In two studies we provide a novel investigation into the effects of monetary switching costs on choice-inertia . Study 1 employed a static decisions-from-feedback task and found that the introduction of, as well as larger, monetary switching costs led to increases in choice-inertia. While experience and decreases in the similarity of options average payouts  increased choice-inertia for the option with a higher EV (the EV maximizing option), switching costs increased choice-inertia for the inferior option (the lower EV option): The proportion of total participants showing choice-inertia for the EV maximizing option also increased with switching costs. Study 2 employed a dynamic decisions-from-feedback task where halfway through the task the EV maximizing option became the inferior option. The effect of switching costs increasing choice-inertia for both the EV maximizing and the inferior option was replicated with little impact of the change in options values being detected. In sum, decision makers appear to be sensitive to switching costs, and this sensitivity can bias them towards inferior or superior options, revealing the good and the bad of choice-inertia. Many of our daily decisions result in repeating our previous choices: Most days we use the toothbrush and toothpaste we used the day before, we drive the same roads to and from work, and buy groceries at the same store. Repeating the same choice over and over, henceforth referred to as choice-inertia, can be the result of an efficient cost-benefit analysis: Once a good option is identified, further exploration of alternative options can lead to a waste of resources . Nevertheless, if the repeatedly selected option is, or becomes over time, inferior to other options, choice-inertia could result in poor outcomes. In real life decisions, evaluating whether choice-inertia is efficient or not is difficult as it is unclear how to quantify the value of repeating one’s last choice and to estimate forgone outcomes . A cleaner examination of the good and bad of choice-inertia can be obtained in controlled lab experiments where the values associated with each option are better specified.We start with the observation that many behavioral biases can be described as reflecting situations in which choice-inertia has negative consequences on performance: The status quo effect  where inAnother set of examples includes the tendency to repeatedly choose suboptimal options while neglecting to explore for the existence of better options, as observed in local maxima stabilization  5] insu insu5] iDespite differing contexts, all the phenomena documented above describe situations in which decision makers stick to a chosen alternative (i.e. exhibit choice-inertia) even when it reduces performance (earnings). Resting on the similarity among these diverse effects we focus on the general tendency to exhibit choice-inertia, with the supposition that all these effects can be placed under a unified umbrella of inertia biases. It is important to note that choice-inertia by itself is not a negative bias  16]17][17]16][1[17]16]1, 19]). ). 19]). One factor that has been suggested to account for many inertia biases is switching costs. For example, transition costs were referred to as a rational cause for status quo inertia ; physicaIn contrast, larger switching costs might have a positive effect on expected value (EV) maximization: Selecting options paying higher amounts on average. It might reduce stochastic or random choice, leading to more (desired) consistency. For example, Plonsky et al.  showed eIn the current paper, we aim to shed light on the influence of switching costs on choice-inertia. Although switching costs are commonly mentioned in the context of inertia biases, to the best of our knowledge, switching costs have yet to be directly manipulated. In addition, the potential positive effects of choice-inertia  have generally been overlooked. In the current studies switching costs are implemented as an explicit monetary loss follow each switch (change in the option selected on consecutive choices). Following Dinner et al.’s suggestion  we also Mage = 39.22; 47% female) participants were recruited using Amazon Mechanical Turk (MTurk) and received $0.50 plus monetary incentives (M = $0.76) based on their decisions (described below). The MTurk platform for on-line participant recruitment mirrors lab-based recruitment in many ways. Crump, McDonnell, & Gureckis (2013) compared lab based and MTurk experiments and found that MTurk experiments led to results similar to those observed in the lab, except learning was slower (e.g. in a category learning task) which they argued is possibly due, “to the more diverse participant sample than is typical in laboratory studies”. An attention check (clicking on an invisible box instead of “continue”) was employed before the study began and participants (N ≈ 147) who failed the check were not allowed to take part. The study was approved by the Technion ethics committee for behavioral studies.One-hundred and thirty-one , 10 (N = 43), or 20 points (N = 45). We informed participants about the switching cost condition they were in to reduce order-effects. For example, if participants had to discover the switching cost condition they were in, the first problem played, particularly in the first few trials, might reflect behaviors/strategies that differed from all others where the switching cost would be known from previous experience. After their first choice in a pair, if a participant in the 10 switching cost condition picked a different option than they had on their previous choice  they were informed that 10 points had been subtracted from their earnings. Switching cost conditions did not change for a participant during the study. That is, while EV gaps were manipulated within participant, switching costs were manipulated between participants. Manipulation of switching costs between participants was done to reduce carry over effects, which could occur if switching costs were manipulated within participant. After participants made 50 choices in all three pairs they were informed of their total earnings and thanked for their time.After providing informed consent and answering standard demographic questions  participants were told that they would make 50 choices in several different pairs of options (labeled Option A and Option B). Participants were told that they would receive feedback about the outcome that occurred for the option they picked in points (40 points = $0.01). Three pairs of options were included and were encountered in random order by all participants. Each option paid out a certain outcome and a random amount drawn from a Gaussian  distribution with a mean of 0 points and a standard deviation of 3 points which was added/subtracted from that certain outcome. Therefore, the expected value (EV) of each option was equal to its certain amount. In one pair both options had an EV of 19 points, thus choosing either option returned the same net earnings on average. In the remaining pairs, one option provided a higher average payout than the other: In one pair one option had an EV of 16 points and the other an EV of 22 points (a 6 point difference/gap on average), while in the final pair one option had an EV of 13 points and the other option had an EV of 25 points (a 12 point difference/gap on average). These made up the three EV gap conditions. Participants made 50 consecutive choices in each pair and were informed that they were moving to a new pair of options following their 50To empirically test these observations we performed a logistic regression predicting choice-inertia by choice number, EV gap, switching cost, as well as their interactions: All variables in Study 1 and 2 are entered linearly from smallest to largest and mean centered and repeated measurement is accounted for by clustering on the level of participant . Full miAs with choice-inertia we performed a logistic regression predicting EV maximization by choice number, EV gap, cost condition, as well as their interactions. The results of this analysis are reported in While the previous analyses provide some insight into choice-inertia and EV maximization, they provide only indirect insight into whether choice-inertia played a positive or negative role in participants EV maximization. To more directly assess the direction of choice-inertia  we examined the rate of EV maximization in choices 38–47 . We examine this range of choices instead of the last 10 choices because in the chosen range for all cost conditions switching from the smaller outcome option to the larger outcome option would on average lead to a net benefit . We performed a logistic regression predicting whether participants who showed 100% choice consistency (213 cases out of 262) never selected the EV maximizing option (coded 0: 34 observations) or always did (coded 1: 179 observations) by EV gap, cost condition, and their interaction. In line with our predictions we find that experience , option similarity , and switching costs all influenced the level of choice-inertia observed. With more experience the rate of choice-inertia increased, presumably because decision makers learned which option they preferred. Increasing the EV gap between options led to increased choice-inertia as well, likely because it made it easier to identify the superior option: An interpretation supported by the finding that increasing the EV gap also increased the likelihood of EV maximization. Nevertheless, the cost of making a switch appeared to have the largest immediate impact with the rate of choice-inertia being greater when switching costs were (present) higher. In terms of the consequences of choice-inertia, only larger switching costs were predictive of decreased EV maximization. At the same time, the proportion of participants consistently showing EV maximization increased with switching cost. Thus, it appears that increasing the costs of exploration increases the proportion of individuals showing 100% choice-inertia, which in many cases  led to continued exploitation of inferior options , but this did not reduce overall rates of choice-inertia which resulted in EV maximization.In Study 2 we examine whether the effects found in Study 1 are replicated in a dynamic environment. As noted, several important behavioral problems like the sunk cost effect and learned helplessness can be described as sticking to an alternative which is good at first but becomes inferior at some point in time. Poor performance following choice-inertia in this context can result from obliviousness to the change  or to the potential benefits of switching . In the current study environmental change was implemented by swapping the two options so that the superior option become inferior and vice versa. Will participants