Whole-pelvis radiation therapy is common practice in the post-surgical treatment of cervical and endometrial cancer. Gastrointestinal mucositis is an adverse side effect of radiation therapy, and is a primary concern in patient management. We investigate whether proteomic information obtained from blood samples drawn from patients scheduled to receive radiation therapy for gynecological cancers could be used to predict which patients are most susceptible to radiation-induced gastrointestinal mucositis, in order to improve the individualization of radiation therapy.We use 132 proteins measured on 17 gynecological cancer patients in a convex-hull-based, selective-voting ensemble classifier to classify each patient into one of two classes: patients who would not (class 1) or would (class 2) develop gastrointestinal mucositis. We employ 20 repetitions of 10-fold cross-validation to measure classification accuracy.We achieved a 95% confidence interval on average prediction accuracy of  using pre-radiation proteomic profiles to predict which patients would experience gastrointestinal mucositis. Pathway analysis of the 12 most prominent proteins indicated that they could be assembled into a single interaction network with direct associations. The function associated with the highest number of these 12 proteins was cell-to-cell signaling and interaction.Pre-radiation proteomic profiles have the potential to classify cervical/endometrial cancer patients with high accuracy as to their susceptibility to gastrointestinal mucositis following radiation therapy. Further study of the network of 12 identified proteins is warranted with a larger patient sample to confirm that these proteins are predictive of gastrointestinal mucositis in this patient population. In the current management of cervical cancer, all patients classified as stage IIb and above receive chemoradiation therapy. Roughly 50% of patients who are IIa and below also receive radiation. In most circumstances the radiation is given to the whole pelvis. For treatment of endometrial cancer, patients with positive pelvic lymph nodes are generally given pelvic radiation. Gastrointestinal mucositis (GM) is a common adverse side effect of radiation therapy, and is a primary concern in patient management. In this study, we investigate whether proteomic information obtained from blood samples drawn from patients scheduled to receive radiation therapy for gynecological cancers could be used to predict which patients might be most susceptible to radiation-induced GM. Successful prediction would suggest that patient management could potentially be improved by augmenting the use of conventional prognostic criteria with proteomic profiles for improved individualization of radiation therapy.Advancements in biotechnology in recent years have increased the availability of high-dimensional data  for biomedical decision making. For such data to be informative for patient care, it must be transformed from simply a mass of raw data on each patient to a higher level of relevant information. Statistical learning techniques have been used to develop computational algorithms that can process such high-dimensional data to classify unknown tissue or blood samples through supervised training on samples of known class . The goaPreviously, we successfully used surface-enhanced laser desorption/ionization (SELDI) mass spectrometry (MS) to develop classification methods from patient serum with high sensitivity and specificity . In thisThe clinical study was approved by the Institutional Review Board of the University of Arkansas for Medical Sciences (UAMS) under Protocol Number 113376. The study population consisted of subjects eighteen years of age and older who were treated for gynecological cancers in the Women’s Oncology Clinic at UAMS, and who were candidates for whole-pelvis radiation therapy following surgery. Recruitment occurred over a three-year period. There were no exclusions based on racial or ethnic backgrounds. Because of the minimal risk associated with the blood draw, subjects were not excluded based on medical criteria. Informed consent was obtained from all participants. Each participant had one 7.5 ml blood sample taken either at initial consultation in the clinic or at the pre-op consultation. Treatment to the whole pelvis included 4500 cGy delivered to standard fields via a 4-field technique with the exception of one patient who received a 2-field technique . Brachytherapy was employed in about two-thirds of the patients, and about one-half had chemoradiation therapy.Study participants were required to complete three written questionnaires during the course of the study. These questionnaires asked for information on the frequency and severity of bowel movements, both before whole-pelvis radiation treatment began and subsequent to radiation treatment, to provide information for the diagnosis and grading of GM. The first questionnaire was completed when the subject consented to participate, the second approximately 4 to 8 weeks after radiation treatment began, and the third approximately 3 months after the second questionnaire. All three questionnaires were intended to be completed during regularly scheduled visits as part of the standard of care; however, in a few cases telephone follow-up was required for the third questionnaire. In all, twenty-three subjects were recruited to the study, of which seventeen completed all three GM questionnaires. GM was graded according to the NCI common terminology criteria for adverse events (CTCAE) phosphine (Pierce) and alkylation in 50 mM iodoacetamide (Sigma-Aldrich), by addition of 100 ng porcine trypsin (Promega) and incubation at 37°C for 12–16 hours. Peptide products were then acidified in 0.1% formic acid (Fluka). Tryptic peptides were separated by reverse phase Jupiter Proteo resin (Phenomenex) on a 100 × 0.075 mm column using a nanoAcquity UPLC system (Waters). Peptides were eluted using an 80 min gradient from 97:3 to 35:65 buffer A: B ratio. . Eluted peptides were ionized by electrospray (1.8 kV) followed by MS/MS analysis using collision induced dissociation on an LTQ Orbitrap Velos mass spectrometer (Thermo). MS data were acquired using the FTMS analyzer in profile mode at a resolution of 60,000 over a range of 375 to 1500 m/z. MS/MS data were acquired for the top 15 peaks from each MS scan using the ion trap analyzer in centroid mode and normal mass range with normalized collision energy of 35.0. Proteins are identified from MS/MS spectra by database searching using the Mascot search engine (Matrix Science) with a peptide mass tolerance of 2 ppm, fragment mass tolerance of ± 0.5 Da, a maximum of 2 missed tryptic cleavages, and fixed carbamidomethylation of cysteine modification and variable deamidation and oxidation modifications. The Mascot results are uploaded into Scaffold 4 (Proteome Software) for viewing the proteins and peptide information. A 1% FDR is used as the cutoff value, and spectral counts are exported into an Excel spreadsheet. Two independent LC-MS/MS analyses were performed for each depleted serum sample. Ingenuity Pathway Analysis [To develop predictors for the classification algorithm, the average of the two technical replicates was used to represent each protein in each sample. These averages were normalized according to the method of Byrum et al.  prior toThe selective-voting algorithm of Kodell et al. , with de132C2 = 8646 pairs in this case. From these 8646 pairs, the m (m = 100 by default) best pairs in terms of highest values of R2 in a two-variable regression model fitted to the training samples are retained. For each retained pair of proteins, the k sample points in the training set may be represented by a two-dimensional plot in which separate convex hulls are formed for class-1 and class-2 samples. These convex hulls are trimmed to achieve complete separation. Then the ensemble member represented by a protein pair is considered to cast a vote to classify a sample, whether training sample or test sample, in class j (j=1 or 2) if that sample falls within the trimmed convex hull for class j. The ensemble member abstains from voting on samples that fall outside both trimmed convex hulls. Thus, a member of the ensemble votes to classify each subject into one of the two classes, or can abstain from voting on selective subjects. A simple majority vote by the members who do vote determines a subject’s classification. This algorithm performs as well as, or better than, a number of well-known classification procedures [To implement the algorithm, all possible pairs of proteins are initially considered, i.e., n samples  is randomly divided into 10 subsets each having (approximately) n/10 samples. For each subset, a classifier is trained on the remaining observations (the training set), and the trained classifier is then used to classify the samples in the subset (the test set). The combined value of the prediction accuracy or other performance index over the 10 test sets is the cross-validated estimate of that index. Twenty repetitions of 10-fold CV were performed based on different random permutations of the 17 samples in the data set. Because n = 17, in each repetition there were seven subsets of two samples each and three subsets of one sample each.The cohort of seventeen subjects who completed all three GM questionnaires was used to assess the predictive ability of the proteomic profiles. The goal was to estimate the ability to predict the outcomes of samples not included in the dataset that was used to train the selective-voting ensemble. For this purpose, 10-fold cross-validation (CV) was employed. With 10-fold CV, a data set of th percentile of Student’s t-distribution with 19 degrees of freedom.A number of different indices may be used to assess a classifier’s performance. For problems having a dichotomous outcome variable like the present study, the sensitivity (SEN: proportion of correct predictions among true positives), specificity (SPC: proportion of correct predictions among true negatives), positive predictive value (PPV: proportion of correct predictions among positive predictions) and negative predictive value (NPV: proportion of correct predictions among negative predictions) are indices that may be of interest in addition to the prediction accuracy  . In the As a further check on the validity of our predictions, we randomly re-assigned GM class labels to the seventeen samples and ran another twenty cross-validations. As it turned out, eleven of the seventeen randomly assigned labels were correct (6 in class 1 and 5 in class 2) while six were incorrect (3 in