The Monty Hall Dilemma (MHD) is a two-step decision problem involving counterintuitive conditional probabilities. The first choice is made among three equally probable options, whereas the second choice takes place after the elimination of one of the non-selected options which does not hide the prize. Differing from most Bayesian problems, statistical information in the MHD has to be inferred, either by learning outcome probabilities or by reasoning from the presented sequence of events. This often leads to suboptimal decisions and erroneous probability judgments. Specifically, decision makers commonly develop a wrong intuition that final probabilities are equally distributed, together with a preference for their first choice. Several studies have shown that repeated practice enhances sensitivity to the different reward probabilities, but does not facilitate correct Bayesian reasoning. However, modest improvements in probability judgments have been observed after guided explanations. To explain these dissociations, the present review focuses on two types of causes producing the observed biases: Emotional-based choice biases and cognitive limitations in understanding probabilistic information. Among the latter, we identify a crucial cause for the universal difficulty in overcoming the equiprobability illusion: Incomplete representation of prior and conditional probabilities. We conclude that repeated practice and/or high incentives can be effective for overcoming choice biases, but promoting an adequate partitioning of possibilities seems to be necessary for overcoming cognitive illusions and improving Bayesian reasoning. Bayesian reasoning has primarily been investigated in the context of imaginary scenarios, in which participants are required to derive a posterior probability  from explicit statistical information. An exception can be found in research with the Monty Hall Dilemma (MHD), where Bayesian reasoning has been studied with both imaginary scenarios and repeated practice. Differing from typical Bayesian problems, priors and conditional probabilities in the MHD have to be inferred, either by learning reward probabilities or by reasoning from the presented sequence of events. By reviewing the main difficulties and interventions for improving either choice or probabilistic judgments in the MHD, two different causes of failures are introduced: (1) emotional-based choice biases (switch aversion and/or the endowment effect), and (2) cognitive limitations in understanding and representing probabilities. We argue that while the first cause produces illusions of control, regret, or distortions in the memory of past choice-outcome events, the second one promotes illusions of equiprobability and/or distortions in understanding the conditions of the game. The present review shows that both causes can independently and simultaneously bias choice and probabilistic judgments. Furthermore, whereas choice biases can be overcome by extended practice or by high incentives, overcoming the erroneous default intuition requires explicit instruction about the correct partitioning of probabilities. Implications for improving Bayesian reasoning are also discussed.cognitive illusions or mental tunnels in which even the finest and best-trained minds get trapped”  swap their original selection for the other still closed door [switch]. The naïve reader would likely believe that each of the remaining two options has an equal probability of containing the prize, as often observed in the literature . This common illusion has been attributed to a misapplication of the equiprobability principle .The MHD is a good example of a counterintuitive decision-making problem, considered to be “the most expressive example of trapped” . In a firinciple  due to trinciple . SpecifiNevertheless, the final choice is generally neither fully coherent with the actual distribution of chances, nor with the mis-application of the equiprobability principle. A large majority of participants prefer to stick with the original choice , a tendeP(D3| H1) = P(D3) = 1/2], the posterior probability of such door containing the prize, conditioned to the elimination of door 3, is the same as its prior probability of containing the prize [P(H1| D3) = P(H1) = 1/3]. In contrast, given that the probability of revealing door 3, conditioned to the prize being hidden in the remaining door 2, is doubled [P(D3| H2) = 2P(D3) = 1], the posterior probability of door 2 hiding the prize, conditioned to the opening of door 3, also doubles [P(H2| D3) = 2P(H2) = 2/3].From a Bayesian perspective, understanding the optimal solution in the MHD requires realizing that the elimination event is conditioned on both the first choice and on the location of the prize . ConsideTable 1). In sum, understanding the MHD requires being able to distinguish conditional and unconditional events, or conditions in which probabilities have to be updated from conditions in which probabilities remain the same. In the following sections we review the difficulties found both in learning to choose optimally and in correct (explicit) Bayesian reasoning in the MHD in order to suggest causes and possible remediation.In other words, the conditions of the elimination have two main implications: (a) the winning probability for the selected door cannot change since it is conditioned to an unconditional event , and (b) the winning probability for the remaining door doubles, as the opening of a non-selected door is conditioned on the current location of the prize , or of the specific sequence of choices associated with the highest reward probability . The latter seems to explain pigeons’ tendency to choose more optimally than humans in analogous MHD tasks . Nevertheless, a larger number of trials appear insufficient for maximizing optimal choice  or for ewhy initial choice tendencies are not optimal. Supporting this claim, significant increments in optimal choice in the MHD have been observed even without explicitly noticing its advantage, although the general tendency to choose optimally (switch) is much weaker than when noticing the switching advantage  with a more complete representation of six arrangements . Results showed that the three-arrangements version promoted more correct responses. The benefit of the simplified representation was interpreted as a consequence of its higher resemblance to a natural frequency format (__ out of 3) and the simplified representation (three arrangements) than between the question and the complete version (six models). So, it could be the case that the more complete representation was less effective due to the additional steps needed to transform presented information into the form requested in the question.For example, y format . Howevery format . If the concrete frequency version, the explanation referred to a specific simulation of six games, analogous to the mental models representation . In the relative frequency version, less precise verbal quantifiers were used . Each version was presented with and without a diagram similar to the one presented in Figure 2. Results showed a significant effect of statistical format (concrete frequencies enhanced performance compared to abstract quantifiers), but no effect was found for the visual diagram. Hence, results supported the Related to the previous hypothesis, in According to the studies reviewed so far, probabilistic reasoning in the MHD can be improved through interventions that facilitate building a more complete representation of the different possibilities, or by prompting the required numerical expression in the format of the requested probabilistic judgment . Nevertheless, the extent to which any corresponding improvement indicates a complete understanding of both prior probabilities and the consequences of the elimination’s conditions, (as opposed to simply being a consequence of a match between representations), remains unclear. As developed in Section “Understanding the MHD: From Intuition to Bayesian Reasoning,” understanding the MHD implies understanding that, after the elimination of an option conditioned to the location of the prize, the winning probability of the first choice remains invariant, whereas the winning probability of the remaining option increases twofold.Table 1) might explain why the MHD is so hard. Based on this assumption, Related to the comprehension of the elimination’s constraints, a different and interesting approach to improve reasoning in the MHD was developed by How people represent the prior probabilities in the MHD has been rarely investigated. In most studies it is assumed that people have an accurate representation of the different probabilities before the elimination event that is, before inducing the equiprobability illusion. However, with the exception of the prior winning probability for the first choice, prior probabilities in the MHD are not necessarily obvious. Representing the winning probability of the initial choice is easy given the transparent correspondence between the initial information, three doors, and one prize, and the correct ratio 1 of 3 chances to win. However, representing the winning probability of the set including the two remaining doors might present a conflict between these two non-selected doors and the three initial doors. In fact, it has been observed that only about 50% of undergraduates understand that the chance of the non-selected options (held by the host or informant in the card game) hiding the ace is 2 of 3, with a common response instead being 1 of 2 . Still mWeak representation of uncertain information causes vulnerability to biases and/or to conservative behavior . SimilarIn sum, a large number of the undergraduates that participate in the MHD experiments do not have adequate knowledge to understand and/or represent prior and conditional probabilities in the MHD . Specifically, controls the running of specific reasoning procedures, and the reflective, which decides which reasoning algorithm to apply and/or whether or not to invest more effort into the task. Therefore, according to this proposal, overriding an erroneous response produced by the autonomous mind . One of the relevant triggering conditions for reflection is the detection of conflicting beliefs, which tends to reduce confidence in the correctness of the response . In the two final, initially equal, doors would have likely prevented finding the correct solution. In this sense, reasoning failures in the MHD could be attributed to automatic processes which build a particularly vivid default mental model of the task, and correspondingly strong justification of its correctness, rather than to a weakness of the reflective mind per se.Related to previous conflict (2), there is evidence that noticing it does not improve the chances to override the default intuition . For exareflective mind might indeed notice certain conflicting information (conflict 2 in previous section), but the relevant information needed to correct the error in the default intuition  is either not available or ignored. Accordingly, the efficacy of interventions aimed at improving Bayesian reasoning in the MHD would depend on the available reasoner skills and/or external hints which enhance the building of a more complete representation of the task. According to the present review, the interventions that have been shown to be the most effective are the ones promoting a different partition of the probability space (each of the three options separately [P(each option) = 1/3], understanding the MHD requires modeling the winning probability of each set of possibilities corresponding to each actor . Notice that with this representation, and with the additional knowledge that the host for sure has at least one null option that must be shown, no other computation is needed (see Table 2).As commented above, participants noticing the switching advantage in the repeated MHD solved the contradiction with the default intuition by building an alternative explanation able to preserve it. This suggests that the ty space . InsteadIn sum, as observed in other Bayesian problems, the correct partition of the problem space of probabilities or corresponding set–subset structure is crucial for correct reasoning . As alsoThe strong counterintuitivity of the MHD has intrigued people for decades. What is it about the MHD that makes it so hard for people to know that switching is the best course of action to win the prize? And on top of that, what is it that generates such strong disbelief even if it is realized that switching is better? Assuming the random assignation of the prize, it is clear that, in the initial stage of the game, most people would correctly assign to each alternative the same probability of hiding the prize. It is after the first choice is already made and the second choice to stick or switch is offered that the dilemma develops. The trouble starts with the initially built representation of the task upon which this second decision is based. On the one hand, emotional biases such as anticipation of regret and the endowment effect make people opt for sticking. On the other hand, it has also been suggested that the incomplete representation of the different possible courses of action is normally mediated by ignorance about the constraints involved in the elimination of one option. Nevertheless, as argued in this review, the initial partition between three equally likely options instead of two unequal sets of possibilities (contestant’s and host’s possibilities) seems also to be an important determinant, frequently ignored, for the difficulty in overcoming the equiprobability illusion in the final two-choice scenario.The relevance of ensuring a correct initial partition of the probability space, combined with understanding that there is a null option within the non-selected partition, is supported by the observation that the best interventions shown to improve Bayesian reasoning in the MHD are the ones promoting the representation of the possibilities of each actor (contestant and host). Furthermore, the dissociation observed between the interventions enhancing optimal choice (repeated practice or increased incentives) and the ones enhancing correct reasoning (explicit partitioning of possibilities) is coherent with current dual process theories of thinking e.g.,  and withThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Saccharomyces cerevisiae has two half transporters that heterodimerize and plants have a single peroxisomal ABC transporter that is a fused heterodimer and which appears to be the single entry point into peroxisomes for a very wide variety of β-oxidation substrates. Our studies suggest that the Arabidopsis peroxisomal ABC transporter AtABCD1 accepts acyl CoA substrates, cleaves them before or during transport followed by reactivation by peroxisomal synthetases. We propose that this is a general mechanism to provide specificity to this class of transporters and by which amphipathic compounds are moved across peroxisome membranes.Peroxisomes are arguably the most biochemically versatile of all eukaryotic organelles. Their metabolic functions vary between different organisms, between different tissue types of the same organism and even between different developmental stages or in response to changed environmental conditions. New functions for peroxisomes are still being discovered and their importance is underscored by the severe phenotypes that can arise as a result of peroxisome dysfunction. The β-oxidation pathway is central to peroxisomal metabolism, but the substrates processed are very diverse, reflecting the diversity of peroxisomes across species. Substrates for β-oxidation enter peroxisomes via ATP-binding cassette (ABC) transporters of subfamily D; (ABCD) and are activated by specific acyl CoA synthetases for further metabolism. Humans have three peroxisomal ABCD family members, which are half transporters that homodimerize and have distinct but partially overlapping substrate specificity;  Peroxisomes are single membrane-delimited organelles found in almost all eukaryotic cells. Originally named for their peroxidative metabolism, it has now become appreciated that they possess multiple, diverse metabolic capabilities and that these vary between organisms and even between cell types or developmental stages of the same organism ,2 New fuin vitro to allow transmembrane passage of small solute molecules with a molecular mass below 400 Da  [CTS gene is expressed throughout the plant and encodes a full-sized transporter with two homologous but distinct halves fused in a heterodimer in the arrangement [TMD1] (the transmembrane domain)–[NBD1] (nucleotide-binding domain)–[TMD2]–[NBD2] ] –24. The ]–[NBD2] . cts Mutcyl-CoAs , suggestArabidopsis, the CTS (COMATOSE) locus was identified in genetic screens for positive regulators of germination . A. Acts muCTS and Pxa1/2p also exhibit sequence asymmetry in TMDs  which poIn recent years, much progress has been made in understanding the physiological functions and biochemical roles of peroxisomal ABC transporters, with the emergence of exciting, novel mechanistic insights. Future challenges include elucidating the transport mechanism in further detail and relating this to the as yet elusive structure of ABC subfamily D proteins.