adapt to the change? We expect this to be dependent on the degree to which the change can be detected and the expected benefits from switching after the occurrence of the change. For example, if two options are relatively similar in their payouts , swapping the two options becomes less salient than in the case of two distant options (options with large differences in EV), and the benefit associated with switching back to the superior alternative becomes smaller. In addition, when the gap is large, replacement can cause a stronger surprise  which by itself can lead to inertia offset (known as Surprise-Triggers-Change ). FurtheMage = 39.43; 56% female, 43% male, and 1% other) new participants were recruited using Amazon Mechanical Turk. Participants received $0.50 plus monetary incentives (M = $0.51) based on their decisions (described below). The same attention check used in Study 1 was employed and as in Study 1 participants (N ≈ 210) who failed it were not allowed to take part.Two-hundred and fifty-three . Thus, participants were randomly assigned to an EV gap and a switching cost condition, switching cost of 0 (N = 37), 10 (N = 40), 20 (N = 43), then made 100 choices with the options being reassigned to labels on choice 51 .Study 2 was identical to Study 1 except for three changes: Only the two pairs with an EV maximizing option were included; participants only made choices in one randomly assigned pair ; instead of 50 choices 100 choices were made and after the 50As in Study 1 we performed a logistic regression predicting choice-inertia by choice number, EV gap, cost condition, a dummy variable indicating whether the choice was before or after the change in option label assignment, as well as their interactions. The results of this analysis are reported in As with choice-inertia we performed a logistic regression predicting EV maximization by choice number, EV gap, cost condition, whether the decision was made before or after the change in option labels, as well as their interactions. The results are reported in As in Study 1 we examined the effect of choice-inertia on EV maximization in choices 38–47 and 88–97. As before, these ranges were employed because in all cost conditions switching from the smaller EV option to the EV maximizing option would on average lead to a net benefit. As in Study 1 we performed a logistic regression predicting whether participants who showed 100% choice consistency (384 out of 506 observations) never (coded 0: 94 observations) or always EV maximized (coded 1: 290 observations) by EV gap, cost condition, change in option labels , and their interaction. The results of this analysis are presented in In Study 2 we replicate the general pattern of findings observed in Study 1: Increased EV maximization with experience which was amplified when there was a larger difference in the options EVs; increased choice-inertia (primarily for the inferior option) as the switching cost increased. In terms of the effect of the options changing labels we find that choice-inertia decreased following the change, more so when making a switch was associated with a higher cost, as did EV maximization. The change in labels did not appear to have any robust effect on the consequences of inertia, though we did find more choice-inertia of the EV maximizing option following the change when there was a greater difference in the options EVs. Thus, as in Study 1, the most robust predictor of choice-inertia appears to be switching costs.In the current studies we sought to gain insight into what features of the environment lead to choice-inertia and its consequences. Specifically, we provided a direct test of whether switching costs increase choice-inertia, and whether that increase in choice-inertia is more likely to be directed towards or away from EV maximizing options. In both studies increasing switching costs increased choice-inertia, decreased overall EV maximization, and appeared to dampen the effects of experience and EV gap on choice-inertia and EV maximization. Thus, in line with our predictions we provide direct evidence that switching costs increase choice-inertia, and can bias the direction of that inertia.Why did switching costs increase choice-inertia for inferior options? One possibility is that the increased switching costs led to less thorough initial exploration. For example, instead of searching each option three times a large switching cost might have led participants to search each option less, skewing their perceptions of the options values. Similarly, increasing the cost of making a switch may have led participants to employ search strategies they did not favor. For example, although summary search which involve less switching was found to increase maximization  this wasHowever, it is important to note that although EV maximization is a popular measure, it does not necessarily imply optimality, because choice-optimality can be argued to include a plethora of other variables such as metabolic costs, mental effort, and the value of time. Also, as mentioned in  and 29]29], otheAs outlined in the introduction understanding choice-inertia is important because it is theorized to underly/reflect many biases affecting decision making. Sunk cost effects  for examIn short, the current studies provided a novel direct investigation into the effects of switching costs on choice-inertia and indicated that larger switching costs increased choice-inertia mainly associated with sticking to inferior options. Providing initial empirical support for the proposition that switching costs influence choice-inertia and clarify their influence by showing that for a growing proportion of decision makers larger switching costs led to choice-inertia with adverse consequences on earnings. As such, the current studies provide evidence that garnering a better understanding of choice-inertia should be of central importance to decision scientists given its potential to hamper effective decision making across a variety of contexts.S1 Appendix(DOCX)Click here for additional data file.


AATF is a central regulator of the cellular outcome upon p53 activation, a finding that has primarily been attributed to its function as a transcription factor. Recent data showed that AATF is essential for ribosome biogenesis and plays a role in rRNA maturation. AATF has been implicated to fulfil this role through direct interaction with rRNA and was identified in several RNA-interactome capture experiments. Here, we provide a first comprehensive analysis of the RNA bound by AATF using CLIP-sequencing. Interestingly, this approach shows predominant binding of the 45S pre-ribosomal RNA precursor molecules. Furthermore, AATF binds to mRNAs encoding for ribosome biogenesis factors as well as snoRNAs. These findings are complemented by an in-depth analysis of the protein interactome of AATF containing a large set of proteins known to play a role in rRNA maturation with an emphasis on the protein-RNA-complexes known to be required for the generation of the small ribosomal subunit (SSU). In line with this finding, the binding sites of AATF within the 45S rRNA precursor localize in close proximity to the SSU cleavage sites. Consequently, our multilayer analysis of the protein-RNA interactome of AATF reveals this protein to be an important hub for protein and RNA interactions involved in ribosome biogenesis. Considering its role in the prevention of apoptosis, the involvement of AATF in the DNA damage response and its impact on p53 function are explicitly interesting. The functional impact of these findings is emphasized by data showing AATF as a pro-tumorigenic factor in several tumor models5. Upon DNA damage AATF is phosphorylated by checkpoint kinases leading to its interaction with the NF-kB subunit p65 and relocalization to the p53 promoter7. However, AATF also modulates the specificity of p53 by shifting its binding preference towards target genes leading to growth arrest over those that mediate apoptosis8. Furthermore, AATF has been shown to play a role in other key pathways involved in tumorigenesis, namely mTOR- and HIF-signaling10. Seeing the impact of this protein on signal transduction in apoptosis and tumor formation, its potential as a therapeutic target in cancer therapy has been discussed extensively12. Interestingly, AATF – whilst also detected in the cyto- and nucleoplasm – primarily localizes to the nucleolus with the nucleolar fraction mediating the impact of AATF on c-JUN-dependent apoptosis13. The strongly regulated biogenesis of ribosomes starts in the nucleolus where transcription and most of the processing of the large primary transcript (45S rRNA) occur. This 45S rRNA contains the 18S, 5.8S and 28S rRNAs interspersed by internal and external transcribed spacers (ITS1/2 and 5′-/3′-ETS). To create the immense molecular machine of a ribosome, ribosomal proteins (r-proteins), pre-ribosomal factors (PRFs) and small nucleolar RNAs (snoRNAs) assemble with the 45S rRNA to support pre-RNA folding, deposit RNA modifications and to remove ITS and ETS regions by endonucleolytic and exonucleolytic cleavage as reviewed by Henras et al.14. Ultimately, in mammalian cells the 60S large subunit is assembled from the 5S, 5.8S, 28S rRNAs and 46 ribosomal proteins (r-proteins) whereas the 40S small subunit contains the 18S rRNA and 33 r-proteins. It is important to note that AATF was recently identified by independent RNAi screens for factors involved in ribosomal subunit production and rRNA processing16. This connection is interesting due to two considerations. Firstly, cell proliferation and ribosome biogenesis rate are closely intertwined, and increased ribosome formation is linked to tumorigenesis18. Secondly, blocking ribosome formation induces ribosome biogenesis stress leading to the activation of p53 mediated by inhibition of MDM2. Vice versa, induction of ribosome biogenesis inhibits p5320. However, the mode of action of AATF in ribosome maturation has remained unclear. A first insight came recently from a study by Bammert et al.21 that could show AATF as part of a nucleolar protein complex (termed ANN complex). Here, AATF, together with NOL10 and NGDN, was essential to efficient generation of the small ribosomal subunit (SSU) - in line with findings from previous screens of ribosome biogenesis factors15. Yet, the molecular function that AATF fulfils within this complex and the question which one of the three proteins mediates binding to rRNA precursors remained elusive. Work published recently shows AATF to be amongst 211 RNA polymerase I-dependent RNA-binding proteins22. However, a global characterization of its RNA interaction partners and binding sites that allows for a better understanding of its functional implications in ribosome biogenesis were missing. We confirmed AATF