class 1 and 3 in class 2). The average ACC over twenty CVs was only 0.394 (SD: 0.09). This poor accuracy is to be expected if the class labels are mixed up, and it gives assurance that our predictions are valid in the sense of reflecting truly predictive information found in the proteomic profiles .An interesting feature of the data was that the occurrence of GM was statistically associated with the date enrolled on the study. While only one of the first eight subjects to be enrolled experienced GM grade 1 or 2, seven of the last nine subjects enrolled experienced GM grade 1 or 2 . We could not identify any reason for this phenomenon. To our knowledge, nothing in our operating procedures changed during the course of the study. In our search for a possible explanation, we noted that there was a statistical association between BMI and the occurrence of GM. Eight of twelve subjects with BMI>30 had GM grade 1 or 2, while zero of five subjects with BMI<30 had GM grade 1 or 2 . This is counter to expectations; that is, ordinarily subjects with higher BMI are expected to be less prone to experiencing GM from radiation treatment.We could not establish a reason for the propensity for GM to be observed in subjects enrolled later in the study, other than the counter-intuitive positive association with BMI. To see what influence, if any, BMI might have on our protein-profile-based predictions, we added BMI as a predictor in the model, and we included age as well. The results of the predictions are given in 2 values in the regression phase of the selective-voting algorithm [From the CV results for the 132 proteins as predictors along with age and BMI , were higher than ours.  However, their investigation included only patients who were at the two extremes of toxicity as measured by the IBDQ-B score. They selected 23 extreme cases from a larger cohort in order to achieve good separation of classes, and thus to be able to establish proof of principle. Acknowledging the built-in bias of this approach toward overstated accuracy, they mentioned that in future work they would include patients between the two extremes. In our study, none of the subjects experienced extreme toxicity, i.e., no subjects had grade-3 or grade-4 GM. Thus, our two classes were not markedly separated, and yet we were able to achieve average accuracy greater than 70%.We note that Covington et al.  investigInterestingly, pathway analysis  of the 1Although limited because of sample size, the results indicate that selective voting in classification ensembles using proteomic profiles has the potential to classify cervical/endometrial cancer patients with high accuracy as to their susceptibility to the occurrence of GM following radiation therapy. However, we do not expect the MS-based method and the associated prediction algorithm to be used routinely for determining patient susceptibility if the biomarkers we have identified can be validated  in further studies. If we are successful in doing that, we envision that lab tests more relevant in clinical labs would be employed . Once a panel of biomarkers is validated, the sophisticated data processing methods of this paper would not be necessary. For clinical practice, the positive predictive value (PPV) and negative predictive value (NPV) may be of most interest, as they indicate the likelihood that a patient is actually in the predicted class. Further development of this approach could provide the radiation oncologist with a valuable tool to help increase the precision of radiation levels for these patients.A few subjects were left unclassified in the cross-validation runs due to tied votes among members of the ensemble. In practice, the prediction algorithm would not be applicable to such subjects, and they would need to be evaluated using different criteria. Fortunately, there were only three tied votes out of 340 attempts to classify when the algorithm used 132 proteins along with age and BMI as predictors, and only one tied vote when it used the 132 proteins alone.We conclude that pre-radiation proteomic profiles have the potential to classify cervical and endometrial cancer patients with high accuracy as to their susceptibility to gastrointestinal mucositis following radiation therapy. Further study of the network of 12 most prominent predictive proteins is warranted with a larger patient sample to confirm their predictivity of gastrointestinal mucositis in this patient population.Supplementary File 1Supplementary File 2


Currently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.http://liubioinfolab.org/targetS/mirna.htmlWe have developed TargetS, a novel computational approach for predicting miRNA targets with the target sites located along entire gene sequences, which permits finding additional targets that are not located in the 3' un-translated regions. Our model is based on both canonical seed matching and non-canonical seed pairing, which discovers targets that allow one bit GU wobble. It does not rely on evolutionary conservation, so it allows the detection of species-specific miRNA-mRNA interactions and makes it suitable for analyzing un-conserved gene sequences. To test the performance of our approach, we have imported the widely used benchmark dataset revealing fold-changes in protein production corresponding to each of the five selected microRNAs. Compared to well-known miRNA target prediction tools, including TargetScanS, PicTar and MicroT_CDS, our method yields the highest sensitivity, while achieving a comparable level of accuracy. Human miRNA target predictions using our computational approach are available online at A simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites. Our method also expands the target search space to different gene regions, rather than limiting to 3'UTR only. This improves the sensitivity of miRNA target identification, while achieving a comparable accuracy with existing methods. MicroRNAs (miRNAs) are endogenous approximately 22 nucleotide RNA molecules that play important gene-regulatory roles in plants and animals . These sIn the past decade, dozens of miRNA target prediction programs for mammalian genomes have been developed, including TargetScanS ,6-8, PicIn this article, we introduce a simple but powerful miRNA target prediction method that is solely based on canonical seed pairing and non-canonical seed matches. Our method does not require stringent seed pairing or evolutionary conservation in searching for human miRNA target sites. In addition, we perform our search on the entire gene sequence  rather than limiting the search space to the 3'UTRs only. We assessed our method based on a set of miRNA targets identified by the pSILAC method [The five types of seed matches used in our study are illustrated in Figure In CDSs, type 2t8 has the most significant signal-to-noise ratio, while the ratios for seed type 2t8 > 2t7A1 > 2t7, which is similar as those calculated for 3'UTRs. However, type 2t8 is more significant than 2t8A1 and 1t8GU is more significant than 2t7A1, deviated from what we have seen in 3'UTR. These results together demonstrate that the mechanism underlying miRNA target recognition and regulation in CDSs may be different from that in 3'UTRs.In 5'UTRs, type 1t8GU has the most significant signal-to-noise ratio, while the order of other types of seed matches is similar as that in CDSs. This indicates that the GU wobble pair may play a much more important role in 5'UTRs relative to its effects in other gene regions.For promoters, the order of the signal-to-noise ratios of four different canonical seed matches is similar to that in 3'UTRs, while 1t8GU type has the second highest ratio. Type 2t7 has the lowest ratio close to 1. These show that promoters are the least effective regions, but they cannot be ignored .A recent study has shown that miRNA binding sites in CDSs mediate smaller regulation than 3'UTRs binding, and there may be possible interactions between targets sites in CDSs and 3'UTRs . AnotherThe proportion of five types of seed matches in each of the four gene regions are given in Table I. For the 50 random shuffled mRNA sequences, the distributions of the seed matches are similar among different regions, whereas, the proportion of seed match type 2t8A1 in 3'UTRs is much higher than that in other regions, based on the miRWalk data. Since type 2t8A1 is the most rigorous seed match type, it suggests that miRNA targets in 3'UTRs have more selection pressure.To verify the robustness of our method, we applied it on an independent benchmark dataset obtained by the pSILAC method  and evalFigure It has been shown previously that evolutionary conservation of target sites is a very important feature for improving the accuracy of target identification. To evaluate the effect of this feature in miRNA target prediction, we simply imported the conservation score of different seed match types calculated by phastCons  and set http://www.r-project.org) with the positives (down regulated proteins in response to miRNA overexpression) and negatives (other proteins measured) identified in the pSILAC dataset. From the table we can see that the two most important features are In the past decade, machine learning methods have been widely used to predict miRNA targets ,21,36,37terature . These tWe compared the performance of TargetS with that of the Random Forest (RF) method. To evaluate the results, we first applied a widely used k-fold cross-validation (CV) approach on the pSILAC data. The original sample is randomly partitioned into k equal size subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k−1 subsamples are used as training data. The cross-validation process is then repeated k times, with each of the k subsamples being used exactly once as the validation data. The k results from the folds can then be averaged to produce an overall estimation. The result of 10-fold cross validation on the pSILAC dataset by RF is shown in the ROC curve Figure . With thMoreover, we note the performance of RF was evaluated based on the CV approach. The comparable performance of RF may be simply an artifact, due to the potential data overfitting effect caused by the CV. To better evaluate the performance of RF, we trained it using the experimentally verified miRNA-mRNA pairs in miRWalk as the positive training set, and the sequences generated by random shuffling as the negative training set. The trained model was then tested on the independent pSILAC dataset. The performance of RF decreased compared to other methods when an independent dataset was used for testing instead of performing CV on the same dataset Figure . The