CMXRos (Invitrogen) added to the culture medium 15 min before microscopic observation. Immunofluorescence in fixed and permeabilized cells was applied for detecting 2 cell flasks containing 3 ml of serum-containing culture medium according to various schemes and time frames. Quinacrine uptake at the end of the allowed incubation period  was determined by rapidly washing each cell flask 3 times with 3 ml of phosphate-buffered saline, pH 7.4, at room temperature and dissolving the cells with 10 ml of 1 N NaOH. Quinacrine content was analyzed in the NaOH extract using a fluorescence spectrophotometer  against a standard curve of the authentic drug dissolved in NaOH 1 N . Control fluorescence from extracts of untreated cells was systematically verified, of small magnitude and subtracted from experimental values.The uptake of quinacrine in mouse cells was established using a slight variation of a technique previously applied to a different cell type . Brieflyin vivo with quinacrine (40 mg/kg/day i.p. for 2 days) or its saline vehicle. To this end, the mouse organs were rapidly homogenized using a glass–glass pestle in 1 N NaOH . The autofluorescence cannot be subtracted in this experimental setting, but may be quantified from the saline-treated animals.The same fluorometric assay was applied to selected organs of mice dosed 2 flasks of confluent cells were treated for 24–48 h with quinacrine, other drugs or serum starvation to monitor effects on proteins affected by autophagy  or lysosomogenesis . The cells were then put in boiling lysis buffer containing 10 mmol/L Tris, pH 7.4, 1.0 mmol/L Na3VO4, and 1.0% SDS. The lysates were centrifuged at 15,000 g for 5 min and incubated for 5 min at 95 °C. Total protein concentrations were then determined with the use of the BCA Protein Assay (Pierce). Fifteen micrograms of total protein was run on a SDS-PAGE and transferred to a PVDF membrane. Some extracts for immunoblots were prepared from homogenized mouse organs, as outlined above, and similarly processed. Anti-human LC3B rabbit polyclonal antibodies  were used to reveal the cytosolic form LC3 I (18 kDa) and the lipidated and membrane-bound form LC3 II in total cell extracts run on 15% SDS-PAGE and transferred to PVDF membranes . The lysosomal/late endosomal glycoproteins LAMP1 and LAMP2 were detected in total cell extracts using the rat monoclonal antibodies 1D4B and ABL-93, respectively  revealed using an HRP-conjugated anti rat IgG. The primary antibodies are specific to the mouse LAMP proteins. All reactions involved HRP-labelled secondary antibodies revealed using a luminescent substrate used as directed  with CL-X Posure film (Thermo Scientific). Equal track loading was verified by separating and transferring the same samples separately and immunoblotting for β-actin . Intensities of immunoblot signals were derived from scanned versions of photographic films: the mean pixel intensity  of rectangular selections corresponding to migrated proteins was evaluated using the Photoshop software . The background signal was subtracted.For the analysis based on cultured fibroblasts, 25-cmembranes . Nine % Mouse dermal fibroblasts were plated in 6-well plates two days prior to stimulation. Total RNA was extracted using Qiazol  according to the manufacturer’s protocol, with modifications. Briefly, after 24 h of treatment, cells were washed twice with room temperature PBS before adding 1 ml of Qiazol in each well. Cells were detached using a cell scrapper and the suspension was transferred to a tube containing 200 µL of chloroform. Afterwards, samples were vigorously mixed and then centrifuged at 12,000 g for 15 min (4 °C). The aqueous phase (≈400 µL) was transferred to a 1.5 ml Eppendorf tube containing an equal volume of isopropanol. After 10 min of incubation at room temperature, the samples were centrifuged for 10 min at 12,000 g (4 °C). The supernatant was discarded and the RNA pellet was washed twice using 500 µL of 70% ethanol followed by a 5 min centrifugation . The final pellet was air-dried for 10–15 min and then re-suspended in RNAse-free water. The resulting RNA solution was then quantified using a Biodrop µLITE instrument.The mRNA transcript level was monitored in mouse dermal fibroblasts using real-time PCR as described by KM and Vmax values calculated from this procedure. Sets of numerical data obtained via immunoblot densitometry, cytofluorometric intensities, enzyme activity, or MPP+ transport were generally compared by ANOVA followed Bonferroni multiple comparison tests to compare selected pairs of values, of by Dunnett’s test to compare experimental groups with a common control value. If a set of values was normalized (control = 1), the non-parametric Kruskal–Wallis ANOVA and Dunn’s multiple comparison test were applied instead. The qualitative effect of treatments on the subcellular distribution of TFEB (either predominantly