to be an RNA-binding protein and extended the analysis to a comprehensive characterization of its RNA-interactome using RNA-sequencing of its targets after crosslinking and immunoprecipitation (eCLIP-seq). Our combination of this approach – showing AATF to bind primarily rRNA precursor molecules and mRNAs encoding proteins required for ribosome biogenesis – with the analysis of its protein interactome suggests AATF to be a central player in the coordination of protein and RNA components in the maturation of the mammalian SSU.The apoptosis antagonizing transcription factor (AATF), also known as Che-1, was originally identified as an RNA polymerase II interacting protein with anti-apoptotic capacities. Through its interaction with RNAPII, AATF modulates the function of a row of transcription factors including pRB, p65 and STAT3, and has been shown to be a transcription factor itself23. In order to confirm this finding and to show it is not limited to our experiment we screened for the presence of AATF in published RIC datasets. Despite the fact that these studies examined different species and cell lines and used different experimental strategies, AATF as well as its mouse (Aatf/Traube) and yeast (Bfr2) orthologues were among the most strongly enriched proteins  data generated by the ENCODE consortium30  generated by the ENCODE consortium  had not been identified21. The NucleOlar localization sequence Detector (NoD)32 predicts two putative NoLS in the C-terminal portion of the protein (amino acid position 326 to 345 and position 494 to 522)  of cultured mouse inner medullary collecting duct (mIMCD3) cells identified the highly conserved mouse orthologue of AATF, Aatf/Traube, as one of the most enriched proteins in the RNA-bound proteome  processing as depicted for the sites in the 5′ external transcribed spacer (5′ ETS) – 01, A0 and 1 – and the first sites in internal transcribed spacer 1 (ITS1) following the sequence of 18S  compared to H/ACA snoRNAs (n = 19) and scaRNAs (n = 7) indicating a specific function associated with C/D box snoRNPs  with the strongest fold enrichment of C/D box snoRNAs and no such overrepresentation is detected for H/ACA box snoRNAs  of FLAG-tagged AATF expressed from a single-copy transgene. In parallel, an immunoprecipitation in a control cell line expressing FLAG-tagged GFP to compare the abundance of proteins in both datasets was performed. We identified 165 proteins significantly enriched by AATF precipitation  that had partly been discovered and validated before to be associated with AATF in yeast or mammals  93 out of 165 interactors identified without RNase still reached our criteria in the experiment after RNase treatment    which still results in only 19 putatively RNA-dependent interactors of AATF  and -independent (59) RBPs  required for efficient generation of the SSU21. However, the actual RBP associating this protein complex to pre-rRNA molecules had not been identified due to a lack of canonical RNA-binding domains. Recently, work by Piñeiro et al. showed AATF to be an RNAPI-associated RBP itself22. Their findings are in clear accordance with the fact that AATF has been identified in a row of RNA-interactome capture screens in different species29. A key finding of our study is the identification of the actual RNA-molecules bound by AATF using eCLIP with a clear overrepresentation of ribosomal RNAs. Furthermore, this approach allowed us to localize the binding sites in the 45S precursor molecule primarily to the cleavage sites required for SSU maturation, strengthening the hypothesized role of AATF in this process  resulted in 45S rRNA processing defects at the cleavage sites that are necessary for SSU generation. Our data provide strong indications that AATF is the actual RBP of this complex. However – just like AATF itself – both NGDN and NOL10 were detected in screens for polyA-tailed RNA-associated proteins and may possess the capacity to bind RNA themselves29. Indeed, Pineiro et al. showed NGDN binding to rRNA using RIP-qPCR22. As mentioned above, none of the ANN complex constituents – including AATF – possess a canonical RNA binding domain. While Bammert et al.21 argued that RNA binding of the ANN complex may be mediated by the WD40 domain of NOL10 which had previously been shown to mediate RNA-binding of other proteins43, the intrinsically disordered, basic C-terminus of AATF, 20% of which is made up of lysine (K) and arginine (R) residues, appears likely to promote RNA binding. This is also in line with recent studies that could emphasize the importance of intrinsically disordered regions (IDR) for RNA-protein interactions44 and the results of recent proteome-wide screens for RNA-binding domains44. Interestingly, work by He et al. showed that the two NoLS of AATF, the deletion of which resulted in a loss of rRNA binding in our hands, were directly associated with RNA26. Nevertheless, future experiments will be necessary to further dissect the exact molecular requirements of the physical interaction between AATF and RNA.AATF had previously been associated with ribosome biogenesis due to its nucleolar localization and the impact of loss of the protein on the generation of the 40S subunit and rRNA maturationOur multilayer analysis - involving the global identification of the protein and RNA interactome - provides further evidence regarding the role of AATF in ribosome maturation. Beyond rRNA precursor molecules themselves AATF binds other RNA species important to the biogenesis of the ribosome. On the one hand, the mRNAs co-precipitating with AATF primarily encode proteins involved with RNA metabolism and ribosome maturation Fig. . Here, i31. As to human disease this protein has been implicated to play an important role in cancer biology, a hypothesis that is partly based upon its ability to inhibit apoptosis. AATF has previously been found to be amplified or overexpressed in both hematological and solid tissue tumors, to correlate with poor prognosis, relapse and reduced survival45 and to mediate its effects on apoptosis by the modulation of p53 abundance and function6. Furthermore, increased ribosome biogenesis is not only employed by tumors to increase their proliferative potential but appears to be a risk factor for cancer onset on its own46. However, AATF appears to ensure cellular survival not only in the setting of cancer: its cytoprotective role has also been established in the setting of oxidative stress exposure to different cell types, including renal tubular cells and cardiomyocytes in models of acute kidney injury and ischemic reperfusion injury49. The known link between ribosome stress and p53 activity in the light of our new data allows for the exciting hypothesis that AATF mediates its effects on cell death employing this pathway. Based on the interaction between r-proteins and the E3 ubiquitin ligase MDM2, defects in ribosome maturation activate p5352. Loss of AATF would thus increase p53 activity linking the roles of AATF in ribosome biogenesis and p53 activity. In the light of an increasing number of studies trying to target ribosome biogenesis in this setting53 and taking into account that AATF has been shown to sensitize cancer but not normal cells to antineoplastic drugs5, future research shedding light on these aspects will be highly valuable.How is the molecular function of AATF in ribosome biogenesis linked to the previously described phenotypes regarding cellular proliferation and tumorigenesis? With rRNA availability being a central requirement for cellular survival and cell division, it is not surprising that loss of AATF in a knockout mouse model led to early embryonic lethalityFor the generation of GFP-tagged AATF transgenes and transient overexpression of full-length AATF or 2ΔNoLS truncation, the AATF wild type sequence or the truncated version of it, generated by overlap extension PCR, were cloned into the modified AAV CAGGS GFP plasmid (Addgene #22212) or pcDNA6 (Invitrogen) using restriction enzymes. siRNA duplexes targeting the 3′ UTR of human AATF (accession number AJ249940.2) were custom-designed by Dharmacon.2 and routinely passaged using 0.05% Trypsin. Mycoplasma contamination was excluded using a commercial kit . Transfections for transient overexpression or stable integration of GFP-tagged transgenes were carried out on 60–80% confluent cells using calcium phosphate as described previously54, or lipofection (Lipofectamine 2000 and Lipofectamine LTX) and electroporation (Amaxa Nucleofector® kit V) according to the manufacturers’ instructions.Human HEK 293T and U2OS, and mouse IMCD-3 cells were grown in standard media at 37 °C, 5% CO56. 24 h after transfection, cell lines were steadily selected with 2 µg/ml Puromycin. All cell lines were genotyped by integration PCR and phenotyped by both immunoblot and fluorescence light microscopy.Transgenic cells were generated using TALEN plasmids targeting the AAVS1 locus  as described previouslyFor knockdown experiments using commercial siRNA pools (Dharmacon), cell lines were transfected with Lipofectamine RNAiMAX and incubated for 48 h .57. In brief, cells from two biological replicates were crosslinked using UV-C irradiation , subsequently lysed on ice and sonicated followed by DNA digestion and limited digestion of RNA using Turbo DNase (Thermo Scientific) and RNase I, respectively. A lysate aliquot was removed to serve as input control. Endogenous AATF was precipitated using primary anti-AATF antibody (Bethyl). Co-precipitated RNA was dephosphorylated and a 3′ RNA linker was ligated using T4 RNA ligase (NEB). The AATF-RNA complexes and input controls were run on a NuPage bis-tris protein gel, transferred to a nitrocellulose membrane and subsequently cut from the membrane to extract protein-bound RNA by Proteinase K digestion. RNA was cleaned using the RNA Clean & Concentrator kit (Zymo Research) and reverse transcribed using SuperScript III Reverse Transkrition kit (Thermo Scientific). After exonuclease digestion and alkaline phosphatase treatment , a 3′ DNA adapter was ligated using the T4 RNA-ligase. The resulting cDNA library was amplified using Q5 Polymerase (NEB) and purified for sequencing analysis.eCLIP of AATF in K562 and HepG2 cells using primary anti-AATF antibody (Bethyl) was performed as previously describedet al. in preparation). To quantify relative enrichment between IP and input, relative information was calculated as the Kullback-Leibler divergence: pi is the fraction of total reads in IP that map to position i, and qi is the fraction of total reads in input for the same position. Regarding the definition of non-rRNA interaction partners we filtered the total dataset  for significant peaks over input  and collapsed all peaks mapping to one transcript to define a list of targets. For AATF eCLIP data accessibility refer