dishttp://liubioinfolab.org/targetS/mirna.html. With the advent of large-scale sequencing and new crosslinking methods, more direct information of miRNAs and their targets' regulation will be obtained. Together with the information obtained from reliable computational prediction methods, the mechanism of miRNAs and their roles in regulating different important biological processes and molecular pathways can be further investigated. We hope such mechanistic insights will help us understand the progression of different types of diseases, and will lead to novel therapeutic strategies associated with miRNAs and their targets' regulation.In this paper, we have proposed a simple and novel computational method for miRNA target prediction (TargetS), which searches for miRNA target sites in either the 3'UTRs, CDSs, 5'UTRs or promoters. As mentioned, our method does not rely on evolutionary conservation, thus allowing the detection of species-specific interactions and making it suitable for analyzing un-conserved genomic sequences. We also include a non-canonical seed pairing type, namely the GU wobble pair as an alternative targeting criterion. The comparison results of TargetS with other methods were based on the independent pSILAC dataset, indicating that TargetS finds a significantly larger number of true miRNA targets at an accuracy level comparable with TargetScanS, PicTar and MicroT_CDS. We have developed a web-based tool that can easily access the human miRNA target prediction results from our TargetS method, with the miRNA name and/or gene name as the input. The user-friendly website is now available at: miRBase: The mature miRNAs sequences are downloaded from miRBase database . There amiRWalk: This dataset hosts experimentally verified miRNA-mRNA interactions as well as the information of genes, pathways, organs, diseases, cell lines, OMIM disorders and literature on miRNAs . It inclhttp://psilac.mdc-berlin.depSILAC: A set of miRNA target genes identified by pSILAC (pulsed stable isotope labeling with amino acids in cell culture) method . It measThe sequences of the promoters, 5'UTRs, CDSs and 3'UTRs for each gene in human have been downloaded from the UCSC Genomes database  using thPreviously published methods ,19,23 haFor the first important feature, the types of canonical seed matches include 2t8A1 (requires Watson-Crick pairing to the 5' region of the miRNA on nucleotides 2 to 8 and the first nucleotide of target mRNAs being adenine), 2t8 (seed paring from position 2 to 8 in the 5' region of the miRNA), 2t7A1 (seed paring from position 2 to 7 with position 1 of target mRNA being adenine) and 2t7 (seed matches from position 2 to 7). However, many experimental results have shown that some 'non-seed' target sites such as single mismatches, GU wobbles, insertions or deletions in the seed-match regions are highly biologically functional as well ,24,31. SThe second important feature of targeting is thermodynamic stability. The binding energy between miRNA and the target mRNAs gained to form the miRNA-target duplex, pression . So we tNAhybrid . For eacWhen summarizing the free energy n 3'UTR) , we propWhere Where Then we set two cutoff values, The authors declare that they have no competing interests.WX and YL conceived of the idea and wrote the paper. WX performed the derivations, implemented the algorithm and prepared the data. FAS created the website and helped with writing the manuscript. ZW participated in the data analysis and results discussion. All authors read and approved the final manuscript.


Gymnandrosoma aurantianum sex pheromone in Central-Southern Brazil. Our analysis show that from 2001 to 2013 citrus growers avoided accumulated pest losses of 132.7 million to 1.32 billion USD in gross revenues, considering potential crop losses in the range of 5 to 50%. The area analyzed, 56,600 to 79,100 hectares of citrus (20.4 to 29.4 million trees), corresponds to 9.7 to 13.5% of the total area planted with citrus in the state of São Paulo. The data show a benefit-to-cost ratio of US$ 2,655 to US$ 26,548 per dollar spent on research with estimated yield loss prevented in the range of 5-50%, respectively. This study demonstrates that, in addition to the priceless benefits for the environment, sex pheromones are invaluable tools for growers as their use for monitoring populations allows rational and reduced use of insecticides, a win-win situation.Pheromone-baited traps have been widely used in integrated pest management programs, but their economic value for growers has never been reported.  We analyzed the economic benefits of long-term use of traps baited with the citrus fruit borer The discovery of bombykol as the sex pheromone of a domesticated insect species . This inThere are many ways in which pheromones can be used for surveillance and IPM programs, including monitoring, attract-and-kill, and mating disruption. Pheromone-baited traps are sufficiently sensitive to detect low population densities and are therefore an effective way for tracking invasive species while they are still at the establishment stage . PopulatCydia nigricana  is a representative case for analysis of the economic benefits achieved by the use of a synthetic pheromone to manage this pest in extensive areas. Brazil is the leading worldwide producer of citrus -8-dodecenyl acetate and (E)-8-dodecenol, was identified by members of our group in early 2000’s  corresponding to total production (in boxes of oranges) that growers avoided losing by using traps baited with the sex pheromone of the citrus fruit borerG. aurantianum, in the monitored areas in the state of São Paulo, Brazil.The economic analysis covered the period from November 2001 to December 2013. Monetary results were calculated as losses avoided, i.e., the amount of gross revenues (in US$) corresponding to total production (in boxes of oranges) whose loss was prevented by using traps baited with pheromone of the citrus fruit borerData on the number of citrus trees in the state of São Paulo and their average yield (boxes/tree) were obtained from the Agricultural Economics Institute (IEA) . To calcG. aurantianum pheromone sold between November 2001 and December 2013, as well as their prices (in US$) were obtained from the Coopercitrus, the only entity responsible for their distribution in the entire state of São Paulo, Brazil. Each year (2001–2013),G. aurantianum was monitored during the citrus harvesting season (~ 6 months). According toThe number of traps baited withG. aurantianum in the period from 2001 to 2013 ranged from US$132.7 million to US$1.32 billion in gross revenues. In other words, this was the estimated aggregate total of gross revenues from the sale of oranges that growers avoided losing by using pheromone-baited traps, considering a 5–50% range of potential losses caused by citrus fruit borer infestation in citrus orchards in the state of São Paulo . It should be noted that some costs were not measured in this study, such as labor costs of the inspections that used to be performed before the traps became available, and the fact that insecticide spraying was once triggered by a 3–5% yield loss caused by the caterpillars ofntianum . TherefoG. aurantianum in citrus orchards in the state of São Paulo.The initial investment in the research that resulted in the development of pheromone-baited trap was US$ 50,000. Therefore, in terms of the governmental costs, the benefit-cost ratio of the initial investment  ranged from US$ 2,655 to 26,548 per dollar spent with a yield loss of 5–50%, respectively . In termG. aurantianum only became available on the market in November, the area monitored during the 12 subsequent years (2002–2013) ranged from 56,600 to 79,100 hectares of citrus (20.4 to 29.4 million trees), corresponding to 9.7 to 13.5% of the area planted with citrus in the state of São Paulo, the main producing region in Brazil.Except for the year 2001, when the sex pheromone ofThese findings reveal a regularity in the sale and use of pheromone-baited traps by citrus growers during that period (2002–2013). Trap sales were relatively stable in that period, with 38,166 units sold per year on average, ranging from 31,970 units (2010) to 47,436 units (2007), possibly due to fluctuations in international prices of orange juice, the main product exported by the Brazilian citrus industry. This regularity suggests a good level of acceptance and application of the technology by growers, and certainly a benefit obtained from its use.It worth mentioning that, according toRaw data for Figure 1 and Figure 2Dataset: data used for economic analysis  covered Click here for additional data file.Copyright: © 2016 Bento JMS et al.2016Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Bento JMS et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).10.5256/f1000research.9195.d129239  in crop and environmental protection.The research methodology is adequate. The sample size is adequately representative .The use of an action level data based on capture with pheromone-baited traps is sufficiently documented/ well exemplified.data analysis is based on benefit-cost analysis applied to the citrus industry in the period from 2001 to 2013 in the state of São Paulo, based on gross revenues (in US$) corresponding to total production (in boxes of oranges) that growers avoided losing by using traps baited with the sex pheromone of the citrus fruit borerG. aurantianum in the monitored areas. Whereas the economic value of the pheromone is adequately demonstrated, and the treatment of data, while generally sound, it could be further enhanced by the following considerations:The main thrust of the papercost of technology delivery apart from initial research costs is often quite considerable. The authors should make an attempt to estimate these, although understandably these are a complex mix of quantifiable and qualitative values that are not easy to calibrate accurately. 4.1. In the calculation of the benefit-cost ratio of the initial investment, i.e. present value of losses avoided/total investment, the reviewer’s considered opinion is that the prices paid for purchase of the traps and the initial amount invested in research to develop the technology  may not adequately define total costs. The authors correctly mention they excluded labor and indirect intellectual investments – which could be considerable. The 4.2. In terms of costs avoided, it would also be instructive to attempt to compute all the previous direct and ancillary costs e.g. labor costs of the inspections that used to be performed before the traps became available, in addition to the directly attributed (avoided) costs of previous chemical control.price calculations are based on the average monthly price published by the Center for Advanced Studies on Applied Economics (Cepea) , corresponding to the average amounts in US$ paid to citrus growers per box, on credit. In this calculation, price stability is assumed. The authors need to mention if they took account or how they treated supply Vs demand pressure on price, or any subjectively negotiated discounts on price? 