The aim of this study was to show that B1 imperfections contribute significantly to the offset. We further introduce a simple method to reduce the systematic error in T2 by discarding the first echo and using the offset fitting approach.T2 relaxometry has become an important tool in quantitative MRI. Little focus has been put on the effect of the refocusing flip angle upon the offset). We further performed T2 relaxometry in a phantom at 9.4T magnetic resonance imaging scanner and used the same methods for post-processing as in the extended phase graph simulated data. Single spin echo sequences were used to determine the correct T2 time.Signal curves of T2 relaxometry were simulated based on extended phase graph theory and evaluated for 4 different methods (inclusion and exclusion of the first echo, while fitting with and without the offset depends on the refocusing pulse, the echo spacing and the echo train length. The systematic error could be reduced by discarding the first echo. Further reduction of the systematic T2 error was reached by using the offset as fitting parameter. The phantom experiments confirmed these findings.The simulation data showed that the systematic error in T2 and the offset as a fitting parameter and discarding the first echo is a fast and easy method to minimize the error in T2, particularly for low to intermediate echo train length.The fitted offset parameter in T2 relaxometry is influenced by imperfect refocusing pulses. Using the  T2 relaxometry is a frequently used method of magnetic resonance imaging (MRI), particularly in preclinical and clinical research. Ever since the first publication in 1971 by Damadian [The “gold standard” method for acquiring T2 relaxometry data is the use of multiple single Spin Echo (SE) sequences with different echo times (TE) are geneAlthough those potential sources of systematic errors in T2 calculation are known, more often than not, post-processing and data-fitting techniques do not account for them. Data from T2 relaxometry are most often fitted to a simple exponential curve:o is the proton density and TE is the echo time. In most cases k and So are merged together to a single factor, because true proton density is hard to separate from the signal gain caused by the measurement process itself.Where k is a proportionality constant subsuming signal gain or attenuation by the scanner’s hard-/software, Soffset is thought to represent a non-zero baseline taking into account signal that may not have converged towards zero. While this approach is sometimes used without explanation for the non-zero baseline [Besides this simple mono-exponential fit, sometimes an offset or baseline is introduced:baseline , some grbaseline , 19, andbaseline  or Ricanbaseline , 22.offset. By using extended phase graph (EPG) theory [The aim of this study is to show, that B1 inhomogeneities and imperfect refocusing pulses contribute significantly to the ) theory –26 we aio and offset. When simulating the data with EPG theory we have the ability to alter the following parameters; T2, T1, ESP, ETL, FA and So. It has been shown in previous work that T1 does not alter the curves to a discernible degree [o = 1000a.u., T2 = 100ms, ETL = 20–50ms in steps of 2ms, ESP = 10–40 in steps of 2 and FA = 120°-180° in steps of 20°. To monitor the accuracy of the four fitting methods for different T2, simulation were undertaken with T2 times of 20ms, 60ms, and 100ms. For these simulations we used an ESP time of 5ms with an ETL of 32 (to ensure that the complete T2 decay is covered by the echoes) and a FA of 120°. Furthermore, to monitor the effect of noise, the simulations were rerun with added Rican noise. The used algorithm to include the noise was Sr = F*Ra+Sc, where Sr is the resulting signal with noise, F the noise factor , Ra random generated number and Sc is the noise-free signal from the EPG simulation. These simulation were ran 1000 times to monitor not only the systematic error but the statistical error as well.T2 relaxation curves were simulated based on the EPG method . Before e degree . Thus, woffset, there is no need to distinguish between different sources of B1 inhomogenieties. Therefore EPG was used to simulate the measured signal for imperfect refocusing pulses without considering the origin of this imperfection.It is important to note that the EPG algorithm itself does not account for B1 inhomogeneity within the slice profile. One could follow the approach of Lebel et. al.  and breaTo test the results from the EPG based simulations, phantom measurements were performed using a 50ml tube filled with a 2.5% agarose to water mixture(to ensure that 3*T1 < TR). Images were performed on a Bruker 9.4T horizontal bore NMR scanner  with a four channel phased array surface coil. Firstly, spin echo sequences with single refocusing pulse were acquired to obtain the T2 time without systematic errors by B1 inhomogeneities. Following this, MSE sequences were used at different pulse angles . The following parameters were the same in all spin echo and MSE sequences: TR = 3000ms, matrix = 150 x 150, FoV = 30mm x 30mm, slice thickness/number = 2mm/1, slice selective pulses, acquisition time = 7min 30sec). We performed 45 different spin echo sequences with TE ranging from 10ms to 450ms with 10ms spacing. For the MSE, we used ESP = 10ms and ETL = 45 to obtain the same echo times as used for the spin echo sequences.o and the offset:Curve fitting was undertaken in MATLAB, release 2014a® (MathWorks Inc.), using the Levenberg-Marquardt nonlinear least squares algorithm providedall echoes were fitted with all echoes were fitted with the first echo was discarded and the remaining echoes were fitted with the first echo was discarded and the remaining echoes were fitted with fit-T2in)|/T2in)*100) and So (dSo = |(Sofit-Soin)|/Soin)*100).To determine the systematic error coming from curve fitting the relative deviation was calculated for T2  and (4), where the offset is a fitting parameter, offset was 0 for all ETL an ESP. Thus, the plots for FA = 180° have not been included in Figs For EPG simulations using 180° as refocusing angle, T2 and S0 of the fitted S0 for the different fitting methods. As with δT2, δS0 increases markedly with decreasing FA. Discarding the first echo leads to a more accurate determination of S0 (i.e. δS0 decreases) for high to intermediate ESP, whereas there is little or no reduction of δS0 for low ESP. Other than with δT2, neither ETL nor the use of the offset as fitting parameter have a distinct influence on the accuracy of S0.Method four was shown to have the closest approximation to the actual T2 time for T2 = 100ms. In order to evaluate whether this result is still holds for different T2s the simulation where rerun. When adding Rican noise to the signal prior to T2 fitting, the offset (method 4) increased for all T2, as both imperfect RF pulses and noise floor contribute to the offset in this case . HoweverSignal decay in the phantom experiments  correspoCorresponding to our findings in the EPG simulations the relative deviation of T2 of the MSE from the reference value of the spin echo sequences increased with decreasing FA. Discarding the first echo reduced the relative T2 deviation for all FA. Relative T2 deviation was further reduced, if the offset was used as a fitting parameter (method 4). However, even for an 180° pulse as flip angle there still was a systematic error between 3.5% (method 1) and 3.6% (method 4) which is most likely due to an imperfect RF setup, B1 variations within the slice profile, or gross B1 inhomogeneity, which is known to occur particularly at high field strength .offset fitting parameter in T2 relaxometry, while past published work pointed to the offset as being a compensation factor for either long T2 time within a compartment [offset. The offset is particularly high for low ESP and low ETL, a setting which is often used particularly in T2 relaxometry in humans, as clinical MRI scanners usually limit the number of echoes and the user often reduces ESP to keep TR and thus, the acquisition time, as short as possible. and maintain the integrity of the curve for shorter T2 times .Mono-exponential fitting is not the proper method for data fitting in T2 relaxometry due to its known inaccuracies in presence of B1 inhomogeneities. As mono-exponential fitting methods, however, are used in the majority of clinical and preclinical studies, our aim was to minimize the error in T2 by two modifications of mono-exponential fitting, that can be easily performed, are not time-intensive and have already been proposed: introducing an offset and/or discarding the first echo. By simulations based on EPG theory we could show, that B1 inhomogeneities and imperfect refocusing pulse angles provide major contributions to the partment , 14, 19 partment , 20. As offset is predominately due to an error in the refocusing FA, this does not mean, that noise and long T2 components do not play a role. Long T2 components, due to several proton pools with different T2 times or due to partial volume effects, always influence the determination of T2 and the offset, in addition to the effects of imperfect B1. If the longer T2 component is not covered by the entire ETL, then the offset would be higher. One could check for possible bi-exponential curves using a statistical Fχ test [offset as an additional parameter [Although we show that the  Fχ test  and thenarameter .Noise may also play a role in the final value of the offset, particularly in the lower values where there is a Rican and or Rayleigh distribution , 33. It offset, within method 2, as an additional fitting parameter does not lead to less systematic error in T2. Quite contrary, the systematic error in T2 increases by adding offset as fitting parameter, particularly for low ESP and low ETL, where the offset is high.Although the fitting offset parameter is strongly influenced by imperfections of the refocusing pulse angle, we found that using the offset and as a major source of error in T2quantification. One easy way to account for large portions of this error is to discard the first echo for curve fitting [We have identified the signal oscillations in the early echoes as a major contribution to the  fitting –36. We, It is important to note that the method of discarding the first echo will not be able to compensate for the systematic error in T2 completely, as all echoes are affected by imperfect refocusing pulses. Several authors have put forward that if there is a discrepancy in the refocusing FA, all odd echoes will not be completely refocused and all even echoes will refocus all isochromats into the transverse plane , 38. Frofitting parameter (method 4). This method works exceptionally well with low to intermediate ETL: δT2 was less than 6% for all ESP and all ETL≤32, even for a refocusing pulse of 120°. As in clinical as well as in experimental settings, most often relatively short echo train lengths are used, method 4 provides an easy-to-use, fast and reliable method to correct for B1 imperfections. Although it is thought that long ETL should be used to gain the most accurate determination of T2 [We could show, that most of the remaining systematic error in T2 after discarding the first echo can be eliminated by using the offset as a on of T2  it is seoffset as a fitting parameter and discarding the first echo can minimize the error in T2, it should be pointed out that this method does not compensate errors in the refocusing pulse completely and further correction of the T2 may be of interest [Although using the interest , 40. In interest  or by painterest . A more interest . In thisoffset in T2 relaxometry is influenced by imperfect refocusing pulses. Using the offset as a fitting parameter and discarding the first echo is a fast and easy method to minimize the error in T2, particularly for low to intermediate echo train length. We expect that by identifying the optimal method for T2 determination, variations in reported times for specific tissue types and pathologies will be minimized, thus achieving better results for T2 as a quantitative measure.In conclusion, we have shown that the S1 Dataset(ZIP)Click here for additional data file.