to “Availability” section below.In addition to standard read processing and processing of reads to identify unique genomic mapping, reads mapping ribosomal RNA were quantified using a family-aware repeat element mapping pipeline that identifies reads unique to 45S pre-rRNA, 18S rRNA, or 28S rRNA respectively . This resulted in 35 nucleolar proteins, 73 non-nucleolar proteins and 42 proteins that could not clearly be assigned (due to conflicting evidence in the literature) contained in the set of 150 RBPs of the ENCODE consortium58 (https://www.encodeproject.org/) that are the basis to the group of nucleolar proteins shown in the eCLIP analyses of this study  on ice. Following lysis in lysis buffer  the samples were homogenized by passing three times through a 23 G needle and sonicated  at 4 °C. The samples were treated with Turbo-DNase (Thermo Scientific) at 37 °C for 5 min and a lysate aliquot was removed to serve as input control. FLAG (M2) antibody (Sigma-Aldrich) to provide the manufacturer of the antibody was coupled to protein G dynabeads (Thermo Scientific). The RNA-protein complexes were immunoprecipitated over night at 4 °C. Beads were washed three times with high salt buffer  and five times with wash buffer . Subsequently, the RNA was recovered by TRIzol extraction and concentrated with RNA clean and concentrator columns (Zymo Research). Equal amounts of immunoprecipitated RNA were reverse transcribed using SuperScript III as described in the supplemental methods section and analysed by RT-qPCR. For quantitative analysis of 18S and 45S rRNA binding, the delta delta-CT method59 was applied to calculate the delta CT for full-length and mutant AATF as well as RFP (CTIP − CTINPUT) and the subsequent delta delta-CT  for three biological replicates. Error bars represent standard deviation calculated for these three biological replicates. For uncropped blots, see Suppl. Fig. HEK 293T cells were transfected with pcDNA6 plasmids containing sequences for triple FLAG-tagged wild-type AATF, a truncation lacking both nucleolar localization sites (AATF 2ΔNoLS), or red fluorescent protein (RFP). 24 h after transfection the cells were washed twice with PBS and UV crosslinked  for 30 min at 4 °C. After three washing steps, samples were eluted from the column in Urea 2 M buffer and subsequently reduced  and alkylated . Proteins were digested at 37 °C for 16 h using trypsin and lysC (both at a 1:75 enzyme-protein ratio) following standard protocols. Formic acid was added to a final concentration of 1% to stop proteolysis. Peptides were loaded onto StageTips, desalted and labeled on column using triplex dimethyl labelling61. Labels were shuffled between biological replicates to exclude any bias. After elution, light, medium and heavy-labelled peptides from one experiment were pooled, dried down in a vacuum concentrator. Peptides were stored in 5% DMSO/1% formic acid at −20 °C prior LC-MS analysis63. For details on MS processing see Suppl. Methods.Seven 10 cm dishes (80% confluency) of HEK 293T Flp-In™ T-REx™ with stably integrated, inducible FLAG/HA-tagged AATF or GFP were used per replicate . Cells were harvested in media, washed with ice-cold PBS and then lysed in modified RIPA buffer containing 1% NP-40 (IgPAL), 150 mM NaCl, 0.25% sodium-deoxycholate and 50 mM Tris and complete protease inhibitors . Lysates were sonicated , passed through a 23 G syringe 3 times and subsequently cleared using centrifugation  and ultracentrifugation . The resulting supernatants were incubated with anti-FLAG beads (Miltenyi Biotec) for 2 h at 4 °C. Co-immunoprecipitated proteins were isolated using magnetic μMACS columns (Miltenyi Biotec) as previously described64. Briefly, MS2 spectra were searched against the Uniprot HUMAN.fasta database, including a list of common contaminants. False discovery rates on protein and PSM level were estimated by the target-decoy approach to 1% (Protein FDR) and 1% (PSM FDR) respectively. The minimal peptide length was set to 7 amino acids and carbamidomethylation at cysteine residues was considered as a fixed modification. Oxidation (M) and Acetyl (Protein N-term) were included as variable modifications. The match-between runs option was disabled. Dimethyl triplex labeling quantification was used, and the re-quantify option was enabled. Maxquant output files were further processed using Perseus (version 1.5.5.3) and R/Bioconductor. Obtained protein ratios were log2 transformed. Proteins flagged as “only identified by site”, “reverse” and “potential contaminant” were removed from the data set. In total, 5 biological replicates were analyzed . Labels were shuffled between biological replicates to exclude any bias. Statistical significance of putative AATF interactors was assessed utilizing a one-sided t-test (fudge factor S0 = 1)65, using the log2 transformed ratios of AATF vs GFP pulldown  to determine statistical significance. For both data sets, q-values were determined using the method of Benjamini and Hochberg.All mass spectrometric raw data were processed with Maxquant (version 1.5.3.8) using default parameters. Protein quantification was performed based on MS1-level peptide ion intensities. Maxquant first determines peptide ion intensities, which are used to calculate peptide ratios between light/medium/heavy labeled peptides. Protein ratios are derived from the mean of all associated peptide ratios and subsequently used for statistical testing. This is described in detail in the Maxquant publicationown Fig. . For comFor immunoprecipitation of endogenous AATF, one 10 cm dish of HEK 293T cells were used. Cells were harvested in PBS, resuspended in modified RIPA buffer , homogenized through a 23 G needle on ice, sonicated  and incubated with 2 U/ml Turbo DNase (Invitrogen) for 10 min at 37 °C, shaking. Immunoprecipitation with anti-AATF antibody  was performed as described above, with rabbit IgG serving as negative control, using magnetic Dynabeads Potein G beads (ThermoFisher Scientific). After overnight incubation, beads were concentrated on-magnet, washed five times with modified RIPA buffer and boiled with equal amounts of SDS loading buffer. Samples were analyzed by Western blotting (Suppl. methods). For uncropped blots of all replicates, see Suppl. Fig. Protein interactome PRIDE database: Project accession: PXD011055 eCLIP data GEO: series GSE107766; samples GSM2878484, GSM2878485, GSM2878554 eCLIP data ENCODE: ENCSR819XBT.Suppl. DataSuppl. Table 1Suppl. Table 2Suppl. Table 3


CTNNB1 and FN1 were highly edited and significantly overexpressed in malignantly transformed cell lines, thus may be responsible for the lung cancer progression. Our work provides a systematic analysis of RNA editing from cell lines derived from human bronchial epithelial cells with high-throughput RNA sequencing and DNA sequencing. Moreover, these results provide further evidence for RNA editing as an important tumorigenesis mechanism.Adenosine (A) to inosine (I) RNA editing is the most prevalent RNA editing mechanism in humans and plays critical roles in tumorigenesis. However, the effects of radiation on RNA editing were poorly understood, and a deeper understanding of the radiation-induced cancer is imperative. Here, we analyzed BEP2D  and radiation-induced malignantly transformed cell lines with next generation sequencing. By performing an integrated analysis of A-to-I RNA editing, we found that single-nucleotide variants (SNVs) might induce the downregulation of ADAR2 enzymes, and further caused the abnormal occurrence of RNA editing in malignantly transformed cell lines. These editing events were significantly enriched in differentially expressed genes between normal cell line and malignantly transformed cell lines. In addition, oncogenes  Lung cancer is the most frequent cancer and the leading cause of cancer death among males, and radGABRA3 editing suppresses breast cancer metastasis [RNA editing is a post-transcriptional modification process, the deamination of adenosines (A) to inosines (I) is the prominent RNA editing event in humans, where ADAR enzymes convert A to I and destabilize double-stranded without affecting the DNA sequence identity . Intrigutastasis . Recent tastasis . HoweverADAR2 gene were correlated with the abnormal editing events in malignantly transformed cell lines. Finally, we reported two potential edited genes, CTNNB1 and FN1, in malignantly transformed cell lines.Here, we investigated A-to-I RNA editing in human bronchial epithelial cell line (BEP2D) and malignantly transformed cell lines (BERP35T1 and BERP35T4), which were important models to characterize the radiation-mediated carcinogenesis of lung , 14. By 238PuO2. Two transformed cell lines, BERP35T1 and BERP35T4, were investigated  and technical artifacts caused by sequencing or read-mapping errors –20. To aWe next investigated the difference of A-to-I RNA editing between BEP2D cell line and malignantly transformed cell lines. First, 3,683~4,217 editing events in BEP2D cell line were not detected in malignantly transformed cell lines, and 1,004~1,844 new editing events occurred from normal BEP2D cell to malignantly transformed cells, indicating dramatic changes of RNA editing when BEP2D cell line was irradiated , gene exNext, we examined genes targeted by A-to-I RNA editing sites (edited genes for short). In general, 484, 426 and 305 genes were edited in BEP2D, BERP35T1 and BERP35T4 cell lines . We founp value < 1E-3, hypergeometric test, To examine whether RNA editing affects transcription activity, we identified differentially expressed genes (DEGs) by performing Cuffdiff program  S3 Tabl. We founADAR1, ADAR2 and ADAR3 [ADAR genes. The expression level of ADAR1 in BEP2D cell line was comparable to that in BERP35T1 and BERP35T4 cell lines and ADAR3 was silence in both BEP2D cell line and malignantly transformed cell lines -immortalized human bronchial epithelial cell line and was established by Dr Curtis C. Harris  . These cous paper. The celTM RNA Library Prep Kit for Illumina  following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~200 bp in length, the library fragments were purified with AMPure XP system . Then 3 μl USER Enzyme  was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.Total RNAs were extracted from cells with RNAiso Reagent  following the manufacturer’s instruction. RNA degradation and contamination were monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer spectrophotometer . RNA concentration was measured using Qubit RNA Assay Kit in Qubit 2.0 Flurometer . RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system . A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext UltraThe clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 150 bp paired-end reads were generated.Total DNAs were extracted from cells with DNAiso Reagent  following the