4.3. Data on annual average yield is adequately treated. However, The accuracy of monetary values is in part, but importantly, dependent on the accurate treatment of price data. On the pivotal question of price, the reviewer considered the following questions: - Is the average price decomposed into the different contributing variables? - What were the relative contributions of each monetary variable to the aggregated components? - What was the price elasticity? - to what extent did the assigned exchange rate affected the overall pricing? - how stable was the exchange rate over the entire reference period? 4.4. In the overall analysis it would be good to capture the computation of price in an equation that shows the relative contribution of each monetary variable to the aggregated components since the monetary variables were derived from multiple independent (assumed non-synchronized) sources e.g. cepea), PTAX), etc. 4.5 Secondly, in transforming nominal price data into real values using the General Price Index – Internal Availability (IGP-DI) the authors need to discuss the price elasticity and sensitivity to each contributing variable. 4.6 Thirdly, still on the question of pricing, monetary variables were updated to values applicable in June 2014, and the final month’s PITAX exchange rate used. The authors need to demonstrate to what extent did the assigned exchange rate affected the overall pricing, and explain how stable the exchange rate had been for the entire reference period.Gymnandrosoma aurantianum sex pheromone. Globally, in the determination of the broader public goods the overall discussion of “how much is a pheromone worth?” could be further enriched if the authors also discussed in greater detail its:The authors have made an excellent attempt to compute the actual economic  value of the the a) Functional value? In terms of environmental ecosystem services provided in addition to economic value? b) Evolutionary value of sex pheromones? Maintenance of ecological balance though managed species populations?Conclusion: The paper is sufficient for indexation, with minor revisions addressing the concerns above.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Gymnamdrosoma aurantianum between 2001 and 2013.  The principle savings was in a  50% reduction in insecticide application accompanied by significantly lower fruit damage. This is the first time the economic analysis of long term pheromone trap monitoring to better time insecticide application has been conducted.  Because manpower costs of monitoring and spraying are not included, and because spraying uses much more manpower than monitoring, the actual additional income to citrus growers using the monitoring program is very likely more than estimated in this paper. The information in this paper is of significant interest to managers of agricultural enterprises.This paper analyzes the monetary benefits accruing to citrus growers adopting a 1 trap / 10 ha pheromone trap monitoring program for citrus fruit borerI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


ST -like measure for epigenetics (termed epi- FST), in order to develop a deeper understanding of the evolutionary pressures acting on epigenetic variation. Results demonstrate that genes with high epigenetic FST values are indeed significantly overrepresented among genes that are differentially expressed between species, and we observe only a weak correlation with SNP density.Epigenetic modifications are alterations that regulate gene expression without modifying the underlying DNA sequence. DNA methylation and histone modifications, for example, are capable of spatial and temporal regulation of expression—with several studies demonstrating that these epigenetic marks are heritable. Thus, like DNA sequence, epigenetic marks are capable of storing information and passing it from one generation to the next. Because the epigenome is dynamic and epigenetic modifications can respond to external environmental stimuli, such changes may play an important role in adaptive evolution. While recent studies provide strong evidence for species-specific signatures of epigenetic marks, little is known about the mechanisms by which such modifications evolve. In order to address this question, we analyze the genome wide distribution of an epigenetic histone mark (H3K4me3) in prefrontal cortex neurons of humans, chimps and rhesus macaques. We develop a novel statistical framework to quantify within- and between-species variation in histone methylation patterns, using an ANOVA-based method and defining an F Elucidating the relationship between genotypes and phenotypes remains an important challenge. One important question that remains in order to make further progress on this front is the ability to quantify why cells in different tissues, despite having the same DNA sequence, express different genes and maintain their distinct cellular identities. One of the key processes used to explain this paradox is epigenetics. Epigenetic modifications  regulate and alter the expression of genes without altering the underlying DNA sequence Bird, . Thus, tOne particularly interesting aspect of these modifications is their ability to escape reprogramming during gametogenesis and embryogenesis, and thus be propagated from parents to offspring . TSS data for humans (hg19) was downloaded from the Ensembl database (http://www.ensembl.org/). Only TSSs for protein coding genes were retained for further analysis. Liftover (http://hgdownload.cse.ucsc.edu/admin/exe/) was used to obtain the TSS coordinates for chimps (panTro2) and macaques (rheMac2) from the human TSS data. Data for the gene expression differences between humans, chimps, and macaques was taken from Cain et al.  can be partitioned into the between-species and within-species components Table :ST -like measure for epigenetics based on analogy with AMOVA . Trimmed illumina paired-end RNA-seq reads were mapped to the human genome hg19 using tophat2 .Quality control of the paired-end illumina RNA-seq reads was performed using FastQC  TSSs from Ensembl biomart, and limited our analysis to protein-coding genes only. Liftover was used to obtain the coordinates of the TSSs in chimps and macaques. As the peaks were obtained by mapping the ChIP seq reads to the human genome, we used Liftover to obtain the peak coordinates in the chimp and macaque genomes. Previous studies have shown that there is a strong overlap in H3K4me3 associated regions between these species .Over the whole-genome H3K4me3 peak data, epi- Fe Figure . We founp < 2.2e-16). This correlation suggests that the presence of SNPs may indeed have an impact on the enrichment/binding of the histone mark—though it cannot be ruled out that both are being impacted by a third biological correlate.To further investigate the relationship between sequence variation and epigenetic variation, we performed a sliding window analysis (using a window size of 1 Mb and sliding 100 kb at a time) for each chromosome and compared the SNP densities in each window with the average peak density and number of histone peaks per window. We observed weak but significant correlations between the number of SNPs with (1) the number of histone peaks and (2) the average peak density . In white matter, no significant overrepresentation was found when considering the top 500 genes with the highest variance (p = 0.42). However, the peaks were called from PFC neurons , which may explain the absence of significant results from white matter.We identified 404 genes as being differentially expressed in gray matter and 526 genes as being differentially expressed in white matter within humans. Significance of differential expression between any pair of individuals was determined based on ST values for genes correlate with differences in gene expression between humans and chimps, using the data for differential gene expression between human and chimps published in Cain et al.  of differentially expressed genes among those with the highest epi- FST. Therefore, epigenetic divergence in H3K4me3 enrichment may explain a fraction of gene expression differences that we observe between species. It is important to note that we review only one histone mark in this study (H3K4me3); in order to capture the full extent of how epigenetic divergence correlates to differences in gene expression between species, it would be helpful to consider several different epigenetic marks. For example, H3K4me3 is the methylation state associated with transcriptional start sites of actively transcribed genes—and further epi- FST comparisons between transcriptional activation and transcriptional repression marks would be of interest in beginning to quantify differences in pressures. Additionally, it would be of great value in future studies to have paired methylation marks and expression data from identical individuals.We next evaluated whether our H3K4me3 epigenetic Fn et al. . A totalWe developed here a simple model to quantify epigenetic variation—studying the variation in H3K4me3 enrichment using PCA and a newly developed ANOVA-based framework to quantify within- and between-species variation. Differences in H3K4me3 are shown to be correlated with differences in gene expression both within humans and between humans and chimps. Moreover, we observe only a weak correlation between peak density and SNP density. These two results, combined with the increasing evidence for the heritability of epigenetic marks, suggests the potentially important role of epigenetic variation in adaptive evolution. Interestingly, we also found evidence that these marks evolve in a clock-like fashion based on pair-wise distances between species generated from a PCA—though wider species comparisons will be necessary to further evaluate this hypothesis. Thus, these results present an important first step toward quantifying within and between epigenetic variation in the context of a standard population genetic framework, enabling for standardized genomic scan and comparative evaluations of the relative contributions of genetic and epigenetic variation in the adaptive process.The 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.