In this study we found that iberiotoxin  and chloro both completely blocked spontaneous transient outward currents (STOCs) in single mouse tracheal smooth muscle cells, which suggests that chloro might block BKs. We further found that chloro inhibited Ca2+ sparks and caffeine-induced global Ca2+ increases. Moreover, chloro can directly block single BK currents completely from the intracellular side and partially from the extracellular side. All these data indicate that the chloro-induced inhibition of STOCs is due to the blockade of chloro on both BKs and ryanodine receptors (RyRs). We also found that low concentrations of chloro resulted in additional contractions in tracheal rings that were precontracted by acetylcholine (ACH). Increases in chloro concentration reversed the contractile actions to relaxations. In the presence of IbTx or paxilline (pax), BK blockers, chloro-induced contractions were inhibited, although the high concentrations of chloro-induced relaxations were not affected. Taken together, our results indicate that chloro blocks BKs and RyRs, resulting in abolishment of STOCs and occurrence of contraction, the latter will counteract the relaxations induced by high concentrations of chloro.The participation of large-conductance Ca However, the role of BKs in chloro-induced relaxation is still undefined. In this study, we found that chloro blocks RyRs and BKs, which results in STOC abolishment and contraction occurrence, the latter will counteract chloro-triggered relaxation. These results suggest that BKs are involved in chloro-induced relaxation in precontracted ASM.It has been reported that chloro induced an increase in intracellular Cacted ASM . The relcted ASM , 2. Whilcted ASM . Recentlcted ASM  and othected ASM  have def2PO4SO4, 4.16 mM NaHCO3, 10 mM glucose, 10 mM HEPES, 0.34 mM NaHPO4·12H2O . The epithelium, cartilage, and connective tissue were removed. The trachealis tissues were minced and incubated for 22 min at 35°C in the above solution supplemented with 2 mg/ml papain, 1 mg/ml dithioerythritol, and 1 mg/ml bovine serum albumin (BSA). The partially digested tissues were then transferred to the above solution supplemented with 1 mg/ml collagenase H, 0.15 mg/ml dithiothreitol, and 1 mg/ml BSA. After incubation for 8 min, the well-digested tissues were washed and gently triturated in the above solution to yield single smooth muscle cells. Cells were stored on ice and used for experiments within 4 h.Single mouse tracheal smooth muscle cells were enzymatically isolated as previously described . Briefly2, 10 mM HEPES, 64 mM K-aspartate, and 3 mM Na2ATP . The extracellular solution contained 130 mM NaCl, 5.5 mM KCl, 2.2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES, and 5.6 mM glucose . The holding potential was set at −40 mV. The junction potential, capacitance, and series resistance were compensated. The 30 s step voltage pulses were applied from −40 to +10 mV with an increment of 10 mV to record STOCs.Ion channel currents were measured using an EPC-10 patch-clamp amplifier . BK-mediated STOCs were recorded using a classical whole-cell configuration . Patch p+ ion concentrations in the pipette and bath solutions. The intracellular solution contained 140 mM KCl, 1 mM MgCl2, 5 mM EGTA, 4.37 mM CaCl2 and 10 mM HEPES . The free Ca2+ ion concentration was 1 μM calculated using WEBMAXC STANDARD (http://www.stanford.edu/~cpatton/webmaxc/webmaxcS.htm). The extracellular solution contained 140 mM KCl, 1 mM MgCl2, 5 mM EGTA, 4.9 mM CaCl2 and 10 mM HEPES . The free Ca2+ ion concentration was 10 μM. Single channel currents were acquired at a digitization rate of 4 kHz and filtered at 1 kHz. Events were detected and all-point amplitude histogram and single channel open probability (Po) obtained using Clampfit 9 software  [Single BK currents  were rec2+ sparks, single mouse tracheal smooth muscle cells were loaded with 2.5 μmol/L fluo-4 AM. Ca2+ sparks were measured using an LSM 700 confocal scanning laser microscope  and Zen 2010 software . The frequency (sparks/s/μm) and amplitude of Ca2+ sparks were analyzed using Zen 2010 software and Interactive Data Language software  [2+ sparks and STOCs were performed by a method that combined a confocal scanning laser microscope with the patch-clamp amplifier [For the measurements of Camplifier .2+ changes were measured using confocal microscope as described previously [2+ level [The global Caeviously , which w2+ level .2·6H2O, 2 mM CaCl2, 10 mM HEPES, 10 mM glucose, pH 7.4) equilibrated with 5% CO2/95% O2. Connective tissue was removed and four cartilage tracheal rings were cut from the same distal tracheae. Each ring was mounted horizontally in a 10 ml organ bath chamber containing the above solution equilibrated with 5% CO2/95% O2. The preload was 0.3 g. During a 60-min equilibration period, the rings were washed every 15 min and then precontracted twice with ACH (10−4 M). After the rings rested for an additional 30 min, experimental measurements of tension were carried out.ASM tension was measured in mouse tracheal rings as previously reported . In briePapain, dithioerythritol, dithiothreitol, collagenase H, bovine serum albumin, 2-aminoethoxydiphenyl borate (2-APB), acetylcholine (ACH) and chloroquine (chloro) were purchased from Sigma. Iberiotoxin (IbTx), paxilline (pax), ryanodine and U73122 were purchased from Cayman Chemical. Fluo-4 AM and fura-2 AM were from Molecular Probes.t-test using Origin 9.0 software , and p < 0.05 was considered statistically significant.Results are expressed as means ± SEM. Comparisons between two groups were performed with the Student’s To study the role of BKs in chloro-induced relaxation, we first sought to determine whether these channels can be blocked by chloro. The voltage steps shown in We next assessed the dose-dependent inhibition of chloro on the STOCs. The STOCs were recorded at 10 mV, which were gradually inhibited following cumulative additions of chloro  induced a small increase in Ca2+ and inhibited the following 10 mM caffeine-induced Ca2+ increases . Thus, we observed the effect of 2-APB, an inhibitor of inositol 1,4,5-triphosphate receptor (IP3Rs), on the caffeine-induced Ca2+ elevations and found that 2-APB significantly inhibited the increases in Ca2+ triggered by 10 mM caffeine  inhibitor U73122 (1 μM) and repeated the same experiments and found that U73122 also inhibited caffeine-induced Ca2+ increases  values were calculated and presented above each trace  and current amplitude (at 60 mV) were significantly inhibited in the presence of chloro  and found that they were completely blocked by a specific blocker of BKs, IbTx  failed to inhibit an increase in Ca3Rs abolishing caffeine-induced Ca2+increases, however, why chloro induced a Ca2+ elevation  to abolish STOCs. The blockade of BKs induces a contractive component that decreases but does not prevent chloro-induced complete relaxation of precontracted ASM. These findings imply that BKs involve in chloro-induced ASM relaxation and that the activators of BKs and chloro might be the potential bronchodilators.S1 Figp < 0.05 (versus control); ** denotes p < 0.01 (versus control). These data show that chloro dose-dependently inhibits STOCs.(A) STOCs were recorded at 10 mV, which were gradually inhibited by chloro. (B) The dose-dependent inhibition of amplitude and frequency from 5 cells. * denotes (PDF)Click here for additional data file.S2 Fig2+ sparks from 8 cells were measured as described in p < 0.05; *** denotes p < 0.001. These experiments demonstrate that low concentrations of chloro do not completely block, but rather inhibit RyRs.Ca(PDF)Click here for additional data file.S3 Fig2+ increase.  Chloro (0.1 mM) induced a transient Ca2+ rise. Following the addition of the same concentration of caffeine, a small Ca2+ elevation was observed. These experiments demonstrate that chloro can induce Ca2+ increases and inhibit the following caffeine-triggered Ca2+ elevations. NS: p > 0.05. Caffeine (10 mM) repetitively triggered a similar transient Ca(PDF)Click here for additional data file.S4 Fig2+ increase, which was inhibited by the IP3R inhibitor 2-APB (B) ***: p < 0.001; NS: p > 0.05. This result indicates that caffeine-induced Ca2+ elevations arise partially from IP3R-mediated Ca2+ release.(A) Caffeine (10 mM) repeatedly induced a similar Ca(PDF)Click here for additional data file.S5 Fig2+ increases were measured using fluo-4 AM and confocal microscope, which were inhibited by the PLC inhibitor U73122 (B) ***: p < 0.001; NS: p > 0.05. This result suggests that PLC mediates caffeine-induced Ca2+ elevations.(A) Caffeine (10 mM)-induced Ca(PDF)Click here for additional data file.S6 Fig2+ increases measured with fluo-4 AM and confocal microscope. Following washout, caffeine-induced Ca2+ increases were still observed. (B) The summary results from 10 cells. ***: p < 0.001; NS: p > 0.05. These data imply that caffeine can induce Ca2+ elevations via RyR-independent pathway.(A) Caffeine (10 mM) and ryanodine (30 μM) were used to open and block RyRs, respectively, which induced Ca(PDF)Click here for additional data file.S7 Figp > 0.05. These data indicate that chloro can time-independently induce biphasic changes in force.(A) A typical experiment performed as described in (PDF)Click here for additional data file.S8 Figp < 0.01; ***: p < 0.001.(A) Following ACH-induced contraction reached a plateau, pax added and resulted in an additional contraction. (B) Summarized contractions from 4 rings. This result demonstrates that blockade of BKs results in contraction in ASM. **: (PDF)Click here for additional data file.