manufacturer’s instruction. The quality of isolated genomic DNA was verified by using these two methods in combination: (1) DNA degradation and contamination were monitored on 1% agarose gels. (2) DNA concentration was measured by Qubit DNA Assay Kit in Qubit 2.0 Flurometer. A total amount of 1μg DNA per sample was used as input material for the DNA library preparations. Sequencing library was generated using Truseq Nano DNA HT Sample Prep Kit  following manufacturer’s recommendations and index codes were added to each sample. Briefly, genomic DNA sample was fragmented by sonication to a size of 350 bp. Then DNA fragments were endpolished, A-tailed, and ligated with the full-length adapter for Illumina sequencing, followed by further PCR amplification. After PCR products were purified (AMPure XP system), libraries were analyzed for size distribution by Agilent 2100 Bioanalyzer and quantified by real-time PCR (3nM).The clustering of the index-coded samples was performed on a cBot Cluster Generation System using Hiseq PE Cluster Kit (Illumina) according to the manufacturer’s instructions. After cluster generation, the DNA libraries were sequenced on Illumina Hiseq platform and 150 bp paired-end reads were generated.We adopted three previously published methods to accurately identify A-to-I RNA editing sites.http://picard.sourceforge.net/) was then used to remove identical reads (PCR duplicates) that mapped to the same location. GATK tools were used to perform local realignment around insertion and/or deletion polymorphisms and to recalibrate base quality scores. Variant calling was performed using GATK UnifiedGenotyper tool with options stand_call_conf of 0 and stand_emit_conf of 0. Further filtering were performed as described [For Jinbilly’s method , we usedescribed .For GIREMI method , RNA-seqFor RNAEditor , fastq fhttp://picard.sourceforge.net/) was then used to remove identical reads (PCR duplicates) that mapped to the same location. GATK tools were used to perform local realignment around insertion and/or deletion polymorphisms and to recalibrate base quality scores. Variant calling was performed using GATK UnifiedGenotyper tool.We used the Bowtie2  to alignp-value was reported to show the significance of DEGs.Gene expression was calculated using Cufflinks program default parameters. Differentially expressed genes (DEGs) were identified by Cuffdiff program, three biological replicates for each cell line were combined as the input of Cuffdiff and a RNA editing targeted genes were assigned with ‘bedops’ program. GO analysis were performed by using DAVID .The sequencing data have been deposited with the Gene Expression Omnibus under the accession ID GSE126723.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Semen analysis is one of the standard diagnostic tools currently used to assess male infertility and reproductive toxicity. However, semen analysis has a limited ability to separate fertile from infertile populations. Additional methods to detect impaired fertility are needed. The purpose of the present study was to evaluate how spermatozoal RNA content varies with sociodemographic and behavior/lifestyle factors, and to determine if spermatozoal large and small RNAs discriminate normal from abnormal spermatozoa. Semen specimens were collected from 133 men aged between 18 to 55 years undergoing semen analysis as part of couple infertility evaluation while 10 proven fertile donors were recruited as control group. Semen samples were classified as normal or abnormal according to World Health Organization (WHO) 2010 criteria. Sperm RNAs were extracted after somatic cells were lysed, and the association of large or small RNA content with semen quality and sociodemographic and behavioral/lifestyle factors was evaluated using a generalized additive model and one-way ANOVA. Inverse relationship was observed between large RNA content and sperm parameters such as sperm count, density and motility. Large RNA content per sperm was significantly increased in semen samples showing abnormal number of round cells. Furthermore, sperm motility was inversely associated with spermatozoal small RNA contents. Grouping donors by the number of semen abnormalities, we observed significant increased spermatozoal large and small RNA content in men with more than two semen abnormalities. Alcohol consumption was strongly associated with increased large RNA per sperm concentration after adjustment for age and BMI. Our study demonstrates a strong relationship between spermatozoal large RNA content and poor semen characteristics that may lead to a role in the assessment of male fertility, and may be used as an endpoint for reproductive toxicology risk assessment. Semen analysis remains the cornerstone of male infertility factor evaluation in subfertile couples and is the first laboratory test a clinician will order to evaluate male reproductive tract function –3. SemenMature spermatozoa have little cytoplasm and a highly condensed chromatin architecture that is enriched in protamines. The presence of ribonucleic acid (RNA) in mature ejaculated sperm has been previously demonstrated as both transcription and translation occur, not in the cytoplasm of mature spermatozoa, but in the mitochondria . Mature Development of sperm RNA biomarkers has been hindered by the difficulty of RNA sample preparation from sperm and the heterogeneity of RNA within an individual semen sample –19. In aHuman semen samples were collected between 2013–2015 from a total of 133 male patients aged between 18 to 55 years presenting to the Division of Urology  for male factor infertility evaluation. Men 18–55 years old (n = 10) who had at least one child and were presenting for a vasectomy were recruited between 2017–2018 for this study as the control group. Exclusion criteria were based on sperm availability. Men who were azoospermic due to testicular failure or obstruction were not studied, as they had no sperm available. All other male patients age 18–55 were considered eligible, regardless of race or ethnicity. Semen samples were collected in sterile conical tubes after 2–5 days of sexual abstinence and allowed to liquefy at 37 °C for 30 minutes before clinical diagnosis according to World Health Organization guidelines . Sperm mSpermatozoal RNA was extracted at the time of each semen collection following semen analysis as described previously . BrieflyWe performed statistical analysis using GraphPad Prism 7 software, SAS and R Studio. We used generalized additive models, a non-parametric regression model, to characterize potential non-linear curvature relations of spermatozoal large and small RNA yields in function of semen quality endpoints . BecauseStudent’s t-test was used to assess statistical differences in the log-transformed spermatozoal large and small RNA contents between semen samples divided in clinically normal or abnormal according to World Health criteria 2010 criteria.2), race/ethnicity . Caffeine totals consumed from coffee, tea  and soda were added together to create a variable for daily consumption. Chocolate was not included in the caffeine intake. We adjusted our analysis of alcohol for age and BMI since they were associated with small RNA content per sperm at an alpha of 0.2. One-way ANOVA was also used to compare large and small RNA content in donors showing normal semen parameters, one abnormal semen parameter, two abnormal semen parameters and more than two abnormal semen parameters with proven fertile donors. Data were presented as mean ± standard error of the mean (SEM). Values were considered to be significant at p-value < 0.05.One-way ANOVA was used to assess RNA contents per sperm as a function of sociodemographic and lifestyle factors. These factors included alcohol intake , caffeine intake , smoking , exercise frequency , age , body mass index . Overall, 92%, 91%, 92%, 84%, and 77% of participants had normal sperm density (> 15 million/ml), total sperm counts (>40 million/ejaculated), sperm motility (>40%), semen viscosity, and absence of agglutination, respectively .6 sperm and densities up to ~100 x 106 sperm/million, respectively. Total sperm count and sperm densities above these levels were not associated with small RNA content.Using GAMs, both total sperm count , sperm density  and motility  compared to the clinically normal ones. Data were analyzed by two-tailed Student’s test and expressed as mean ± SEM .(TIF)Click here for additional data file.


We investigated the functional link between lncRNAs and the m6A modification in YAP signaling and CRC progression.YAP activation is crucial for cancer development including colorectal cancer (CRC). Nevertheless, it remains unclear whether N6-Methyladenosine (m6A modified targets of YTHDF3 in CRC. Gain-of-function and Loss-of-function analysis were performed to measure the function of GAS5-YAP-YTHDF3 axis in CRC progression in vitro and in vivo.YAP interacting lncRNAs were screened by RIP-sequencing, RNA FISH and immunofluorescence co-staining assays. Interaction between YAP and lncRNA GAS5 was studied by biochemical methods. MeRIP-sequencing combined with lncRNA-sequencing were used to identify the m6A reader YTHDF3 not only a novel target of YAP but also a key player in YAP signaling by facilitating m6A-modified lncRNA GAS5 degradation, which profile a new insight into CRC progression. Clinically, lncRNA GAS5 expressions is negatively correlated with YAP and YTHDF3 protein levels in tumors from CRC patients.GAS5 directly interacts with WW domain of YAP to facilitate translocation of endogenous YAP from the nucleus to the cytoplasm and promotes phosphorylation and subsequently ubiquitin-mediated degradation of YAP to inhibit CRC progression in vitro and in vivo. Notably, we demonstrate the m6A-induced decay of GAS5 on YAP signaling in progression of CRC which may offer a promising approach for CRC treatment.Our study uncovers a negative functional loop of lncRNA GAS5-YAP-YTHDF3 axis, and identifies a new mechanism for m Colorectal cancer (CRC) is one of the leading cancer types resulting in new cancer cases and deaths worldwide . IncreasS127 expression [Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that have no or limited protein-coding capacity. A large body of evidence has demonstrated that lncRNAs are engaged in the signaling pathways of CRC. LncRNAs are important versatile molecules involved in a variety of tumorigenic processes and diseases via interactions with DNA, RNA, or proteins. LncRNAs execute molecular functions as archetypes of decoys, signals, guides, and scaffolds . For inspression . Neverth6A) RNA modification, as the most abundant internal epi-transcriptomic modification in eukaryotic messenger RNAs (mRNAs), is introduced by the m6A methyltransferase complex, which have been designated as a “writer,” and can be