AGO1a in ago18 background rescues or increases rice antiviral activity. Notably, stable transgenic expression of AGO18 confers broad-spectrum virus resistance in rice. Our findings uncover a novel cooperative antiviral activity of two distinct AGO proteins and suggest a new strategy for the control of viral diseases in rice.Viral pathogens are a major threat to rice production worldwide. Although RNA interference (RNAi) is known to mediate antiviral immunity in plant and animal models, the mechanism of antiviral RNAi in rice and other economically important crops is poorly understood. Here, we report that rice resistance to evolutionarily diverse viruses requires Argonaute18 (AGO18). Genetic studies reveal that the antiviral function of AGO18 depends on its activity to sequester microRNA168 (miR168) to alleviate repression of rice AGO1 essential for antiviral RNAi. Expression of miR168-resistant DOI:http://dx.doi.org/10.7554/eLife.05733.001 Rice is a major food crop, providing over a fifth of all calories consumed by people around the world. As such, it is important to find ways to prevent the diseases that affect rice plants. Many of the viruses that infect rice are transferred between plants by insects and many insects carry more than one virus at a time; this means it can be difficult to predict where a disease will next emerge. As a result, there is a pressing need to develop new and effective strategies that boost the ability of rice plants to fight off harmful viruses.One way that plants defend themselves from viruses involves using a system called RNA interference to identify and destroy the RNA molecules that viruses produce. This process depends on the Argonaute (AGO) family of proteins, although the roles of many of its members are not well understood. One of the better-studied AGO proteins is called AGO1 and is known to be important for defending plants against viruses. Unfortunately, a small RNA molecule called miR168 acts to limit the amount of AGO1 in a cell, and the levels of miR168 increase in virus-infected rice plants.Wu, Yang et al. exposed rice plants to two species of insect that each carried a different plant virus. Rice plants infected with these viruses increased their levels of both AGO1 and another AGO protein called AGO18. Modifying the ability of rice plants to produce AGO18 revealed that the anti-viral activity of AGO1 is abolished in plants lacking AGO18. However, plants that over-produce AGO18 are better able to fight off viral infections. Wu, Yang et al. further showed that AGO18 binds to miR168 and so prevents this small RNA from reducing AGO1 levels. Therefore, AGO1 and AGO18 work together to defend rice plants from viruses.Wu, Yang et al. suggest that engineering rice plants to make more AGO18 could make them more resistant to viruses. Further work will be needed to confirm whether AGO1 and AGO18 also work together to defend rice against viruses other than the two tested so far and to investigate whether these proteins also perform similar roles in other crops.DOI:http://dx.doi.org/10.7554/eLife.05733.002 Small RNAs including small interfering RNAs (siRNAs) and microRNAs (miRNAs) are important regulators of gene expression via RNA interference (RNAi) pathways in many organisms . The RNAArabidopsis thaliana encodes four DCLs that function indistinct and yet overlapping RNAi pathways to control diverse biological processes ranging from development, response to abiotic stresses, to defense against pathogens  is one of the most important food crops as well as an experimental model plant for monocotyledonous plants (monocots). However, rice production and consequently food sustainability is under the constant threat of emerging and reemerging viral diseases. Rice viral pathogens are genetically diverse and many highly pathogenic viruses such as Rice stripe Tenuivirus  and Rice dwarf Phytoreovirus  are transmitted persistently and solely by arthropod vectors (Rice ( vectors . BecauseLaodelphax striatellus) and leafhopper (Nephotettix cincticeps) that transmit RSV and RDV, respectively. Intriguingly, we found that AGO18, a member of a new AGO clade that is conserved in monocots, is specifically induced by the infection of two taxonomically different viruses and is required for the antiviral function of AGO1. Loss-of-function ago18 mutation abolishes, whereas over-expression of AGO18 increases, the AGO1 antiviral activity. We further demonstrated that AGO18 competes with AGO1 for binding miR168, resulting in elevated levels of AGO1 in the infected plants to enable antiviral defense. Expression of an miR168-resistant AGO1a variant in the ago18 background rescues or increases rice antiviral activity. The antiviral function of AGO18 requires its small RNA-binding, not slicing activity. Our findings reveal a novel mechanism AGO1 homeostasis regulation by AGO18 for antiviral defense and have significant implications in understanding the evolutionary amplification of RNA silencing mechanisms and in developing novel antiviral strategies.The rice genome encodes five DCLs and 19 AGOs. How different AGOs regulate antiviral RNAi in rice is not known. In this study, we report a critical role of RNAi in antiviral defense in rice under natural infection conditions, when the plants were inoculated, as in the field, by viruliferous insect vectors brown planthopper  under the control of AGO18 promoter. RSV infection led to high levels of GUS expression in such plants as compared to the background levels in mock-inoculated plants , respectively    from thes plants . The agoT plants . Viral rt plants . These rof AGO18 .Phytoreovirus -northern blot analyses further confirmed that RSV infection of WT rice plants increased the association of miR168 with AGO18 but decreased its association with AGO1a and 1b, whereas several control miRNAs (including miR166 and miR156) showed no obvious changes . Consista and 1b , which w at mRNA  and prot at mRNA  levels. NA level , likely NA level , AGO2 wae plants . Intriguof AGO18 . Thus, AN. benthamiana leaves. MiR444 was used as a control, because it is a species-specific miRNA in monocots that can be loaded by AGO1  and AGO18D833A (D833A). YF/AA contains alanine substitutions at two conserved residues (Y537F538) that are known to be required for small RNA binding  in the ago18 background, under the control of the native AGO1a promoter (AGO1a/AGO1a-Res mRNA levels of both AGO1a and AGO1a-Res transgenes accumulated to higher levels relative to the AGO1a levels in wild-type (WT) non-transgenic and ago18 plants  and miR168-resistant promoter . The ste8 plants . It is nnfection , indicata plants . Viral ra plants . Thus, ABased on the above results, we conclude that AGO18 regulates AGO1 homeostasis by specifically loading miR168 during viral infection. Sequestering of miR168 by AGO18 leads to increased accumulation of AGO1 to embark on an effective antiviral defense response.Arabidopsis  seedlings were grown in a greenhouse at 28–30°C and 60 ± 5% relative humidity under natural sunlight for 4 weeks. The viruliferous (RSV and RDV-carrying) insects of L. striatellus and N. cincticeps, as well as virus-free L. striatellus and N. cincticeps (mock) were used for inoculation. After feeding 3 days, the insects were removed, and the rice seedlings were returned to the greenhouse to grow under the greenhouse conditions above. 3 weeks post-inoculation when the newly developed leaves started to exhibit viral symptoms, the whole seedlings were harvested. For each sample, at least 15–20 rice seedlings were pooled for RNA extraction.Plant growth and virus inoculation were essentially carried out as described . BrieflyL. striatellus) and leafhopper (N. cincticeps). Details of the procedures were described previously , 10 mM EDTA, and 0.5 mg/ml X-gluc , followed by incubation at 37°C in the dark overnight and then destained in 70% ethanol before photographing.Synthetic peptides AGO1aN (KKKTEPRNAGEC), AGO1bN (KKRTGSGSTGEC), and AGO18N (YHGDGERGYGRC) were used to raise rabbit polyclonal antibodies against AGO1a, AGO1b, and AGO18, respectively, essentially as described . The antN. benthamiana and stable rice transformation. The binary gateway vector pMDC32 (p32:pAGO18) or AGO1a promoter (p32:pAGO1a). Most cDNA and miRNA genes were cloned into pENTR/D vectors and pENTR/D-Flag-AGO18D833A, YF/AA and AGO1a:miR168 Res clones were prepared by using the QuikChange site-directed mutagenesis kit . All these clones were confirmed by sequencing and transferred to the appropriate destination vectors by recombination using the Gateway LR Clonase II Enzyme mix (Invitrogen). pCam2300:Actin1::OCS and pCam2300:35S::OCS vectors  was used to make binary constructs. Several destination vectors were created for transient expression in r pMDC32  was modi vectors  were useN. benthamiana were performed as described , FLAG , and tubulin .Rice AGO-containing complexes were immunopurified from RSV-inoculated rice plants as previously described . The quaSmall RNA cloning for Illumina sequencing was carried out essentially as described previously . A detai32P-end labeled oligonucleotide probes complementary to sRNAs were used for Northern blots. The sequences of the probes are listed in Northern blot analysis with total sRNAs or from purified AGO complexes was performed as described before . 