RNA-sequencing (RNA-seq) has become the tool of choice for transcriptomics. Several recent studies demonstrate its successful adaption to single cell analysis. This allows new biological insights into cell differentiation, cell-to-cell variation and gene regulation, and how these aspects depend on each other. Here, I review the current single cell RNA-seq (scRNA-seq) efforts and discuss experimental protocols, challenges and potentials. Transcriptome profiling has been a popular tool in molecular biological research for more than a decade. Mostly implemented by microarray technology, it has led to numerous insights and discoveries. These range from cell type specific single factors that were identified in differential expression screens to findings based on large portions of the entire transcriptome, such as disease signatures  and theFollowing its recent introduction, RNA-sequencing (RNA-seq) ,4 is rapThe analysis of the transcriptome in individual cells offers a number of advantages compared to cell-averaging experiments. Tissues or other cellular assemblages are heterogeneous even if a single, “traditional” cell type is concerned. This is particularly apparent from the study of immune cell types that are often defined based on the expression of surface markers. Improving experimental technologies reveal continuous expression ranges and fairly unrestricted combinatorial expression of surface markers . This meWhile tumor cell heterogeneity can be attributed to accumulating mutations, even genetically identical cells, under identical conditions, display high variability in their gene and protein expression levels. This is usually referred to as “noise”, defined as variance or standard deviation over mean . A numbeWhile standard microarray or RNA-seq experiments yield mean expression levels, distributions in single cells demonstrate that only a negligible portion of cells express mRNAs close to the actual mean levels. Depending on the skewness of the distributions, this could mean that cells that express certain mRNAs at “outlier” levels are functionally important, yet remain undetected by traditional experiments. Knowledge about the shape of the distributions can also be used to understand mechanisms that are involved in transcriptional regulation . RNA-seq ideally allows the accurate quantification of mRNA expression levels, covers the entire transcript lengths at equal representation at each position, and retains strand information. Depending on the experimental protocol, particularly for single cell RNA-seq (scRNA-seq), these goals are not simultaneously achievable with current methods.Sample processing for RNA-seq is largely based on traditional molecular biological protocols that were developed decades ago for common assays such as RT-PCR, northern blots or microarrays. The vast majority of RNA-seq methods thus include the basic steps of poly-(A)+ RNA isolation, fragmentation, reverse transcription, and amplification before the actual sequencing takes place.The selection of poly-(A)+ RNA is usually performed in order to suppress the “loss” of sequencing reads to structural RNAs such as rRNA and tRNA, which represent the bulk of cellular RNA. One disadvantage of this is that certain protein coding RNAs without the poly-(A) tail, such as replication dependent histones, are lost . Total RThe fragmentation step is carried out in order to produce many short RNA or DNA fragments that represent the original transcript. Various methods, including enzymatical, mechanical, and chemical ones have been used successfully. Depending on the exact protocol, the fragmentation serves to optimize the length for the sequencing machine, generates more reads, thus allowing more accurate quantification, and/or is used to reduce positional biases in the transcript representation.in vitro transcription (IVT), which are both popular means to amplify the material. Reverse transcription is necessary, since the suitability of RNA-dependent RNA polymerases [This bias is largely due to reverse transcription that is used to generate the DNA template for PCR or ymerases  for RNA ymerases , which iymerases .These biases can be strongly reduced if the fragmentation step is performed on the original RNA . This maFour different scRNA-seq strategies have been explored so far . The fir3 on its 3' end (and a suitable 5' sequence that will later serve as primer binding site for PCR amplification) anneals to these nucleotides as soon as the first strand is complete, and provides a template-extension. The RT thus switches from an RNA- to a DNA-template   exploitstemplate . ImportaThe first SMART-based scRNA-seq protocol was named “Single-cell tagged reverse transcription” (STRT) . The temAlthough it was published later, the second SMART-based scRNA-seq, “Smart-Seq”, is essentially a simplified version of the first one . No barcst strand primer. Its advantage lies in the linear mode of amplification, which, unlike PCR, does not exponentially deplete of sequences that are difficult to process [CEL-seq provides the latest addition among the tested scRNA-seq protocols . This st process . The abi process . CuriousCommon to all four scRNA-seq methods is the usage of two rounds of amplification (including the sequencing technology-specific sample preparation). It should be further noted that the SMART protocol does not lead to better transcript coverage in the sense that the full-length cDNA is synthesized more often. Instead, incomplete cDNAs are less likely to be carried on in the protocol. The downside of this is that transcripts, which could at least have partially contributed to the information gained by the experiment, are discarded.etc.) and differences in parameters such as PCR cycle numbers might have strong effects on the final results. A specific effort to evaluate all four methods against each other under identical conditions might be desirable, as it has been done for conventional RNA-seq protocols [st strand synthesis in CEL-seq, or barcoding may be used with the Tang protocol.It is difficult to directly compare the technical performance of the existing scRNA-seq schemes based on their original descriptions, aside from the obvious differences such as strand specificity or transcript coverage. The means to assess performance were largely different in the three works  compared to embryonic stem cells (ESCs) [The potency of scRNA-seq to reveal novel biological insights is explored in the original and follow-up studies of these. One widely used advantage of RNA-seq over microarrays is the possibility to detect splice variants, which has been demonstrated on single cell basis for all three PCR-based scRNA-seq protocols. The Smart-Seq and STRT works further illustrate how global gene expression profiles can be used to categorize cells into traditional cell types, while individual cells might exhibit closer relatedness to cell types other than the one they are classified as ,27. The  Elegans . Since ts (ESCs) . In a fis (ESCs) . This res (ESCs) . This deThe first few studies on scRNA-seq have proven the feasibility of this type of experimental assay. The true strength of the method will probably become fully apparent when it is implemented to study the characteristics of distributions of mRNA expression levels among individual cells. This is likely to yield decisive new insights into transcriptional regulation .in situ hybridization (smFISH) of at least a few hundred cells each under different conditions . This ns  pores of nanometer dimension that are inserted into membranes. Bases that pass through the pores are detected electronically or optically .Further help for sample preparation will be microfluidics systems such as provided by RainDance Technologies or Fluidigm. These have been mainly designed for targeted amplification and highly parallelized PCRs , but ha


Myc induced tumors. To test this role we interbred MMTV-Myc transgenic mice with E2F knockouts. Surprisingly, we observed that the loss of E2F2 sharply increased the percentage of lung metastasis in MMTV-Myc transgenic mice. Examining the gene expression profile from these tumors, we identified genetic components that were potentially involved in mediating metastasis. These genes were filtered to uncover the genes involved in metastasis that also impacted distant metastasis free survival in human breast cancer. In order to elucidate the mechanism by which E2F2 loss enhanced metastasis we generated knockdowns of E2F2 in MDA-MB-231 cells and observed increased migration in vitro and increased lung colonization in vivo. We then examined genes that were differentially regulated between tumors from MMTV-Myc, MMTV-Myc E2F2−/−, and lung metastases samples and identified PTPRD. To test the role of PTPRD in E2F2-mediated breast cancer metastasis, we generated a knockdown of PTPRD in MDA-MB-231 cells. We noted that decreased levels of PTPRD resulted in decreased migration in vitro and decreased lung colonization in vivo. Taken together, these data indicate that E2F2 loss results in increased metastasis in breast cancer, potentially functioning through a PTPRD dependent mechanism.In human breast cancer, mortality is associated with metastasis to distant sites. Therefore, it is critical to elucidate the biological mechanisms that underlie tumor progression and metastasis. Using signaling pathway signatures we previously predicted a role for E2F transcription factors in  Breast cancer has been shown to be a highly heterogeneous disease through genomic analysis of both human and the mouse tumor samples –7. ClassMyc model [Myc tumors would require additional genetic events in order to model metastasis found in c-Myc associated human breast cancer metastasis.To study breast cancer development and progression, one approach that has been used is the generation of genetically engineered mouse model systems. One well studied model is the highly metastatic MMTV-PyMT strain, where metastasis to the lung is observed in virtually all mice reaching tumor endpoint , 13. Conyc model  is poorlyc model –17. ThisE2F1 expression has been shown to be reduced in primary and metastatic breast carcinoma [E2F2 chromosomal region was also observed [Myc mice, an enrichment of E2F bound genes in the EMT/squamous subset of tumor samples was noted [E2F1 and E2F2 loss was shown to vastly reduce metastasis. Other studies have implicated the E2Fs in the tumor process, including an examination of Neu and Myc initiated tumors in the absence of various E2Fs with impacts on latency and metastasis that varied by model [E2F1 increased extravasation of circulating cancer cells from the endothelium [In mouse models of breast cancer, the E2F transcription factors have been predicted to be activated in many tumor models . Traditiarcinoma , 24 and observed . In the as noted . Recentlas noted . Specifiby model –29. In tby model  and thatothelium .Myc mouse model of breast cancer affected incidence and latency [E2F2 in Myc induced tumors dramatically increased breast