deleted by m6A demethylases, such as fat mass and obesity-associated protein (FTO) and ALKBH5. Factors interpreting specific modifications have been identified as “readers,” such as YTHDF1/2/3 and YTHDC1/2 [6A demethylase that is highly expressed in acute myeloid leukemia (AML), and it plays a critical oncogenic role [6A is reported to be important in cancer progression, whether lncRNAs regulate the m6A modification and the role of m6A in lncRNA transcripts in cancer progression remain unknown. Here, we aimed to investigate the functional links between lncRNAs and the m6A modification in YAP signaling in CRC.The N6-methyladenosine  was performed using jetPRIME , according to the manufacturer’s instructions. As we previously described, 24 h after transfection, cell lysates were subjected to western blot, and the western blot data were quantified using the ImageJ software [DLD1, LOVO, SW480, SW620, LS174T, HCT116, RKO, and HT29 cell lines were obtained from the Cell Bank of Type Culture Collection . The cell line authentication report showed the cell lines to be considered as identical to the reference cell line in the ATCC STR database. The cells were cultured in incubators containing 5% COsoftware . DetaileAn RIP experiment was performed according to the instructions of the Magna RIP RNA Binding Protein Immunoprecipitation Kit . Briefly, lysate was prepared in a lysis buffer containing protease inhibitor cocktail and RNase inhibitor. Then, protein A/G magnetic beads were prepared for incubation with 5 μg of purified antibodies per immunoprecipitation with rotation for 30 min at room temperature. Further, to precipitate RNA-binding protein-RNA complexes, and the mixture was incubated with rotation for 3 h overnight at 4 °C. Finally, RNA was purified using proteinase K buffer and an Agilent 2100 Bioanalyzer . A NanoDrop 2000  was used to analyze the total RNA quality and quantity. Further RNA purification in an immunoprecipitation may be pursued by deep sequencing or quantitative reverse transcription polymerase chain reaction (qRT-PCR). The cDNA libraries were sequenced on the Illumina sequencing platform by Genedenovo Biotechnology Co., Ltd. .6A Kit . Briefly, the total cell RNA is fragmented into ~ 100-nt-long oligonucleotides using fragmentation buffer under elevated temperature. Then the post-fragmentation size distribution is validated by an Agilent 2100 Bioanalyzer with an Agilent RNA 6000 Kit. The Magna ChIP Protein A/G Magnetic Beads were incubatated for 30 min at room temperature with m6A-specific antibody in immunoprecipitation buffer. The mixture was then incubated with the MeRIP reaction mixture for 2 h at 4 °C. Then eluted RNA and MeRIPed RNA were analyzed by deep sequencing on an Illumina Novaseq™ 6000 platform at the LC-BIO Bio-tech ltd  following the vendor’s recommended protocol.Total RNA was extracted using Trizol reagent . An Agilent 2100 Bioanalyzer  and NanoDrop 2000  were used to analyze the total RNA quality and quantity. More than 50 μg of total RNA is sufficient following RNA fragmentation and immunoprecipitation according to the instructions of the Magna MeRIP™ m6 viable cells were subcutaneously injected into the flanks of mice. Tumor volume was assessed as (L × W2/2), where L and W represent the length and the width of the tumor, respectively. After 4 weeks, the tumors were embedded in paraffin and stained for in situ hybridization (ISH) or immunohistochemistry (IHC). For the lung metastasis model, 2 × 106 viable cells were injected into the tail veins of mice. The mice were monitored for 6 weeks for lung metastasis by hematoxylin-eosin (H&E) staining. The metastatic foci were calculated using Dmetrix software by combining the number and area of lung metastatic nodules in individual mice.BALB/c male mice 6–8 weeks old were purchased from Guangdong Medical Laboratory Animal Center, China. Mice were raised under pathogen-free conditions. All in vivo experiments were done according to approved protocols from the Institutional Animal Care and Use Committees, according to national and institutional guidelines. All procedures were performed essentially as previously described. Briefly, for the subcutaneously injected tumor model, 2 × 10Formalin-fixed paraffin-embedded (FFPE) colon cancer tissues and adjacent noncancerous tissues were retrieved from the Department of Pathology at Sun Yat-sen Memorial Hospital, Sun Yat-sen University . The study was approved by the Human Research Ethics Committees of Sun Yat-sen University. The study is compliant with all relevant ethical regulations for human research participants, and informed consent was obtained from all subjects.All procedures were performed essentially as previously described . BrieflyThe staining scores were evaluated by two individuals in a blinded fashion. A quick scoring system from 0 to 12 that combined the intensity and percentage of the positive signal was used as described previously , 18. BriBiotin-labeled RNAs were transcribed in vitro with the Biotin RNA Labeling Mix and T7 RNA polymerase . Biotinylated RNAs were mixed with streptavidin agarose beads  at 4 °C overnight. Total cell lysates were freshly prepared and added to each binding reaction with Protease/Phosphatase Inhibitor Cocktail and RNase inhibitor, and then the mixture was incubated with rotation for 1 h at 4 °C. After washing thoroughly three times, the RNA–protein binding mixture was boiled in SDS buffer and the eluted proteins were detected by western blot or mass spectrometry.A bio-layer interferometry (BLI) experiment was carried out using the Octet system . Streptavidin sensors were used for immobilization of biotin-labeled lncRNA GAS5. A five-point concentration series was assayed for purified YAP protein (10-nM to 1-μM range). Wells with assay buffer only were used as reference wells. Reference biosensors with no immobilized ligand was used to avoid nonspecific binding to GAS5 during the binding event. The procedure of Octet System Data Acquisition software was followed to analyze the kinetics. Dissociation (Kd) and association rate constants (Ka) were determined with the Octet Data Analysis Software, as a result of a global fit considering the entire step times, and assuming a 1:1 binding model.The locked nucleic acid-modified oligonucleotide probe targeting GAS5  was used for RNA fluorescent in situ hybridization (FISH). We detected YAP protein in situ in CRC cells with immunofluorescence assays. The anti-YAP1 antibody (Alexa Fluor 647)  was used to detective the YAP protein with a confocal microscope  (shown in red), and DAPI was used for labelling nuclear DNA (shown in blue). The RNA signal was detected by incubation with biotinylated conjugated anti-DIG antibodies, and the signals were amplified using SABC – FITC (shown in green).The chromatin immunoprecipitation (ChIP) procedure was performed using SimpleChIP® Enzymatic Chromatin IP Kit  following the manufacturer’s instructions. Briefly, cells are fixed with formaldehyde to cross-link histone and non-histone proteins to DNA. Then, chromatin is digested with micrococcal nuclease into 150- to 900-bp DNA/protein fragments. Antibodies specific to YAP proteins are added and the complex co-precipitates are captured by Protein G magnetic beads. Finally, cross-links are reversed, and the level of enrichment of the target DNA sequence is purified, at which point it is ready for PCR. One tenth of the input chromatin was also treated in the same way and purified. The enriched DNA fragments were presented as a percentage of input chromatin. Luciferase reporter assay was measured using the Dual-Glo Luciferase Assay System  following the manufacturer’s instructions. Briefly, YAP plasmids were co-transfected with YTHDF3 promoter-luciferase vector and pRL-TK Vector. The pGL3-basic vector was transfected as a negative control. After 24 h, prepared cell extracts were used to measure the luciferase activity on a Spark multimode microplate reader .P < 0.05, **P < 0.01, ***P < 0.001). A multi-way classification analysis of variance tests was performed to assess data obtained from the CCK8 assays and tumor growth. Survival curves were plotted based on the Kaplan–Meier curves and log-rank tests. Correlations among GAS5 expression, YAP, and YTHDF3 were analyzed with a Spearman rank correlation. P < 0.05 was considered to indicate a significant difference. Each experiment was repeated independently with similar results at least three times.All statistical analysis was carried out using GraphPad Prism version 7  for Windows to assess the differences between experimental groups. The data were analyzed by analysis of variance tests or Student’s t-tests  analysis, RNA fluorescent in situ hybridization (RNA FISH) and immunofluorescence co-staining assay, to identify the interaction between lncRNA GAS5 and YAP. BLI analysis was performed to examine the binding affinity of YAP–GAS5 complex with a Fortebio Octet system. The illustration in Fig.  nM Fig. l. Furthep-value< 0.05), mostly deregulated in the same direction and enriched in the Hippo pathway, strongly supporting the key role of GAS5 in YAP signaling . The down-regulation of genes in the YAP knockdown group could be directly suppressed by the elevated expression of GAS5. Surprisingly, among these target genes of YAP, YTH-domain family member 3 (YTHDF3), a cytoplasmic N6-methyladenosine (m6A) reader, was consistently found to be down-regulated in both groups by more than 10-fold  combined with transcription sequencing to clarify the mechanism underlying YTHDF3 in CRC  were used to identify the molecular function of YTHDF3 associated m6A modification genes, which showed that these genes were enriched for functions associated with protein binding  . Results showed that the total percentage of m6A in YTHDF3-silenced CRC cells increased two-fold compared with that in its negative control  and a positive relationship between YAP and YTHDF3  in 208 CRC specimens. As shown in Additional file P = 0.040) and TNM stage (P = 0.038) in cohort 1. Notably, YAP and YTHDF3 expression were also significantly correlated with TNM stage . The median survival time of the CRC patients with lower GAS5 expression was 40 months, which was significantly shorter than the survival time of those with higher GAS5 expression (72 months)  using Kaplan–Meier analysis. The median survival time of CRC patients with higher YTHDF3 expression was about 48 months, which was significantly shorter than those with lower YTHDF3 expression (about 86 months)  experiments.Additional file 5: Supplementary raw data 2. Analysis of lncRNA-seq-based differential expression after knockdown of YTHDF3.Additional file 6: Supplementary raw data 3. Analysis of MeRIP-seq analysis combined with transcription sequencing to clarify the differential expression after knockdown of YTHDF3.