cytosolic or nuclear) was evaluated for each stained fibroblast in microphotographic records and proportions of affected cells were compared using the χ2 statistics to determine drug effect. Fluorescence intensity in organ extracts from mice treated with either saline or quinacrine was compared using t test. All computations were performed using the InStat 3.05 computer program, GraphPad Software .Numerical results are presented as mean ± S.E.M. Quinacrine transport data obtained by varying the drug concentration during a fixed incubation period were fitted by non-linear regression to the Michaelis–Menten equation using a least-square method  and α-actin distributed as stress fibers , corresponding to a packed cell volume of 5–7 µl, indicating a ∼5,000-fold concentration of quinacrine in the cell compartment, relative to the drug concentration in the culture medium. The concentration-effect relationship of bafilomycin A1-induced suppression of quinacrine uptake by fibroblasts has been explored for a fixed quinacrine and low concentration and a 2-hr treatment duration . As judged by the cell abundance of LAMP1 and LAMP2, a 48-hr quinacrine treatment  significantly increased the mass of late endosomal/lysosomal compartments where these proteins are found . Serum sThe transcription factor TFEB controls the expression of LAMP1, LAMP2, V-ATPase subunits and the expression of hundreds of other genes encoding proteins present in lysosomes . FibroblWhether quinacrine increases the activity of a lysosomal hydrolase, cathepsin D, has been examined in mouse fibroblasts treated for 24 or 48 h . Quinacrin vitro for high resistance to vinblastine and overexpresses a glycoprotein later termed PGP . The residual quinacrine uptake may correspond to nuclear staining as observed in microscopic experiments. As in murine fibroblasts, quinacrine treatment determined the accumulation of the cleaved and lipidated autophagic effector, LC3 II in HEK 293a cells .Myc-tagged construction of human OCT-1 to -3 were encoded in vectors that were transfected into HEK 293a cells . The antubstrate , was sigubstrate . The OCTubstrate . Howeverin vitro  under the form of discrete bright dots dispersed in the alveolar structure . In lungs dissected from quinacrine-treated mice (40 mg/kg/day), the green fluorescence of the drug could be observed as a discontinuous dot pattern  or with the saline vehicle were homogeneized and the extracts were submitted to immunoblotting. In lungs, a dose-dependent significant accumulation of p62/SQSTM1 was observed  and high lipophilicity (logP 5.67); considerations relevant for its use in vivo are its low toxicity  and the fact that its major metabolite, a desethyl secondary amine . These predictions were verified in murine models, including lung tissue from quinacrine-treated mice . Autophain vitro and in vivo, V-ATPase-mediated cation sequestration is associated, above a certain threshold, to autophagic accumulation and feed-back lysosomogenesis in murine models.The tissue distribution of quinacrine in dosed mice varies considerably from one organ to the other, but no mechanism modulating the cellular uptake of micromolar quinacrine concentration levels has been isolated. As shown 10.7717/peerj.1314/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.1314/supp-2Figure S1β-actin was performed to document equal loading of tracks. Representative of three separate experiments.Parallel immunoblotting for Click here for additional data file.10.7717/peerj.1314/supp-3Figure S2P < 0.05). Only the co-treatment with the PGP inhibitor elacridar significantly changed the uptake of doxorubicin into CEM-VLB cells .Cells were treated at 37 °C for 30 min with doxorubicin (2.5 µM), with optional co-treatment applied 15 min before doxorubinin (bafilomycin A1 100 nM or elacridar 5 µM). (A) Transmission and epifluorescence microscopy of representative cells (600×) are represented side by side with cytofluorometric distribution of the drug-associated fluorescence. (B) Mean cell red fluorescence in replicated experiments. The effect of drugs on doxorubicin uptake was tested with ANOVA for CEM cells (non-significant) or CEM-VLB cells  Transmission and epifluorescence microscopy of representative cells (600×). (B) Cytofluorometric distribution of the drug-associated fluorescence in the 2 cell subtypes.Click here for additional data file.10.7717/peerj.1314/supp-5Figure S4Morphological evidence of fluorescent drug uptake  in perinuclear vacuoles and/or nuclei of HEK 293a cells and effect of co-treatment with the V-ATPase inhibitor bafilomycin A1 on the drug uptake and subcellular distribution.Click here for additional data file.10.7717/peerj.1314/supp-6Figure S5Uptake of quinacrine into HEK 293a cells as measured using extract-associated fluorescence: effect of quinacrine concentration on the uptake during a 30-min period as modified by an