cancer metastasis. To define the role of E2F2 in the metastasis process, we generated gene expression data with and without E2F2 in Myc induced tumors. Candidate genes regulating metastasis were identified and tested for roles in metastasis. This analysis demonstrated that a tumor suppressor gene, PTPRD [E2F2 to mediate metastasis.We have previously shown that the perturbation of activator E2Fs levels in the MMTV- latency . Here wee, PTPRD –34, may Myc tumors [E2F1 was lost and a delay in tumor onset when E2F2 or E2F3 was lost. Indeed, we saw that loss of E2F2 increased time to tumor onset by an average of 160 days . Here we observed that the loss of any E2F increased metastases in MMTV-Myc intiated tumors which are normally not highly metastatic (13%). However, only the loss of E2F2 significantly increased the percentage of tumor bearing mice with metastasis to the lung to 67% .Our previous research had predicted and demonstrated a role for E2F pathway activation in MMTV-c tumors . In thatE2F2 loss in Wap-Myc mice decreased time to tumor onset and did not describe a metastatic phenotype [Myc E2F reports, we sought to ensure that the metastatic effects were not a strain specific artifact. To do this, we interbred a separate MMTV-Myc transgenic, MMTV-Myc WT21 [E2F2 knockouts. With the loss of E2F2 in this strain, tumors developed 147 days earlier  which further confirmed our finding of the role of E2F2 in MMTV-Myc mediated metastasis.Other published work noted that henotype . Given tMyc WT21  with ther Figure . Importa% Figure , demonstMyc strains typically resulted in sections lacking metastases . Interestingly, this experiment demonstrated an enrichment of an invasive ovarian epithelial cancer geneset in the lung metastastic samples, suggesting that similar gene expression patterns could be shared .To begin to determine the mechanism by which loss of . Figure . Focusin. Figure . Given tE2F2 that resulted in an increase of lung metastasis in Myc induced tumors we examined gene expression data from tumors and metastases. The goal of this analysis was to identify genes that were regulated directly or indirectly by E2F2. Importantly, we sought to examine genes that also affected human metastatic outcome. The data analysis pipeline  curve where high levels of PTPRD were associated with poor DMFS outcomes  relative to low levels of PTPRD and was associated with basal subtype of human breast cancer ).Microarray analyses revealed that R Figure . Furthers Figure . The metPTPRD knockdown, we began by examining whether the mouse effects of E2F2 translated to human breast cancer. Using an shRNA approach we knocked down E2F2 levels in MDA-MB-231 cells. The knockdown of E2F2 was validated in stable clones through immunoblotting for E2F2 in relation to the Grb2 loading control . This increased propensity to migrate was also demonstrated through a wound healing assay (data not shown).In order to establish a system where we could assess the effects of l Figure . No altel Figure . Parentain vitro experiments only examine a portion of the functions necessary for metastasis, we examined the ability of the cells to colonize the lungs by injecting the cells into the bloodstream. As expected, in control cell lines this resulted in localized discreet colonization . Taken together our results in vitro and in vivo showed that knockdown of E2F2 increased the metastatic capability, correlating well with the increased metastasis in the MMTV-Myc E2F2−/− mouse model.Given that the n Figure . Strikins Figure . QuantitE2F2 knockdown increased metastatic potential, we then examined if loss of PTPRD would reduce metastasis in the same system. To this end, we used an shRNA approach to knockdown levels of PTPRD to 40% of wild type levels . No effects on proliferation were noted . In vivo, knockdown of PTPRD resulted in fewer metastatic lesions in the lung relative to controls after cells were injected retro-orbitally . This result reflected our gene expression finding in the MMTV-Myc mouse model where PTPRD was found to be upregulated by 2.83 fold in lung metastasis compared to MMTV-Myc tumor samples. This fold change difference was further validated by using qRT-PCR which showed that PTPRD RNA expression was increased by 2 fold in lung metastases samples compared to MMTV-Myc samples . Survival analysis showed that overexpression of PTPRD decreased time to distant metastasis in the basal subtype of human breast cancer  that is involved in various biological processes in cancer [PTPRD has been shown as tumor suppressor in glioma [PTPRD was shown to be deleted through array comparative genomic hybridization and copy number analysis [PTPRD in glioblastoma demonstrated that the loss of PTPRD led to the accumulation of active phospho-STAT3 in a p16Ink4A−/− mouse model [PTPRD was discovered through The Cancer Genome Atlas project to be a novel gene that was frequently mutated in addition to PTPN22, suggesting the emerging roles of protein tyrosine phosphatases in breast cancer associated biological processes [PTPRD was found to be hypermethylated in late-stage breast cancer [PTPRD in mediating breast cancer metastasis in conjunction with the loss of E2F2.n cancer . Specifin glioma , 41, livn glioma . In glioanalysis . Furtherse model . In brearocesses . Specifit cancer . Our datPTPRD is not predicted to be a direct transcriptional target of E2F2, analysis of the TCGA data revealed that there was potentially connection at the level of protein regulation, through BCAR1. Alternatively, PTPRD may function through STAT3 [STAT3 and Myc relationship [STAT3 levels has been shown to alter the expression of the oncogene c-Myc [Myc interacts with Rb/E2F [c-Myc binding site on the promoter of PTPRD, it is plausible that the loss of E2F2 allowed increased expression of PTPRD mediated by c-Myc. This increase of PTPRD could then lead to increased lung metastasis, a role for PTPRD and E2F2 that is unique to a subpopulation of breast cancer. Further analyses revealed that PTPRD overexpression was correlated to decreased time to metastasis in human basal subtype tumors. Taken together these results demonstrated that E2F2 and PTPRD expression in c-Myc tumors could act as additional risk factors associated with metastasis. Finally, although MYC is known to drive proliferation, it can also suppress metastasis [MYC signaling should avoid affecting E2F2 signaling to maintain metastasis suppression.While gh STAT3  given thtionship . Indeed,ne c-Myc –47. Moreh Rb/E2F , 49 and tastasis  and therMyc [Myc (WT21) [E2F2−/− [Myc E2F2−/− females were kept in continuous breeding cycles. Mice were palpated weekly for tumor presence and tumors were measured by calipers weekly until endpoint when primary tumor reached 20 mm in the largest dimension [Myc WT21 or MMTV-Myc WT21 E2F2−/− tumor into MMTV-Myc WT21 and MMTV-Myc WT21 E2F2−/− backgrounds. 20 mice per genotype were used in the transplantation experiment and tumors were monitored as previously mentioned. Primary tumors and lungs were harvested and submitted for histological analyses as previously mentioned.Animal use and husbandry was in accordance to institution and federal guidelines. MMTV-Myc , MMTV-Myc (WT21)  and E2F2[E2F2−/−  mice wer[E2F2−/− . MMTV-Myimension . Primary5 cells in 50 μl as previously described [Colonization assays were performed by retroorbital injection of nude mice at a concentration of 5 × 10escribed . Mice obPublicly available GEO datasets: GSE11121, GSE14020, GSE2034, GSE2603, GSE3494, GSE4922, GSE6532, GSE7390 and gene expression dataset from human breast cancer cell lines E-TABM-157 were obtained as well as their corresponding clinical annotations  and 5. TMyc and E2F2 activation, Myc and E2F2 signatures were applied as previously described to predict the pathway activation of Myc and E2F2 [Myc and E2F2.Validated training data from previous studies utilizing adenovirus infection of primary human mammary epithelial cells to build pathway signatures was used to identify samples with and E2F2 –56. BrieMouse gene expression dataset GEO24594 was merged with 6 lung metastasis samples (GEO71815). Briefly, CEL files were analyzed by Affymetrix Expression Console software to ensure that expressions were within bounds. Gene expression datasets were normalized using Microarray Suite 5.0 (MAS5) and Robust Multi-array Average (RMA) methods. Unsupervised hierarchical clustering was performed in RMA normalized samples with Cluster 3.0. Clusters characteristics were identified based on the majority of the histological subtypes present.Myc, MMTV-Myc x E2F2−/− and lung metastasis samples were analyzed by GeneSpring. A t-Test was used to determine statistical significance for fold change analysis with p-value cut-off of 0.05. Fold change genes were examined for their correlation with human time to distant metastasis [http://www.broadinstitute.org/gsea/index.jsp). Regulatory network analysis was performed through examination of TCGA database [E2F2 and PTPRD.To identify samples with human relevance, gene expression from previously mentioned human tumors were co-clustered with mouse tumor dataset. Fold change difference (>1.5) between MMTV-tastasis , 58 and tastasis . To detetastasis  and Swistastasis . GSEA [6tastasis  analysisdatabase , 64 usinMyc and E2F2 pathway activation. Cells were cultured in DMEM, 10% Fetal Bovine Serum (FBS) and 2.0 mM L-glutamine.MCF7 and MDA-MB-231 were chosen based on the probability of E2F2 and PTPRD were purchased from OriGENE . Cells were transfected using ExtremeGENE HP transfection reagent  according to manufacturer's protocol. Cells were selected using 2 μg/ml puroMycin 48 hours after transfections. Both populations and colonies were tested using western blot to determine the knockdown efficiency. Wound healing assay and transwell migration assay was performed as previously described [shRNA constructs targeting escribed . TranswePTPRD (human) were F-TCACCAAGCTGCGTGAAATG and R-CAGCCATGGGATCTACAACAAA and the primers for PTPRD (mouse) were F-GGCTAGCCATCCTCCAATACC and R-TCCTGGGATTCCTCATATTCC . qRT-PCR was performed on 20 ng of total RNA (MDA-MB-231) or 200 ng of total RNA (MCF7) using QuantiTECT Sybr green PCR kit (Qiagen).RNA samples were isolated using RNeasy Plus mini-kit  according to the manufacturer's protocol. The primers for E2F2  or rabbit anti-PTPRD .Primary antibodies for immunoblotting were rabbit anti-t-Test was performed on the quantification of tumor metastases, percent area reduction, and transwell migration assays. Differences in latency and distant metastasis free survival was examined by plotting Kaplan-Meier survival plot. To examine the percentage of mice with metastasis, Fisher's test with 2 × 2 contingency table was used.Statistical analysis was performed using GraphPad Prism 5 and GraphPad Quickcalcs. Non-parametric E2F2 loss contributes to metastasis in Myc induced tumors; 2. E2F2 may act through PTPRD to increase metastasis in the MMTV-Myc model of breast cancer.This study concludes that; 1.