32P-endOsEF-1a gene was detected in parallel and used as the internal control. All the other primers used are listed in Total RNAs were extracted from rice plants with Trizol (Invitrogen). After removal of contaminating DNAs by digestion with RNase-free DNaseI , the RNAs were reverse transcribed by SuperScript III Reverse Transcriptase (Invitrogen) using oligo (dT). The cDNAs were then used as templates for quantitative PCR and RT-PCR. Quantitative PCR was performed using SYBR Green Real-time PCR Master Mix . The rice http://rice.plantbiology.msu.edu/, version 6.0). The sRNAs with perfect genomic matches were used for further analysis. Rice miRNA annotations were from miRBase  and our previous publication  under accession number PRJNA273330.Small RNA data sets generated in this study are deposited in the NCBI sequence read archive (SRA) ( review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the decision was reached after discussions between the reviewers. We regret to inform you that your work will not be considered further for publication.Thank you for choosing to send your work entitled “Antiviral RNAi requires a novel cooperative activity of two distinct Argonaute proteins” for consideration at All three reviewers have major but non-overlapping concerns about the manuscript. The major reservations are that:1) The concept described overlaps with the previously described AGO1-AGO10 competition;2) The study is incomplete. Some obvious experiments have not been done include (but are not restricted to) the use of miR168 resistant AGO1 mRNA and analysis of AGO2;3) The fact that all RNAi lines  have the same phenotype, raising the possibility that there is interference of transgene RNAi and antiviral RNAi.Reviewer #1:The paper confirms that RNA silencing mediates defense against RSV and RDV delivered by insect vectors. Overall these are nicely executed experiments although I would say that the obvious and most direct test of the hypothesis with a miR168 resistant AGO1 message is conspicuously absent. The data also do not test the prediction that AGO18 induction in virus-infected plants would mediate overexpression of AGO1 and that the timing of the effect would precede the maximal rate of virus accumulation. The finding that the plant AGO-mediated antiviral silencing can be enhanced in virus-infected plants is not new and the decoy AGO mechanism is also not novel although it was described previously in a different context. Taking the sum of these considerations my view is that the paper in its present form is more suited to a specialist journal.eLife. It would be interesting to know for example about the features of AGO18 that differentiate it from other AGO clades and to know more about the regulation of AGO18 in virus-infected plants. Specific questions would concern the role of insect vectors in the activation of this gene and the possibility  required for antiviral activity? If so, the results obtained during AGO1 RNAi are also questionable.In addition, the results obtained with the Reviewer #3:Small RNAs (sRNAs) including miRNAs and siRNAs are a group of non-coding RNAs that regulate expression of complementary target genes. sRNAs are loaded into AGO-centered RNA inducing silencing complexes (RISCs) and guide AGO to cleave their targets. It is well-known that plants, and possibly other organisms as well, utilize RNAi system to defense themselves from virus propagation. On the other hand, viruses encode viral suppressors to block different steps of posttranscriptional gene silencing pathway (i.e. destabilizing and inhibiting AGO proteins) to counter defense the host responses. It has been also shown that some viruses evolve a mechanism that induces expression of endogenous miR168 that happens to target AGO1 effector itself, leading to suppression of RNAi so that viruses can survive.In this paper, Wu and collaborators studied siRNA responses of plants to the RSV infection, the authors took advantage of the RSV/RDV–Oryza sativa patho-systems. These RNA viruses infect rice by a planthopper vector and establish infection by overcoming the AGO1-mediated antiviral siRNA response in the plant. Nonetheless, the sRNA machinery is highly diversified in rice, encoding 5 DCL and 18 AGO proteins, most of which have still unknown functions. Importantly, the authors opted to use the natural insect vector of the viruses in the inoculation of the plants to mimic the natural infection conditions and be able to discern important effectors in the sRNA mediated antiviral defense. They found that one AGO protein, AGO18, is specifically upregulated by virus infection and also essential for antiviral immunity. They then tried to explore the mechanism underlying AGO18 function. They found that AGO18 bound less viral-derived siRNAs compared to canonical AGO1, leading to their proposal that AGO18 is unlikely an effector protein for viruses. Because they previously published a paper that the viral infection elevate the expression of both miR168 and AGO18, they hypothesized there might be come connection between the two events. They performed a deep-seq and found that AGO18 bound a large amount of miR168 (but not the largest group), and therefore proposed a model and evidence that Ago18 maintains AGO1 level by sequestering miR168 as an antiviral strategy.Overall, the manuscript is interesting and presented lots of nice data, most of which are likely solid. It will advance the field, especially, understanding of the arms race between hosts and pathogens. This manuscript is amazingly an extension of a previous story that AGO10 specifically sequesters miR165/166 to antagonize the silencing activity of AGO1 . Cell. H1) The authors should integrate their work in the literature context. Starting from the Introduction of well-studied AGO functions, the authors selectively emphasized their own work and ignored other elegant work . Another example is that the mechanism presented here is essentially the same as the AGO10-AGO1 story . The onl2) The manuscript missed one piece of important information: could sequestering of miR168 under AGO18 promoter through miRNA sponge or target mimicry rescue ago18 responses to viral infection?3) In AGO1b or ∼3-fold AGO1a under virus infection. Given that miR528 is an important metabolic regulator during embryo development in rice, it seems very interesting to discuss its possible relationship to virus defense and whether AGO18 acts also as a decoy and not a silencing effector with this miRNA.4) In the 5) In the Discussion part, the authors attempted to differentiate this finding from the AGO10-AGO1 weakens, instead of strengthening, their discussion, for having evidence of this phenomenon used not only for development but as an adaptive mechanism for viral defense in a distant organism as rice, makes it ubiquitous and conserved.eLife. Your revised article has been favorably evaluated by Detlef Weigel (Senior editor) and three reviewers, one of whom, David Baulcombe, is a member of the Board of Reviewing Editors. The manuscript has been improved but there are some remaining minor issues that need to be addressed before final acceptance, as outlined below:Thank you for resubmitting your work entitled “Viral-Inducible Argonaute18 Confers Broad-Spectrum Virus Resistance in Rice by Sequestering A Host MicroRNA” for further consideration at dcl mutant analysis as the interpretation is not conclusive and the data are not required for the interpretation of the AGO18 analysis.1) We strongly recommended removal of the 2) We recommend a brief discussion of the follow points:a) What is the proposed fate of the AGO18 bound miR168?b) What is the sensing mechanism of viral RNA that triggers AGO18 expression? [Editors’ note: the author responses to the first round of peer review follow.]All three reviewers have major but non-overlapping concerns about the