optional bafilomycin A1 co-treatment.Click here for additional data file.10.7717/peerj.1314/supp-7Figure S6β-actin to document equal loading of tracks and the densitometric evaluation of LC3 I and II cell contents in replicated experiments and reported as histograms. The effect of the autophagic inhibitor spautin-1 was validated. LC3 I concentration between groups did not significantly differ (ANOVA). LC3 II values were heterogeneous .The figure includes a representative immunoblot of total cell extract revealed with anti-LC3 antibodies, a matched immunoblot for Click here for additional data file.10.7717/peerj.1314/supp-8Figure S7(A) Microscopy. Presentation as in Click here for additional data file.10.7717/peerj.1314/supp-9Figure S8P < 0.01). ∗P < 0.01 vs. mock-transfected cells.(A) Immunoblot for the myc tag of the constructions. Representative of 2 experiments. (B) Direct immunofluorescence (AlexaFluor-488 conjugated anti-myc tag antibody) of fixed and permeabilized HEK 293a cells transiently expressing OCTs. (C) Uptake of MPP+ (100 nM) as influenced by the expression of OCTs. Results are expressed as fold of the values recorded in mock-transfected cells . Values were significantly heterogeneous  Effect of the multivalent OCT inhibitor decynium-22 on the transport of quinacrine in total HEK 293a cell extracts. (B) Effect of overexpression of myc-tagged OCTs on the transport of quinacrine by transfected HEK 293a cells .Click here for additional data file.10.7717/peerj.1314/supp-11Figure S10t test .The autofluorescence is quantified in the extracts of saline-treated animals and was compared to the fluorescence in organ extracts from drug-treated animals using Student’s Click here for additional data file.10.7717/peerj.1314/supp-12Figure S11Quinacrine uptake, judged from fluorescence, is not present in red blood cells, but present in scattered leukocytes. Original magnification 600× (inserts at the right show some of the observed leukocyte morphologies).Click here for additional data file.


The expression levels of anoctamin 1 , a calcium-activated chloride channel (CaCC), are significantly increased in several tumors, and inhibition of ANO1 is known to reduce cell proliferation and migration. Here, we performed cell-based screening of a collection of natural products and drug-like compounds to identify inhibitors of ANO1. As a result of the screening, idebenone, miconazole and plumbagin were identified as novel ANO1 inhibitors. Electrophysiological studies showed that idebenone, a synthetic analog of coenzyme Q10, completely blocked ANO1 activity in FRT cells expressing ANO1 without any effect on intracellular calcium signaling and CFTR, a cAMP-regulated chloride channel. The CaCC activities in PC-3 and CFPAC-1 cells expressing abundant endogenous ANO1 were strongly blocked by idebenone. Idebenone inhibited cell proliferation and induced apoptosis in PC-3 and CFPAC-1 cells, but not in A549 cells, which do not express ANO1. These data suggest that idebenone, a novel ANO1 inhibitor, has potential for use in cancer therapy. Idebenone is currently being investigated for the treatment of a number of mitochondrial and neuromuscular diseases, such as Friedreich’s ataxia (FRDA), mitochondrial encephalopathy with lactic acidosis and stroke-like episode (MELAS) and Duchenne muscular dystrophy (DMD), due to its ability to serve as an electron carrier in the mitochondrial electron transport chain and its powerful antioxidant activity. Beneficial clinical effects of idebenone have been reported in the idebenone treated patients with FRDA , MELAS  and DMD  –36. In ainh-A01 showed similar response on cell proliferation (We showed that idebenone potently inhibited the ANO1 activity in YFP fluorescence quenching assay  and elecferation . This referation  and 5A. max) of parental and total idebenone were 22.4 and 8,158 ng/mL, respectably [In clinical trial, patients have been treated with idebenone up to 2,250 mg/day. When healthy male subjects received a single oral dose of 750 mg of idebenone three times per day, the maximum plasma concentrations (Cpectably . The ver50 in the range 10–20 μM and 3–10 μM, respectively [In this study, we also showed that miconazole and plumbagin potently inhibited ANO1 channel activity without altering the intracellular calcium concentration in FRT and HT-29 cells Figs  and 2C. ectively , 26–28. In In conclusion, this study shows that idebenone is a novel potent inhibitor of ANO1, and it did not affect intracellular calcium signaling and CFTR chloride channel activity. Idebenone potently inhibited ANO1/CaCC channel activity in PC-3 and CFPAC-1 cells, and significantly reduced cell proliferation, inhibited cell migration and induced apoptosis in these cells. These data suggest that idebenone may be a powerful candidate in the development of novel therapeutic agents for the prevention and treatment of cancer.