Covalently closed circular RNA molecules (circRNAs) have recently emerged as a class of RNA isoforms with widespread and tissue specific expression across animals, oftentimes independent of the corresponding linear mRNAs. circRNAs are remarkably stable and sometimes highly expressed molecules. Here, we sequenced RNA in human peripheral whole blood to determine the potential of circRNAs as biomarkers in an easily accessible body fluid. We report the reproducible detection of thousands of circRNAs. Importantly, we observed that hundreds of circRNAs are much higher expressed than corresponding linear mRNAs. Thus, circRNA expression in human blood reveals and quantifies the activity of hundreds of coding genes not accessible by classical mRNA specific assays. Our findings suggest that circRNAs could be used as biomarker molecules in standard clinical blood samples. Regulatory RNAs such as microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) have been implicated in many biological processes and human diseases such as cancer  and SRY can act as antagonists of specific miRNAs by functioning as miRNA sponges ,31]. Mo. Mo31]. Since circularity renders RNA largely resistant to exonucleolytic activities, circRNAs are stable molecules as demonstrated by their long half lives in cells ,19],,[in silico circRNA detection pipeline [i.e. indicative for circRNAs. Anchors that aligned consecutively were used to determine linear splicing as an internal library quality control and to assess linear RNA isoform expression . We then compared the blood data to published ENCODE project datasets from cerebellum, representative of neuronal tissues that in general have high circRNA expression [From the RNA of two human donors we identified 4550 and 4105 unique circRNA candidates, respectively, by at least two independent reads spanning a head-to-tail splice junction . In bothpression  and to aFurther, as observed in other human samples, we find that most circRNAs are derived from protein coding exonic regions or 5’ UTR sequences . TogetheThe predicted spliced length of blood circRNAs of 200–800 nt (median = 343 nt) is similar to that in liver or cerebellum (median = 394/448 nt) and previous observations in HEK293 cell cultures and other human samples .To assess the reproducibility of the sequencing results we designed divergent circRNA specific primer and measured relative abundances of the top eight expressed circRNAs compared to linear control genes in qPCR . circRNAWhen inspecting the read coverage in blood sequencing data, we noticed that oftentimes the expression of circularized exons was outstandingly high compared to the coverage of neighboring exons expressed in linear RNA isoforms of the same gene. For example, we observed that the two exons of circRNA candidate 5, which is product of the PCNT locus were densely covered with sequencing reads in the blood samples, while the upstream and downstream exons were barely detected . This paWe next analyzed the relative circular to linear RNA isoform abundance on a transcriptome wide scale. To this end, we compared read counts that span head-to-tail junctions and are therefore indicative of circRNAs, to the median number of read counts on linear splice site junctions on the same gene, the latter serving as a proxy for linear RNA expression . We obseOur results show that circRNAs are reproducibly and easily detected in clinical standard blood samples and therefore suggest that they may have the potential to serve as a new class of biomarker for human disease.n = 5, Recent publications show that circRNAs can be detected in plasma and saliva samples ,33. HoweFurther, we demonstrated that many circRNAs have a high expression compared to linear RNA isoforms from the same locus, a feature that distinguishes blood circRNAs from other primary tissues such as cerebellum or liver. Considering that this was observed for hundreds of blood circRNA candidates  and thatNevertheless, especially given the urgent need for non-invasive biomarker detection for many disease states, we think these findings encourage future in-depth follow up analysis of circRNAs. It will be interesting to search for circRNA biomarkers not only in blood but also in other clinical samples such as cerebrospinal fluid. Although in principle blood circRNA expression might be specifically altered in a plethora of human diseases, investigations of neurological conditions would be of particular interest, since circRNA expression is exceptionally high in neuronal tissues  and the 2EDTA coated Vacutainer  and stored on ice until used for RNA preparation. For downstream RNA analysis by sequencing or qPCR assays presented here, 100 μL blood  is sufficient.Blood sampling, processing and analysis performed in this study was approved by the Charité ethics committee, registration number EA4/078/14 and all participants gave written informed consent. 5 mL blood were drawn from subjects by venipuncture and collected in K2 and 37°C.Human HEK293 Flp-In T-REx 293  were cultured in Dulbecco’s modified Eagle medium GlutaMax (Gibco) with 4.5 g/l glucose, supplemented with 10% FCS, at 5% COC. elegans total RNA was spiked-in followed by phenol-chloroform extraction of the RNA mixture. For controls the RNA was mock treated without the enzyme.Total RNA was isolated from fresh whole blood samples. Blood was diluted 1:3 in PBS and 250 μL of the dilution were used for RNA preparation using 750 μL Trizol LS reagent . Samples were homogenized by gentle vortexing and 200 μL chloroform was added. After centrifugation at 4°C, 15 min at full speed in a table top centrifuge, the aqueous phase was collected to a new tube . RNA was precipitated by adding an equal volume of cold isopropanol and incubation for ≥ 1 hour at -80°C. RNA pellets were recovered by spinning at 4°C, 30 min at full speed in a table top centrifuge. RNA pellets were washed with 1 mL 80% EtOH and subsequently air dried at room temperature for 5 min. The RNA was resuspended in 20 μL RNase-free water and treated with DNase I  for 15 min at 37°C with subsequent heat inactivation for 10 min at 65°C. HEK293 total RNA was prepared using 1 mL Trizol on cell pellets. For sequencing experiments the RNA preparations were additionally subjected to two rounds of ribosomal RNA depletion using a RiboMinus Kit (Life Technologies K1550-02 and A15020). Total RNA integrity and rRNA depletion were monitored using a Bioanalyzer 2001 . For qPCR analysis the samples were treated with RNase R  for 15 min at 37°C at a concentration of 3 U/μg RNA. After treatment 5% cDNA libraries were generated according to the Illumina TruSeq protocol . Sample RNA was fragmented, adaptor ligated, amplified and sequenced on an Illumina HiSeq2000 in 1x 100 cycle runs. Sequencing data have been deposited at GEO under accession number GSE73570.C. elegans RNA spike-in RNA. Error bars denote standard deviations (n = 3).Total RNA was reverse transcribed using Maxima reverse transcriptase (Thermo Scientific) according to the manufacturer's protocol. qPCR reactions were performed using Maxima SYBR Green/Rox (Thermo Scientific) on a StepOne Plus System (Applied Biosystems). Primer sequences are available in the PCR products were size separated by agarose gel electrophoresis, amplicons were extracted from gels and Sanger sequenced by standard methods .). The resulting splicing events were filtered using the following criteria 1) GT/AG signal flanking the splice sites 2) unambiguous breakpoint detection 3) maximum of two mismatches when extending the anchor alignments 4) breakpoint no more than two nucleotides inside the alignment of the anchors 5) at least two independent reads supporting the head-to-tail splice junction 6) a minimum difference of 35 in the bowtie2 alignment score between the first and the second best alignment of each anchor 7) no more than 100 kilobases distance between the two splice sites.The detection of circular RNA was based on a previously published method  with theGenomic coordinates of circRNA candidates were intersected with published gene models . circRNAs were annotated and exon-intron structure predicted as previously described . Known iwww.circbase.org/ [In this study we used rRNA depleted RNA-seq data from whole blood samples (own data), fetal cerebellum (ENCODE accession: ENCSR000AEW) fetal liver (ENCODE accession: ENCSR000AFB) and HEK293  S4 Tabl. Expressase.org/ .left, right).The number of reads that span a particular head-to-tail junction were used as a measure for circRNA expression. To allow comparison of expression between samples, raw read counts were normalized to sequencing depth by dividing by the number of reads that map to protein coding gene regions and multiply by 1,000,000 left,  Figs. ToCircular-to-linear ratios were calculated for each circRNA by dividing raw head-to-tail read counts by the median number of reads that span linear spliced junctions of the respective host gene. For both measures one pseudo count was added to avoid division by zero. circRNAs from host genes without annotated splice junctions according to the ENSEMBL gene annotation, were not considered in this analysis.For analysis in S1 FigThe overlap of 2,442 circRNAs found with at least 2 read counts in both samples is considered as reproducibly detected circRNA set.(PDF)Click here for additional data file.S2 FigA library of blood Sample 1 was sequenced twice see .(PDF)Click here for additional data file.S3 FigSignificantly enriched GO terms (p<0.05) for circRNAs found in both samples  and for the same number of top expressed linear RNAs.(PDF)Click here for additional data file.S4 FigPredicted spliced circRNA length distributions for circRNA candidates detected in liver, cerebellum and blood.(PDF)Click here for additional data file.S5 Fig(a) Top circRNA candidate expression was measured in qPCR using divergent primer on mock or RNase R treated total RNA preparation. 7/8 were successfully amplified while candidate 8 did not yield specific PCR products and is therefore excluded from further analysis. Linear RNAs and previously described circRNAs are shown as controls. (b) PCR amplicons for divergent and convergent primer sets  of the tested candidates, end point analysis after 40 cycles. (c) Standard curves for tested candidates, div—divergent primer for circular isoforms, con—convergent primer for linear RNA isoforms. (d) PCR amplicons were subjected to Sanger sequencing and checked for the presence of a head-to-tail junction, representative example result is shown.(PDF)Click here for additional data file.S6 Fig(a) Comparison of circular RNA candidates detected in blood (Sample 1) and cerebellum shown for the whole expression range. (b) fraction of circRNA candidates that overlap between the two samples binned by blood expression level.  Analysis as before but for liver circRNA candidates.(PDF)Click here for additional data file.S7 FigNumber of detected transcripts: blood = 29,908; cerebellum = 38,192; liver = 27,880; TPM = transcripts per million.(PDF)Click here for additional data file.S8 FigRaw Ct values (Cycle threshold) and median linear splice junction spanning read counts are given for the respective RNA isoform.(PDF)Click here for additional data file.S9 FigHistogram of number of exons per circRNA. Reproducibly detected set  without intergenic circRNAs (n = 27); median exon number: 2, mean exon number: 2.8.(PDF)Click here for additional data file.S1 TableGenomic location, ENSEMBL gene identifier, gene symbols and gene biotype are given together with raw read counts for each circRNA candidate in each sample. In a second sheet head-to-tail junction reads and median linear splice site junction spanning reads are given for blood cerebellum and liver samples.(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(XLS)Click here for additional data file.S5 Table(XLS)Click here for additional data file.