manuscript. The major reservations are that:1) The concept described overlaps with the previously described AGO1-AGO10 competition.Arabidopsis. This competition, however, does not affect AGO1 homeostasis. In contrast to these negative regulations of AGO1 activities, binding of miR168 by AGO18 in infected rice plants directly boosts AGO1 accumulation. Thus, among mechanisms of AGO-regulation of AGO1 homeostasis or activity, the up-regulation of AGO1 via AGO18 binding of miR168 represents a novel type of mechanism. This opens up the question of whether other AGOs function similarly in Arabidopsis, rice and other plants to up-regulate AGO1 or another AGO to impact developmental processes or defense responses.We feel there is a major difference between our finding and previous findings on AGO1-AGO10 competition to make it novel and interesting. As discussed in the Results section, indeed there were previous examples of competitive binding of other miRNAs between other AGOs and AGO1 in 2) The study is incomplete. Some obvious experiments have not been done include (but are not restricted to) the use of miR168 resistant AGO1 mRNA and analysis of AGO2.AGO1a gene rescued the deficiency of ago18 for viral resistance. The results are presented as a new section  The fact that all RNAi lines  have the same phenotype, raising the possibility that there is interference of transgene RNAi and antiviral RNAi.pC2 (a glycoprotein) and p4 . In the pC2 and p4 RNAi plants, siRNAs accumulated at similar levels as those in the dcl RNAi lines  that there is a systemic signal associated with its induction. We know about signals induced in disease resistant plants but this would be a signal in susceptible plants. Is it an RNA or perhaps it is a JA response induced by the insect vectors of the virus?Our mock-inoculation experiments actually addressed this concern, which we should have explained in the original manuscript. Mock-inoculation with virus-free insect vectors had no effect on AGO18 expression, essentially eliminating the possibility that insect biting  in triggering AGO18 expression. We have discussed this in the main text.Reviewer #2:In this manuscript, Wu et al., show that AGO18, a monocot-specific member of the ARGONAUTE family, is induced during virus infection and preferentially binds to miR168, a miRNA that normally down-regulates AGO1, which has antiviral RNAi activity. Thus, AGO18 induction titrates miR168, leading to up-regulation of AGO1 and increased resistance to viruses. The data are convincing and support the authors' conclusions.However, the authors totally ignore the contribution of AGO2, both in the bibliographic introduction and in the development of their story. Indeed, at least five papers have shown that, in addition to AGO1, AGO2 contributes to antiviral RNAi activity . None of these papers is referenced in the Introduction and only a brief sentence in the Discussion says that, in addition to AGO1, AGO2 also has catalytic activity against viral RNA, but there is no reference cited.We have added and discuss the literature on AGO2 in the main text.The absence of investigation on AGO2 in this paper is a big concern, considering that the authors have previously shown that AGO2 is induced during virus infection, and actually more induced than AGO1 . Could AGO18 induction also lead to AGO2 up-regulation, and by which mechanism? This question needs to be addressed.We have performed new experiments to show that AGO18 specifically induces AGO1 but not AGO2 .In addition, the results obtained with the dcl RNAi lines are puzzling and actually raise a number of concerns regarding the RNAi strategy. Indeed, RNAi against either DCL1, DCL2, DCL3a, DCL3b or DCL4 increases infection rates. How is this possible, given the different role of each DCL? Isn't it that the production of very large amounts of dsRNA during RNAi somehow titrates the DCL(s) required for antiviral activity? If so, the results obtained during AGO1 RNAi are also questionable.Arabidopsis showed that DCL4 and DCL2 (in the absence of DCL4) are involved in antiviral defense  The authors should integrate their work in the literature context. Starting from the Introduction of well-studied AGO functions, the authors selectively emphasized their own work and ignored other elegant work . Another example is that the mechanism presented here is essentially the same as the AGO10-AGO1 story . The only difference is AGO18-AGO1 for miR168 (which happens to feedback regulate AGO1). The way the authors wrote the manuscript suggests that their discovery is completely novel, if one does not go deep through the Discussion part.As we responded to a similar comment from reviewer #1 above, we have discussed our findings in the context of previous findings in the main text. We believe our finding adds a new and important layer regulatory mechanism of AGO functions that is of high and broad interest.2) The manuscript missed one piece of important information: could sequestering of miR168 under AGO18 promoter through miRNA sponge or target mimicry rescue ago18 responses to viral infection?ago18 mutant; and 4) our new data showing that the expression of miR168-resistant AGO1a rescued the deficiency of ago18 for viral resistance  AGO18 and AGO1 are both induced by RSV infection ; 2) AGO1sistance .3) Init is apparent an increase in AGO18 levels upon RSV infection, especially in line #5. This is unexpected given that the gene is under a constitutive promoter. Is it possible that the virus infection affects the protein stability? It is known that AGO proteins are unstable in the apo form (this is without sRNA bound), could it be possible that virus miR168 induction regulates AGO18 stability? The authors could address this issue by checking transcript and protein levels of AGO18 in OE lines before and after virus infection. Is the concurrent increase of AGO18 and miR168 upon viral infection synergetic effect?We apologize for this confusion. We have repeated twice the Western blot experiments described in 4) In the, the highest accumulation of miRNA in AGO18 complexes is of miR528, which is more than 9-fold higher than in AGO1b or ∼3-fold AGO1a under virus infection. Given that miR528 is an important metabolic regulator during embryo development in rice, it seems very interesting to discuss its possible relationship to virus defense and whether AGO18 acts also as a decoy and not a silencing effector with this miRNA.We have been discussed this in the main text.5) In the Discussion part, the authors attempted to differentiate this finding from the AGO10-AGO1 weakens, instead of strengthening, their discussion, for having evidence of this phenomenon used not only for development but as an adaptive mechanism for viral defense in a distant organism as rice, makes it ubiquitous and conserved.As described above, we have revised our discussions to clarify how our findings add a new layer of regulatory mechanism to AGO functions.We appreciate the favorable comments made by the Senior and Reviewing editors. In the revised manuscript, we have provided additional data or explanations to address some minor concerns raised by the two editors. We wish the revisions are sufficient and the manuscript is now acceptable for publication. Point-by-point responses are listed below.1) We strongly recommended removal of the dcl mutant analysis as the interpretation is not conclusive and the data are not required for the interpretation of the AGO18 analysis.dcl mutant analyses.As recommended, we have removed the data and text about the 2) We recommend a brief discussion of the follow points:a) What is the proposed fate of the AGO18 bound miR168?AGO1 mRNA in RSV-infected rice plants […]. It will be highly interesting to test these possibilities in future studies.”We have added the following paragraph in the Discussion: “Our findings suggest that AGO18-bound miR168 are not competent in cleaving b) What is the sensing mechanism of viral RNA that triggers AGO18 expression?After submitting the manuscript, we obtained new data to address this question. We examined the possibility that viral proteins produced during infection induce AGO18 expression. To do this, we generated transgenic rice plants that over-express Myc-tagged RSV P2, CP, pC4 and P4 proteins, respectively. We found that AGO18 was induced only in the transgenic lines that over-expressed CP, but not in those that over-expressed three other viral proteins. This suggests that RSV CP is an effector to induce AGO18 expression during viral infection. These new results are included in a new supplemental figure .