Here, we investigated the correlation between alpha1D-adrenergic receptor  and the transient receptor potential vanilloid type 1 (TRPV1) expression levels in human PCa tissues and evaluated the ability of alpha1D-AR to cross-talk with TRPV1 in PCa cell lines.There is evidence that calcium  by cytofluorimetry. Moreover, alpha1D-AR and TRPV1 colocalization was investigated by confocal microscopy in PCa cell lines and by fluorescence microscopy in benign prostate hyperplasia (BPH) and PCa tissues. Cell proliferation was assessed by BrdU incorporation. Alpha1D-AR/TRPV1 knockdown was obtained using siRNA transfection. Signalling pathways were evaluated by measurement of extracellular acidification rate, Ca2+ flux, IP3 production, western blot and MTT assay.The expression of alpha1D-AR and TRPV1 mRNAs are increased in PCa compared to BPH specimens and a high correlation between alpha1D-AR and TRPV1 expression levels was found. Moreover, alpha1D-AR and TRPV1 are co-expressed in prostate cancer cell lines and specimens. Noradrenaline (NA) induced an alpha1D-AR- and TRPV1-dependent protons release and Ca2+ flux in PC3 cell lines; NA by triggering the activation of phospholipase C (PLC), protein kinase C (PKC) and extracellular signal-regulated kinase 1/2 (ERK1/2) pathways stimulated PC3 cell proliferation, that was completely inhibited by clopenphendioxan (WS433) and capsazepine (CPZ) combination or by alpha1D-AR/TRPV1 double knockdown.The levels of the alpha1D-AR and TRPV1, that is involved in the control of PC3 cell proliferation. These data strongly support for a putative novel pharmacological approach in the treatment of PCa by targeting both alpha1D-AR and TRPV1 channels.We demonstrate a cross-talk between alphaThe online version of this article (doi:10.1186/1471-2407-14-921) contains supplementary material, which is available to authorized users. Wilma Quaglia, School of Pharmacy, University of Camerino i increase in Fluo-3-loaded PC3 cells vehicle-treated (control) or treated with WS433 or/and CPZ was evaluated by FACS analysis. Data shown are the mean ± SD of three separate experiments. (C) The measurement of IP3 levels performed as above described in PC3 cells vehicle-treated (control) or treated for different times with WS433 (1 μM) or/and CPZ (1 μM). Data shown are the mean ± SD of three separate experiments. (D-E) Lysates from PC3 cells vehicle-treated (control) or treated for different times with WS433 (1 μM) or/and CPZ (1 μM) were separated on SDS-polyacrylamide gels, transferred and then blotted with anti-pERK, ERK and anti-phospho-(Ser) PKC substrate Abs. The GAPDH protein level was evaluated as a loading control. Data shown are representative of one out of three separate experiments. (F) Cell growth was assessed by MTT assay in PC3 cells treated for 24 h with vehicle (control) or with chelerythrine (0.05-5 μM), PD98059 (1–100 μM) and U73122 (0.5-50 μM). Data shown are the mean ± SD of three separate experiments. Statistical analysis was performed by comparing chelerythrine, PD98059 and U73122 treated cells with control (*), p ≤ 0.01. (G) The proliferation of PC3 cells was analysed by labelling with BrdU. The cells treated with WS433 (1 μM) or CPZ (1 μM) were stained with an anti-BrdU FITC-conjugated Ab and analysed using a FACScan flow cytometer. Data shown are representative of one of three separate experiments. (TIFF 13 MB)Additional file 2: Figure S1: Effects of WS433 and CPZ antagonists and chelerythrine, PD98059, U73122 inhibitors in PC3 cells. 1D-AR and TRPV1 genes in PC3 cells. (A) The α1D-AR and TRPV1 mRNA levels were evaluated by qRT-PCR in siGLO-, siα1D-AR- and siTRPV1-transfected PC3 cells. The relative α1D-AR and TRPV1 expression levels, normalised to the β-actin mRNA level, were calculated using siGLO as a calibrator. (B) Lysates from siGLO-, siα1D-AR- and siTRPV1-transfected PC3 cells were separated by SDS-polyacrylamide gel electrophoresis and probed with anti-α1D-AR or anti-TRPV1 Abs. The GAPDH protein level was evaluated as a loading control. Representative immunoblots are shown. (C) FACS analysis was performed in PC3 cells double silenced for α1D-AR and TRPV1 genes. Silenced cells were double-stained with anti-TRPV1 and anti-α1D-AR Abs followed by respective secondary Abs. Data shown are representative of one of three separate experiments. (TIFF 3 MB)Additional file 3: Figure S2: Silencing of the α