The APOBEC3 (A3) cytidine deaminases are antiretroviral proteins, whose targets include human immunodeficiency virus type-1 (HIV-1). Their incorporation into viral particles is critical for antiviral activity and is driven by interactions with the RNA molecules that are packaged into virions. However, it is unclear whether A3 proteins preferentially target RNA molecules that are destined to be packaged and if so, how. Using cross-linking immunoprecipitation sequencing (CLIP-seq), we determined the RNA binding preferences of the A3F, A3G and A3H proteins. We found that A3 proteins bind preferentially to RNA segments with particular properties, both in cells and in virions. Specifically, A3 proteins target RNA sequences that are G-rich and/or A-rich and are not scanned by ribosomes during translation. Comparative analyses of HIV-1 Gag, nucleocapsid (NC) and A3 RNA binding to HIV-1 RNA in cells and virions revealed the striking finding that A3 proteins partially mimic the RNA binding specificity of the HIV-1 NC protein. These findings suggest a model for A3 incorporation into HIV-1 virions in which an NC-like RNA binding specificity is determined by nucleotide composition rather than sequence. This model reconciles the promiscuity of A3 RNA binding that has been observed in previous studies with a presumed advantage that would accompany selective binding to RNAs that are destined to be packaged into virions. Cellular intrinsic immunity constitutes a key defense against infection by viruses. The APOBEC3 (A3) family of cytidine deaminases are intrinsic immune proteins that can hypermutate and destabilize retroviral genomes. For A3 proteins to exert their antiviral activity, they must be incorporated into nascent virions. Although A3 RNA binding activity has been shown to be critical for virion incorporation, the mechanism by which packaged RNA molecules are targeted by A3 proteins has been unclear. Therefore, we employed a cross-linking and deep sequencing strategy to analyze the targets of A3 proteins in HIV-1 infected cells and purified virions. We found that A3 proteins preferentially bind to particular types of RNA sequence in HIV-1 and cellular RNAs. In particular, we found that A3 proteins bind to G-rich and A-rich RNA sequences, a property that is reminiscent of that exhibited by the HIV-1 Gag protein. Further analyses revealed that A3 proteins preferentially bind the same sequences as the nucleocapsid domain of the HIV-1 Gag protein in viral RNA and cellular 7SL RNA. These results suggest a model of A3 incorporation into virions that that is based on mimicry of the RNA binding specificity of the HIV-1 nucleocapid protein. APOBEC3 (A3) proteins are a family of germline-encoded proteins that inhibit the replication of a broad range of viruses and retroelements . A3 In primates, there are seven members of the A3 protein family that are categorized by their possession of either a single zinc (Z)-containing deaminase domain , or two Z domains  (reviewed in ). The anPackaging of A3 proteins into HIV-1 virions depends on the nucleocapsid (NC) region of the viral Gag polyprotein and its associated RNA –25. In tStudies that have aimed to determine the identity of the RNA that is targeted by A3 proteins for incorporation into virions have yielded a variety of conclusions. One study indicated that viral RNA is targeted by A3 proteins , while aNotably, if it is true that A3-RNA interaction is completely non-selective, then cellular RNAs would compete with viral RNAs for A3 binding. Because cellular RNAs are present in large excess over HIV-1 RNAs in infected cells, their presence should inhibit A3 incorporation into HIV-1 virions to a significant degree. Indeed, such a scenario would require that A3 proteins be associated with a large fraction of the RNA molecules in the cell in order to be incorporated into a correspondingly large fraction of virions. While some RNA binding proteins associate with RNA in this completely non-specific manner, many other RNA binding proteins bind to their target RNA molecules through the recognition of specific RNA sequences and/or structures (reviewed in ), using For these reasons, we undertook a detailed study of the interactions between A3 proteins and RNA. Recent advances in ribonomic technologies, such as cross-linking immunoprecipitation coupled to next generation sequencing (CLIP-seq (reviewed in ) have enNL4-3 ΔVif and cultured in the presence of the ribonucleoside analog 4-thiouridine (4SU). Cells and purified virions were UV-irradiated, lysed, and digested with ribonuclease A. Thereafter, A3-RNA complexes were immunoprecipitated using an anti-HA antibody, 5'-end labeled with γ-32P-ATP and detected by autoradiography and Western blotting. As expected, A3F, A3G and A3H from cells and purified virions were cross-linked to RNA  resulted in a similar distribution of binding sites on HIV-1 RNA indicating that cellular context does not greatly affect the HIV-1 RNA binding specificity of A3F and A3G . FurtherTo investigate potential similarities and differences in A3:RNA binding that might exist among the three A3 proteins in infected cells versus mature virions, we performed correlation analyses of RNA binding sites . DespiteWhile the A3 proteins bound to many sites on the viral genome, the aforementioned results suggested a degree of specificity in A3-RNA binding. To investigate RNA target specificity of A3 proteins we undertook a detailed inspection of RNA types and sequences that were most frequently bound by A3F, A3G and A3H in uninfected cells, infected cells and in purified virions. We aligned the raw reads derived from A3 CLIP-seq experiments done using infected cells to both the human and viral genomes and found that 2.5%–4.1% of the total reads were HIV-1 derived, whereas 97.5–95.9% were from cellular RNA . In compTo investigate the basis for this apparent specificity, A3 CLIP reads were aligned to the human genome and the HIV-1 genome and analyzed using PARalyzer . This apAs mRNAs constituted the preferred target of A3 proteins, compared to other types of cellular RNAs, we analyzed the binding pattern of A3 proteins to the 10 most frequently A3 bound mRNAs  Figs. CoThese results clearly showed that A3 proteins have diverse RNA targets, but that binding is not indiscriminate. In cells, mRNAs represent the bulk of A3-bound RNA, with a clear preference for 3' untranslated sequences. Moreover, A3 proteins preferentially target viral RNA sequences in infected cells. These findings indicate that A3 proteins are incorporated into HIV-1 virions predominantly via interactions with viral RNA that are preferred over interactions with cellular mRNAs or other RNAs. However, it was unclear how viral RNA might be selected by A3 proteins from a diverse pool of RNAs in the cell. Note that some HIV-1 RNA species, including the full length packaged viral genome, have long 3'UTRs which might contribute to their apparent selection for binding by the A3 proteins . An addiNL4-3 genome, and ~23%, ~25%, ~22% and ~30%, respectively, for human RNA as determined by our RNA-seq analysis)  in a complex RNA source (cellular mRNAs) that were most frequently bound by A3F, A3G and A3H in cells. This analysis selected clusters representing RNA regions of 200–900 nucleotides in length. Thus, they likely represent concentrations of several A3 protein binding sites rather than individual A3 binding sites. Next, we determined the nucleotide composition of these clusters, or collections of binding sites. This analysis revealed that the clusters had a striking propensity to be G-rich and/or A- rich  compared to mean G-, A-, C- and U- content of ~24%, ~36%, ~18% and ~22%, of the HIV-1nalysis) . We alsonalysis) .Additionally, we used cERMIT  to examiWe noticed that the apparent preference of A3 proteins for G-rich and A-rich RNA sequences was reminiscent of the two modes of RNA binding exhibited by the HIV-1 Gag and NC proteins, before, during and after HIV-1 particle assembly . TherefoWe next compared the RNA binding profile of A3F, A3G and A3H to the HIV-1 genome with that of NC and Gag in mature and immature virions, respectively, using NC and Gag CLIP-seq data . RemarkaIn immature virions, the patterns of A3G and A3H protein binding to the viral genome did not correlate with the pattern of Gag binding , in contvice versa. Strikingly, however, as Gag assembled into immature and then mature virions there was a progressive harmonization of binding patterns, and there was clear correlation between A3G and Gag or NC RNA binding in both immature and particularly mature virions. This finding reinforces the notion that A3 and HIV-1 NC proteins have apparently similar RNA binding specificities, using 2 different target RNAs, and suggests that competition by other proteins (including perhaps SRP proteins that bind 7SL proteins in cells) may affect RNA target availability and the apparent propensity of A3 proteins to bind to particular RNA elements in cells.The aforementioned observations indicated that some degree of change in the RNA binding profile of A3 protein to viral RNA occurs during virion genesis, likely as a consequence of RNA representation, and competition with immature Gag during viron genesis. Ultimately, however, the A3 proteins and NC demonstrate an apparently similar viral RNA binding specificity in mature virions. To investigate whether these changes occurred with a different RNA target, we examined 7SL1, a cellular RNA that is ordinarily part of the SRP ribonucleoprotein complex but is highly enriched in the virions, through binding the HIV-1 Gag and NC proteins. 