Apple stem pitting virus (ASPV) mainly infects apple, pear and their closely related species in the world. ASPV causes some symptoms like leaf etiolation and stony pit in cultivated pear, but produces few symptoms in cultivated apple. We inoculated tobacco with ASPV, which originates from Nanking cherry (Prunus tomentosa), and we obtained tobacco RNA-sequencing data through high-throughput sequencing. In total, 17,401,736 clean reads were obtained after sequencing and removing adaptor sequences, contamination and low-quality reads. An RNA-seq data has been uploaded to Sequence Read Archive (SRA), which enables researchers to access the RNA-sequencing data of tobacco inoculated with ASPV. Some neew years . ASPV caculation . ASPV caculation .Genome of ASPV is a single-stranded linear RNA molecules of 12–15 nm × 800 nm in size . It is rCerasus tomentosa), which contains plenty of secondary metabolites, such as polyphenols and polysaccharides. We have reported ASPV infected Nanking cherry, however, we cannot amplify more genome region by RT-PCR from Nanking cherry [The variation of ASPV is very complicated. In addition, it is difficult to isolate high quality nucleic acid from the tissue of Nanking cherry  is 96.26%, and GC content among all four kinds of bases is 39.35%. The quality control of sample shows in 22.1Nanking cherry sample was obtained from Heilongjiang province of China in 2013, and ASPV was detected by RT-PCR and ELISA . ASPV wa2.2Frozen tissue was grinded into fine powder in liquid nitrogen before RNA extraction. The total RNA was extracted from 100mg tissue sample using the RNAprep pure Plant Kit  referred to manufacturer's instruction. RNA from plant tissue was fixed in the spin column after extraction by chloroform. Then, several washing steps were proceeded to elute genome RNA from spin column. RNA was concentrated by ethanol and stored in TE buffer. The RNA was analysed by agarose gel electrophoresis, while the concentration of RNA was determined using the Qubit Fluorometer   addition. After that, the short fragments are connected with adapters. The suitable fragments are selected for the PCR amplification. Then, A 800bp insert library was constructed and sequenced on an Illumina HiSeq 4000 platform . To obta


Cancer vaccines may be harnessed to incite immunity against poorly immunogenic tumors, however they have failed in therapeutic settings. Poor antigenicity coupled with systemic and intratumoral immune suppression have been significant drawbacks. RNA encoding for tumor associated or specific epitopes can serve as a more immunogenic and expeditious trigger of anti-tumor immunity. RNA stimulates innate immunity through toll like receptor stimulation producing type I interferon, and it mediates potent adaptive responses. Since RNA is inherently unstable, delivery systems have been developed to protect and deliver it to intended targets in vivo. In this review, we discuss liposomes as RNA delivery vehicles and their role as cancer vaccines. Cancer immunotherapy is a burgeoning field with evidence for anti-tumor activity in several randomized phase III trials ,2,3,4. MCancer vaccines can be utilized to induce de novo responses against tumor specific antigens. Prototypical vaccines are typically composed of antigen coupled with an adjuvant before local administration ,14,15. LMost prototypical vaccines involve peptides and tend to have poor immunogenicity, however they can be bound to adjuvants  to confer an immunologic response ,35,36. UOne drawback of RNA is its fundamental lack of stability, making it difficult to administer ‘naked’ RNA to patients in vivo. Since cancer vaccines must localize to APCs where RNA must be translated, processed, and presented on MHC class I and II molecules, degradation has been a potent barrier for the development of new mRNA technologies. To overcome these limitations, RNA has been administered through direct intranodal injections and has shown promise in early phase studies of refractory cancers ,55. RNA Nanocarriers have been developed as delivery vehicles for RNA, however novel designs remain mired with unknown in vivo reactivity, requiring rigorous preclinical toxicity studies in large animal models . Given tThe type of immune response that is elicited by liposomes is often affected by the lipid material’s surface properties. Liposomes may be polar or non-polar . Polar lThe electrostatic interactions between RNA and liposomes could have unintended effects in vivo. Small positively charged particles might aggregate, forming larger particles that could adhere to plasma proteins in the bloodstream . These iAlthough RNA loaded liposomes have been used in several preclinical studies and appear to have a promising safety profile, adverse effects have been reported. Positively charged particles have been shown to mediate transaminitis in preclinical studies and may elicit apoptosis of transfected cells ,72. MuchDOTAP is one of the first liposomes to be developed and has been under investigation for decades . As suchDue to the proof of concept studies demonstrating the safety of liposomes like DOTAP in first-in-human trial application, the translation of new RNA-liposome technologies are not mired with as many regulatory hurdles. Several RNA-liposomes have already been investigated in human trials, including RNA interfering agents that have been safely used in phase I studies. Small interfering RNAs (siRNAs) that inhibit vascular endothelial protein kinase N3 encapsulated in cationic liposomes for solid tumor disruption were well tolerated in a human phase I study ,82; thisWhile liposomes have predominantly been developed for intratumoral targeting via EPR effect, the development of mRNA-liposomes as cancer immunotherapeutic agents requires localization to APCs for the activation of antigen specific T cell immunity. For mRNA-liposomes, this poses a distinct challenge. After intravenous administration of lipid particles, first pass organs  are often the initial sites of transfection . When enAlternatively, our group has prioritized RNA-liposomal cancer vaccines consisting of personalized tumor derived mRNA (representing a tumor specific transcriptome) that is encapsulated into DOTAP NPs . By extrThough these RNA-liposomes are not targeted to a specific organ , the systemic release of type I interferon from in vivo transfected cells mediates activation of APCs throughout lymphoid compartments . We showUnlike synthetic RNA manufacturing , personalized tumor mRNA loaded liposomes allow APCs to process and present whole tumor transcriptome. Although this might induce autoimmunity, T cell receptors (TCRs) recognizing self-antigens should have been negatively selected by the thymus and are not expected to be present on autologous T cells. Consequently, TCRs recognizing the most foreign/immunogenic tumor epitopes should be activated, allowing the immune system to decide the best tumor targets. This strategy forgoes MHC binding prediction algorithms and other methods (mass spectroscopy) that are necessary to detect the most immunogenic tumor epitopes. Despite these advantages, tracking antigen specific immunity in vivo is challenging when the immunoreactive epitope is unclear. To more directly target tumor antigens using T cells, APCs may be bypassed through bispecific T cell engagers (BiTEs) and chimeric antigen receptor (CAR) modified T cells ,89. BiTEWhile still in the early stages, RNA-liposomes have been tested in human clinical studies. A dose-escalation study explored the safety and activity of tetravalent RNA-lipoplex vaccines (targeting four tumor-associated antigens) in patients with advanced melanomas . Aside from transitory flu-like illnesses, the vaccines have been safe and well tolerated with activity based on the induction of IP-10 and type I interferon ,92. MoreWhile innovative NP designs continue to be constrained by toxicology concerns and unknown in vivo effects, well characterized lipid-NPs have been prioritized as RNA delivery vehicles. Liposomes are composed of biocompatible materials and appear to have favorable safety profiles. Instead of engineering these lipid backbones with targeting moieties, changes to the charge ratio (of negatively charged RNA to positively charged liposome) and lipid composition can hone these particles to lymphoid organs. Other translational modifications that preserve these well-characterized lipid backbones ) may increase biocompatibility and liposome circulation time for an intratumoral EPR effect. RNA-liposomes are now in human clinical trials and have shown promising early results. While cancer vaccines have traditionally suffered from inadequate immunogenicity in therapeutic settings, RNA-nanoliposomes are expected to harness multiple arms of the immune system for expeditious and effective anti-tumor immunity.


Immunity to leishmaniasis requires selective T cell responses, with immunization schemes that raise either CD4 or CD8 T cell responses being protective in small animal models. We have defined the leishmaniasis vaccine candidate recombinant fusion antigens, LEISH-F2 and LEISH-F3+, that when formulated in a stable emulsion with a Toll-like receptor (TLR) 4 agonist, induce protective CD4+ T cell responses in animal models as well as providing therapeutic efficacy in canine leishmaniasis and in clinical trials in leishmaniasis patients. We used the genetic sequences of these validated vaccine antigens to design RNA vaccine constructs. Immunization of mice with the RNA replicons induced potent, local innate responses that were surprisingly independent of TLR7 and activated antigen-presenting cells (APC) to prime for extremely potent antigen-specific T helper 1 type responses upon heterologous boosting with either of the subunit vaccines . Inclusion of RNA in the immunization schedule also generated MHCI-restricted T cell responses. Immunization with LEISH-F2-expressing RNA vaccine followed later by subunit vaccine afforded protection against challenge with Leishmania donovani. Together, these data indicate the utility of heterologous prime-boost immunization schemes for the induction of potent antigen-specific CD4 and CD8 T cell responses for protection against intracellular pathogens.The rapid generation of strong T cell responses is highly desirable and viral vectors can have potent CD8 Although viral vectors such as alpha- and lentivirus-based constructs also potently activate T cells, less is known with regard to how RNA vaccine technology can be used for the induction and characterization of T cell responses against intracellular pathogens.It is critical that we develop effective interventions to counteract infectious agents that continue to emerge . RNA vacLeishmania donovani is an important human pathogen that can manifest visceral leishmaniasis (VL). It is estimated that up to 90% of humans infected with L. donovani remain in an asymptomatic state, with an effective antigen-specific T cell response critical for containing the infection and preventing advancement to VL  then assessed their ability to complement the activity of a defined subunit  vaccines for the rapid and potent generation of antigen-specific T cell responses.A plasmid encoding the 5′ and 3′ untranslated regions and nonstructural genes of Venezuelan equine encephalitis virus (VEEV) strain TC-83 was utilized as the replicon backbone, as previously described . Gene se5 cells per ml in the HEK-Blue™ detection medium (InvivoGen) then incubated with corresponding TLR agonists. Activation induced expression of SEAP  and were measured spectrophotomerically at OD650 nm.HEK293-Blue-TLR2, -TLR3, -TLR4, -TLR7, or null cells  were maintained in DMEM+GlutaMax cell culture medium