7SL is also bound by A3G and A3H proteins in infected cells and in virions –35, 48. The RNA binding activity of A3 proteins is essential for incorporation into HIV-1 virions and restriction of virus replication. Our data confirm previous studies in that A3 proteins are promiscuous in the RNA molecules that they bind. Importantly however, our findings also argue that A3 proteins exhibit significant RNA binding selectivity that could facilitate their incorporation into nascent HIV-1 virions and thus contribute to their antiviral activity.The majority of molecules that are bound by A3 proteins in cells were diverse messenger RNAs, while other cellular RNAs including 7SL RNA, miRNA, tRNA, rRNA were bound less frequently. The promiscuity of A3 protein binding is consistent with previous observations that A3G forms high molecular mass ribonucleoprotein complexes in cells that contain numerous cellular RNAs and RNA binding proteins  includinAlthough RNA binding by A3 proteins is promiscuous, our data show that A3 proteins preferentially bind HIV-1 viral RNA in infected cells. Moreover, A3F, A3G and A3H proteins bound, with similar, reproducible, non-uniform distributions along the length of the HIV-1 genome. The RNA sequences that are preferentially bound by A3 proteins, and regions of the viral genome that are preferentially targeted could be governed by several factors including: (i) RNA abundance (ii) RNA binding specificity inherent to the A3 proteins (iii) secondary RNA structures present in RNAs, that might differ in cells and in virions and (iv) competition by other RNA binding proteins (most notably Gag). Like all lentiviruses, HIV-1 genomic RNA has a characteristic A-rich nucleotide composition, and also exhibits secondary structure, with regions that have greater or lesser propensity to exhibit single-stranded and double-stranded character . HIV-1 Rin vitro by single-stranded RNA molecules that contain G residues, but not single-stranded RNA molecules that lacked G-bases  ). The beads were then incubated with 50 μg/ml of a mouse monoclonal anti-HA for 30 min, room temperature prior to being washed twice with 1 ml of citrate-phosphate buffer and resuspended in 1 bead volume. Approximately 20 μl/ml of beads was added to lysates, and then binding was performed for 2 h at 4°C in 1.5 ml siliconized low retention tubes (Fisher). After binding the beads were washed thoroughly twice with 1 ml of each of the following buffers: IP wash buffer , LiCl buffer , NaCl buffer , KCl buffer , dephosphorylation buffer . Thereafter, dephosphorylation was performed in 1 bead volume of dephosphorylation buffer  containing 0.5 U/μl calf-intestinal phosphatase (NEB) for 15 min at 37°C in a Thermomixer. The beads were then washed twice with 1 ml of each of phosphatase wash buffer  and polynucleotide kinase (PNK) Buffer . The beads were subsequently resuspended in 1 bead volume of PNK buffer containing 0.5 μCi/μl γ-32P[ATP] ATP and 1 U/μl T4 PNK (NEB) and incubated for 30 min at 37°C in a Thermomixer. Thereafter, cold ATP was added to a final concentration of 20 μM and the incubation was continued for 10 min at 37°C. The beads were then washed once with 1 ml of PNK, LiCl, KCl and NaCl buffers. Beads were then resuspended in 50 μl of NuPAGE SDS-PAGE loading buffer and incubated at 72°C for 10 min (with constant mixing) to elute protein-RNA adducts.Protein G-conjugated magnetic Dynabeads (Invitrogen) were washed twice with and resuspended in two bead volumes of citrate-phosphate buffer  was used to process the raw reads prior to mapping. Reads that were fewer than 15 nt, did not contain the 3' adapter sequence or contained ambiguous nucleotides were excluded from further analyses. Reads were subsequently aligned using Bowtie [NL4-3 genome, or to the viral genome alone, allowing for a maximum of 2 mismatches Reads derived from the R region of the HIV-1NL4-3 LTR have been displayed at the 5' end of the genome in our analyses and interpreted with caution. SAMtools [The analysis pipeline that was used in this study has been previously described . Brieflyg Bowtie  to the hSAMtools  was usedSAMtools  using paSAMtools  and the SAMtools . PARalyzSAMtools . PreviouSAMtools  were useSAMtools . BrieflyS1 FigNL4-3 genome obtained from two independent A3F, A3G and A3H CLIP experiments in infected cells and mature virions. Read densities for the 5' 2000 nucleotides of the viral genome are shown for clarity.Comparison of the read densities on the HIV-1(TIF)Click here for additional data file.S2 FigNL4-3 genome in A3F- and A3G-CLIP experiments done using infected 293T cells or MT4 cells (top). Data from MT4 cell CLIP experiments using a single cell clone for A3F and two single cell clones for A3G are shown. An expanded view of the frequency distributions of read densities for the 5' 2000 nucleotides of the viral genome are also shown for clarity (bottom). Correlation analysis of A3 binding frequencies on the viral RNA genome HEK 293T and MT4 A3F- and A3G-CLIP experiments (right).Frequency distributions of read densities on the HIV-1(TIF)Click here for additional data file.S3 FigNL4-3 ΔVif infected HEK 293T cells stably expressing 3×HA-tagged A3G or A3H proteins that had been supplemented with 6SG and UV-irradiated. Complexes were visualized by autoradiography (top) and Western blot analysis using an anti-HA antibody (bottom). (B) Frequency distributions of read densities on the HIV-1NL4-3 genome in 4SU- and 6SG-based A3G- and A3H-CLIP experiments (top). An expanded view of the frequency distributions of read densities for the 5' 2000 nucleotides of the viral genome are also shown for clarity (bottom). Correlation analysis of A3 binding frequencies on the viral RNA genome in 4SU- and 6SG-based A3G- and A3H-CLIP experiments (right). (C) Classification of individual reads that map to the human and HIV-1NL4-3 genomes from A3G and A3H CLIP experiments with uninfected and infected cells in 6SG-based CLIP experiments.(A) A3-RNA cross-linked complexes were immunoprecipitated from mock infected or HIV-1(TIF)Click here for additional data file.S4 FigNL4-3 genome for CLIP experiments in which the same A3 protein was immunoprecipitated from infected cells or mature virions. Read densities for the 5' 2000 nucleotides of the viral genome are shown for clarity. (B) Comparisons of read density frequency distributions on the HIV-1NL4-3 genome for CLIP experiments in which different A3 proteins were immunoprecipitated from infected cells or mature virions, as indicated. Read densities for the 5' 2000 nucleotides of the viral genome are shown for clarity.(A) Comparison of read density frequency distributions on the HIV-1(TIF)Click here for additional data file.S5 FigDepiction of the proportions of reads that were derived from 5'UTR, 3'UTR and coding sequences (CDS), for reads that mapped to cellular mRNAs in A3-CLIP experiments. The total number of reads analyzed is indicated below each bar.(TIF)Click here for additional data file.S6 FigRead density frequency distribution in CLIP and RNA-seq experiments for the 10 most frequently A3F-bound cellular mRNAs The 5'untranslated (UTR), coding sequence (CDS) and 3'UTR regions are indicated for the CLIP reads.(TIF)Click here for additional data file.S7 FigRead density frequency distribution in CLIP and RNA-seq experiments for the 10 most frequently A3G-bound cellular mRNAs The 5'untranslated (UTR), coding sequence (CDS) and 3'UTR regions are indicated for the CLIP reads.(TIF)Click here for additional data file.S8 FigRead density frequency distribution in CLIP and RNA-seq experiments for the 10 most frequently A3H-bound cellular mRNAs The 5'untranslated (UTR), coding sequence (CDS) and 3'UTR regions are indicated for the CLIP reads.(TIF)Click here for additional data file.S9 FigNL4-3 genome for CLIP experiments in which A3 proteins or Gag were immunoprecipitated from infected cells. Read densities for nucleotides 2000–6000 of the viral genome are shown. (B) Comparisons of read density frequency distributions on the HIV-1NL4-3 genome for CLIP experiments in which A3 and NC were immunoprecipitated from purified mature virions. Read densities for nucleotides 2000–6000 of the viral genome are shown.(A) Comparisons of read density frequency distribution on the HIV-1(TIF)Click here for additional data file.S10 FigNL4-3 genome for CLIP experiments in which A3 was immunoprecipitated from purified mature or immature virions. Read densities for the 5' 2000 nucleotides of the viral genome are shown for clarity. (B) Comparisons of read density frequency distributions on the HIV-1NL4-3 genome for CLIP experiments in which A3 or Gag was immunoprecipitated from immature virions. Read densities for the 5' 2000 nucleotides of the viral genome are shown.(A) Comparisons of read density frequency distributions on the HIV-1(TIF)Click here for additional data file.