containing 10% FCS, 1% penicillin and streptomycin, 50 ug/ml neomycin, and selective antibiotics . Cells were untreated or treated with the mixture of Lipofectamine™ 3,000 (Invitrogen) and F2 or control RNA constructs. For stimulation, cells were seeded into 96-well plates at a concentration of 2.5–5 × 10Female C57BL/6 mice  and TLR7–/–  were maintained in specific pathogen-free conditions and in accordance with animal procedures approved by the IDRI institutional animal care and use committee. Mice entered experiments at 6–8 weeks of age.Mice were injected with 20 μl vaccine into calf muscle. The RNA vaccine was prepared to provide a total of 10 μg dose. SLA-SE was injected at 5 μg per injection to serve as a positive control for immune activation. At various times after injection, the draining lymph node was excised and single cell suspensions prepared. Mononuclear cells were enumerated using a ViaCount assay with a PCA system .Mice were immunized by intramuscular injection of vaccines into the thigh.E. coli as previously described  were added to the plates. Plates were analyzed at 405 nm . Endpoint titer was determined as the last optical density (OD) value greater than a threshold determined by sera from saline-immunized control mice.Blood was collected from the retro-ortital sinus and serum prepared. Antigen-specific IgG responses were detected by enzyme linked immunosorbent assay (ELISA). Briefly, ELISA plates  were coated with 1 μg/ml antigen in 0.1 M bicarbonate buffer and blocked with 0.1% BSA-PBS. Then, in consecutive order and following washes in PBS/Tween, serially diluted serum samples, anti-mouse IgG-HRP  and ABTS-H5 cells per well in duplicate in a 96-well plate  in RPMI-1640 supplemented with 5% heat-inactivated FCS and 50,000 Units penicillin/streptomycin (Invitrogen). The antigen-specific recall response was determined by incubating the cells with 10 μg/ml recombinant protein or MHCI-restricted peptides for 3 days before collection of the culture supernatant. Cytokine (IL-5 and IFNγ) content in the supernatant was measured by ELISA according to the manufacturers' instructions .Spleens were removed 4 weeks after the final immunization and single cell suspensions prepared. Cells were cultured at 2 × 10To determine the immune cell composition of draining lymph nodes (DLN), single cell suspensions were prepared and incubated with Fc receptor block (clone2.4G2) before incubation at room temperature for 30 min with the following antibodies: anti-CD4 (clone RM4-5), anti-CD8a-FITC (clone 53-6.7), anti-CD11b (clone M1/70), anti-CD11c (clone N418), anti-CD19 (clone 1D3), anti-F4/80 (clone BM8), and anti-Ly6G (clone 1A8). Expression of selected surface molecules was determined by incubation with anti-CD69 (clone H1.2F3) and anti-CD80 (clone 16-10A1). Antibodies were purchased from BD Biosciences, eBioscience, BioLegend or Tonbo Biosciences.6 cells per well in duplicate in a 96-well plate  in RPMI-1,640 supplemented with 5% heat-inactivated FCS and 50,000 Units penicillin/streptomycin (Invitrogen) for 18–20 h in the presence of GolgiStop (BD Bioscience). Each sample was incubated with Fc receptor block (clone2.4G2) before incubation at room temperature for 30 min with the following antibodies: anti-CD4 (clone RM4-5), anti-CD8a-FITC (clone 53-6.7), and anti-CD44 (clone IM7). Expression of selected cytokines was determined by incubation with anti-IFNγ (clone XMG1.2), anti-IL-2 (clone JES6-5H4), anti-CD154 (clone MRI), anti-TNFα (clone MP6-XT22), anti-IL-5 (clone TRFK5), and anti-Granzyme B (NGZB). Antibodies were purchased from BD Biosciences, eBioscience, BioLegend or Tonbo Biosciences. Data were acquired using Fortessa (BD Biosciences) and analyzed with FlowJo software .Antigen-specific T cell memory responses generated by vaccination were determined following incubation of spleen cells with 10 μg/ml F2 recombinant protein. Cells were cultured at 1 × 106L. donovani (MHOM/SD/00/1S-2D) into the retro-orbital sinus. Four weeks later livers were harvested and homogenized. DNA was extracted from homogenate using QIAmp DNA mini kits (Qiagen) and quantified using Nanodrop UV-Vis spectrophotometer (ND-1000). L. donovani DNA was then detected by real-time PCR using primers for L42486  with FAM reporter sequence (5′- CAACGCGTATTCCC-3′) that detects a 203-bp genomic repeat region specific to Leishmania species (NCBI Blastn). Mouse Gapdh FAM (Life Technologies) was used as an internal reference control. The number of parasite per μl of DNA was determined by extrapolating the crossing points (Cps) of each sample against a standard curve generated with known quantities of parasites, then burdens expressed as parasites per organ.Mice were infected by injection of 1 × 10p-values were < 0.05.Statistical analyses were conducted using one-way analysis of variance and Dunnett's or Tukey's multiple comparison test used to compare two groups. Statistical significance was considered when the To determine if F2-RNA could promote activity through known innate immune receptors, HEK293-Blue cells expressing various human Toll-like receptor (TLR) were incubated with the RNA vaccine construct. The only tested condition that generated the induction of SEAP under the IFN-β promoter fused with NF-κB and AP-1 binding sites was when F2-RNA was incubated with HEK293-TLR7 cells that had been treated with Lipofectamine™  had measurable alterations in their draining lymph nodes, including upregulation of early activation markers . Injecti Figures . A large Figures . Both th Figures . Thus, Fin vitro finding indicating TLR7 engagement, we injected either saline or F2-RNA into wild type and TLR7–/– mice then compared the subsequent DLN cell numbers. Immunization caused a significant increase in cell numbers within the draining lymph nodes from both mouse genotypes  expression and production of IL-2, were induced by all of the immunization regimens . Homologous prime-boost with F2+SLA-SE generated a more robust CD4 T cell memory response than homologous prime-boost with F2-RNA as revealed by a greater proportion of cells or producing IFNγ and TNF or producing IL-2 and expressing CD154  immunizations with either F2+SLA-SE or F2-RNA did not significantly reduced liver parasite burdens in L. donovani-infected mice . Thus, shortened immunization schemes that included the RNA vaccine induced responses that were potent enough to protect against infection with an intracellular parasite.Having demonstrated that heterologous strategies using F2-RNA and F2+SLA-SE raised a mix of antigen-specific CD4 and CD8 T cell responses, we assessed if these responses could protect against e Figure . ParasitL. donovani. Insertion of the F2 gene into an alphavirus replicon created F2 RNA that induced innate responses via engagement of intracellular TLR7 and promoted local immune responses with activation of APC. While immunization with F2 RNA alone elicited only a small population of antigen-specific Th1 cells, a heterologous immunization scheme involving priming with F2 RNA followed by boosting with a LEISH-F2+SLA-SE vaccine (recombinant antigen/ synthetic adjuvant) resulted in extremely potent Th1 responses and IFNγ secretion.In the face of emerging infectious diseases and drug-resistant pathogens, vaccines that can quickly induce protective responses are typically preferred. It is critical that rapid and scaled production procedures of such vaccines are developed. As a proof-of-concept for the rapid generation of a vaccine that mediates protection through T cells, we created F2 RNA with a goal of protecting against infection with From their initial conception, by mimicking immunization with a live vaccine, nucleic acid vaccines, delivered virally, such as with viral replicon particles or similar systems have held promise as an effective way to induce T cell immunity. In particular, viral delivery of replicon RNA derived from the alphavirus genus has demonstrated potent CD8 T cell responses . Howeverde novo target, by building upon a subunit vaccine with an already defined target we were able to use a RNA vaccine producing the same target to enhance immune responses and reduce the need for more than two immunizations. Coupling of the RNA and subunit vaccines in a heterologous prime boost scheme generated extremely potent CD4 T cell responses that removed the need for multiple immunizations with the subunit vaccine. Our data therefore imply that under conditions where the available quantities of a defined vaccine may be limited , the rapid generation of an RNA vaccine encoding the same target antigen could be rapidly generated as a means of filling the supply gap.Clinical trials have demonstrated the safety and tolerability of nucleic acid vaccines , 37. TheInterestingly, our data indicate that immunization with F2 RNA alone generated immune responses that were qualitatively different from the LEISH F2 subunit vaccine. Immunization with the subunit vaccine induced slightly larger Th1 responses and very much larger antibody responses than immunization with the F2 RNA alone, which generated low Th1 responses and only very weak antibody responses. These data indicate the important impact of the antigen production/ presentation platform has on immunity, and suggest that naked RNA immunization can be used for the preferential induction of T cell responses. Indeed, experiments with F3+ RNA vaccine allowed us to distinguish the preferential induction of antigen-specific CD8 T cell responses. Long term immunization experiments are required to determine how long these CD4 and CD8 T cell responses persist. Minimizing antibody generation may be beneficial in clinical or therapeutic situations where immune complexes can cause complications.+ cells to upregulate CD80 and CD19+ cells to express high levels of CD69, these alterations were induced at lower levels consequent to F2 RNA injection. In contrast, F2 RNA caused a more pronounced adjustment of the T cell: B cell ratio within the draining lymph node than F2+SLA-SE. At present we can only speculate that these early differences contribute to dichotomous antigen-specific T cell responses elicited by each vaccine. Further evaluation of these early events and the cytokine profiles within the draining lymph node appear merited. Similarly, the relative contribution of either CD4 or CD8 T cells to protection against Leishmania infection could be delineated by further immune comparisons or use of gene-deficient mice.Our data demonstrate that although both RNA and subunit vaccines induced early changes in the cellular composition and activation state of the draining lymph node there were some distinguishing features. While F2+SLA-SE induced the majority of CD11cA restrictive feature of naked RNA vaccines to date has been their disappointing potency, indicating that efforts to enhance immunogenicity are needed. Similar to other reports examining mRNA, our data indicate that the F2 RNA replicon engages TLR7 to activate the innate immune responses , 39. ThiTogether, our results demonstrate the rapid production of an RNA vaccine and further indicate the utility of RNA vaccines for the promotion of CD4 T cell responses. This development process has great appeal for the rapid provision of vaccines that can protect against newly emerging pathogens that lack clear treatment or control strategies.MD, NH, JE, DS, and SR designed experiments and contributed to manuscript writing. ZM, RM, JE constructed RNA replicons. ZM, AP, MD, and F-CH performed immunization and immunoassay experiments. MD, NH, JE, and F-CH performed data analyses.Steven Reed and Malcolm Duthie are co-inventors on a patent for leishmaniasis vaccine development. All remaining authors declare no competing interests.