The extracellular matrix (ECM) plays an instrumental role in determining the spatial orientation of epithelial polarity and the formation of lumens in glandular tissues during morphogenesis. Here, we show that the Endoplasmic Reticulum (ER)-resident protein anterior gradient-2 (AGR2), a soluble protein-disulfide isomerase involved in ER protein folding and quality control, is secreted and interacts with the ECM. Extracellular AGR2 (eAGR2) is a microenvironmental regulator of epithelial tissue architecture, which plays a role in the preneoplastic phenotype and contributes to epithelial tumorigenicity. Indeed, eAGR2, is secreted as a functionally active protein independently of its thioredoxin-like domain (CXXS) and of its ER-retention domain (KTEL), and is sufficient, by itself, to promote the acquisition of invasive and metastatic features. Therefore, we conclude that eAGR2 plays an extracellular role independent of its ER function and we elucidate this gain-of-function as a novel and unexpected critical ECM microenvironmental pro-oncogenic regulator of epithelial morphogenesis and tumorigenesis.DOI:http://dx.doi.org/10.7554/eLife.13887.001 Cancer cells multiply abnormally fast and therefore produce protein molecules faster than normal cells. To avoid becoming stressed by this overproduction, cancer cells make use of proteins that fold the new proteins inside the cell. One of these protein folders is called anterior gradient-2 (or AGR2 for short) and is produced at high levels in so-called epithelial cancers, such as breast and lung cancer.Previous research has shown that AGR2 inside cancer cells can help them grow and survive and AGR2 can also be found outside cells, such as in the blood or the urine of cancer patients. Therefore some researchers have suggested that measuring the levels of AGR2 in bodily fluids may be a useful marker for detecting cancers. Fessart et al. hypothesized that – apart from becoming a promising diagnostic tool – the AGR2 protein itself, specifically when found outside cells, might make cancer cells more aggressive.Fessart et al. used a range of techniques to test this hypothesis. For example, healthy lung cells and lung cancer cells were grown into miniature replicas of lung organs in the laboratory, and in a key experiment, AGR2 was added to the lung organoids grown from the healthy cells. The addition of AGR2 protein was enough to change the non-tumor organoids into tumor organoids and boosted their growth about ten-fold. Further experiments then revealed that AGR2 also makes cells more invasive and capable of moving, both important features of aggressive cancer cells.Overall, Fessart et al. have proven that AGR2 is a signalling molecule found outside cancer cells that makes them more aggressive. In future, more research addressing how AGR2 achieves this may lead to new therapeutic strategies against some forms of cancer.DOI:http://dx.doi.org/10.7554/eLife.13887.002 It is well established that proper organization, maintenance, and function of the epithelial organs are largely ascribed to cell-cell adhesion and polarization of epithelium and its interaction with extracellular matrix (ECM). Apical-basal polarity is a fundamental feature of epithelial cells required for cell division, differentiation and morphogenesis . PolarizAnterior gradient 2 (AGR2) encodes an endoplasmic reticulum (ER)-resident protein mainly expressed in epithelial cells in human. Enhanced intracellular AGR2 (iAGR2) expression is observed in many cancers (reviewed in Ref  This could explain how high levels of iAGR2 expression in cancer cells combined with higher cell density and/or altered growth media could impact on the amount and rate of eAGR2 secretion.”2) Related to these points:What is the final concentration of AGR2 added to the medium? A titration curve is shown in supplementary material, but the concentration used in the experiments is not clearly indicated. Are they in the ranges found in patients?The final concentration of AGR2 added to the medium is 40 ng/ml. This is now included in the Results section of the revised manuscript: “Remarkably, the addition of recombinant human AGR2 (40 ng/ml)  to the mMoreover, the concentration of eAGR2 that we used for our experiments is in the range of eAGR2 levels of present in the serum from cancer patients. This information has now been added in the Results section: “Given that eAGR2 is secreted by tumor organoids  and thatAlso, it would be important to address the following issues:Is the KTEL still present and accessible in secreted AGR2?This is an interesting point raised by reviewers. To address this issue, we have used structural data available to visualize the structure of the KTEL domain . Our data reveal that for the eAGR2 extracellular activity, the KTEL motif is not required.”Is the T to D replacement in the C terminal tetrapeptide required for secretion?As suggested by the reviewers, we have measured the secretion capacity of AGR2-KTEL as compared to AGR2-KDEL and two others KDEL mutants (K to A or K to D). These experiments were performed by Dr. Tony Avril , who is now listed as a co-author in the revised manuscript. Our data show that the replacements of T to D, K to A or K to D in the carboxyl-terminal KTEL sequence still allowed AGR2 secretion.These new results are included in 3) In the Abstract and throughout the manuscript the authors present the eAGR2 as part of the ECM and in this context playing an important role as microenvironment pro-oncogenic regulator, but the experimental data supporting this statement are missing. The authors did not provide any evidence for the eAGR2 as part of ECM. The fact that the eAGR2 was identified in conditioned medium or secretome does not mean that is part of the ECM. The tissue staining did also not confirm the presence of AGR2 in ECM. There was no colocalization with ECM components in stained tumor tissue – this could be easily confirmed by co-staining with antibodies against ECM components. Co-IP could also be an alternative to prove that the eAGR2 is part of the ECM. And since there is no evidence that AGR2 is part of ECM, the data presented and their interpretation are less convincing.We agree with reviewers that the proof that eAGR2 is part of the ECM needs to be established. As a consequence, new experimental data were added in the revised manuscript demonstrating that eAGR2 interacts with ECM proteins. To this end, two approaches were used including i) an ECM purification strategy and ii) immunofluorescence microscopy.The method is now described in the revised Materials and methods section of the revised manuscript as follows: “To generate cell-derived matrices, HBECs were plated in 6-well plates and cultured for 8 days, changing the growth medium every two days. […] ECM was fixed with 4% paraformaldehyde and proceeds to immunofluorescence as described previously.”The corresponding results are presented in the revised We also added a paragraph about the implications of these data in the Discussion section of the revised manuscript as follows:“We observed that eAGR2, which is undetectable in the ECM of non-tumor organoid, is abundantly secreted by tumor organoids and is present in the ECM. Hence, these data suggest an important role for eAGR2 in controlling ECM functionality, and most likely in the remodeling of tumor microenvironment. A similar role was described for PDIA3 in remodeling of the ECM during renal fibrosis .”4) To further clarify the mechanism, the organoid system can be exploited to address the following:A) Recombinant AGR2 protein can mimic some of the effects on cell outcomes and an AXXA mutant is used as a negative control. The AXXA mutant might be unfolded or denatured and a better control for the CXXS mutant would be a SXXS mutant, not a AXXA mutant. Could this more subtle AGR2 mutant protein be purified and accessed? For example, the SXXS mutant might be active, meaning a protein-protein interaction (PPI), not redox, is driving this event.As suggested by reviewers, the AGR2-SXXS mutant was purified and tested on organoids from H23-Sh-AGR2 cells and compared to the effects yielded by wild type AGR2 (eAGR2). The thioredoxin active eAGR2 mutant restored the formation of tumor organoids. The corresponding results are now presented in revised We also discussed these data in the Discussion section of the revised manuscript as follows:“Moreover, tumor organoid formation was restored when AGR2-depleted tumor organoids were supplemented with AGR2-SXXS (thioredoxin inactive), but not in the presence of reducing agent, suggesting a protein-protein interaction between eAGR2 and ECM proteins.”Can AGR2 function be replaced by chemical reductants like DTT? If so, then redox could be important. Perhaps both a PPI and redox are driving the signalling events. This can be addressed experimentally.To address whether the redox function of AGR2 is important for its extracellular functions we assessed the effect of a chemical reductant (DTT), used at a final concentration of 5 µM, on organoid formation. We found that the addition of DTT to AGR2-depleted H23 organoids did not restore the formation of tumor organoids. The corresponding results are presented in revised This result is also discussed in the Discussion section of the revised manuscript, as follows:“Moreover, tumor organoid formation was restored when AGR2-depleted tumor organoids were supplemented with AGR2-SXXS (thioredoxin inactive), but not in the presence of reducing agent, suggesting a protein-protein interaction between eAGR2 and ECM proteins. Therefore, our data demonstrate that eAGR2 drives tumor organoids formation, not by changing the redox nature of microenvironment but through its presence within the ECM.”B) If authentic AGR2 can be detected in seasoned media, can this replace recombinant bacterially expressed AGR2 and/or can anti-AGR2 antibodies neutralize this effect?As suggested by reviewers, the ability of conditioned medium to replace bacterial recombinant on 3D cell growth in a paracrine/autocrine manner was examined and presented in in revised The results are also described in the Results section of the revised manuscript: “To determine whether eAGR2 acts through an autocrine/paracrine mechanism, conditioned media from HBEC cells stably infected with empty vector (HBEC-EV) or overexpressing AGR2 (HBEC-AGR2) were added to HBEC organoids . […] Thi


Payment incentives are known to influence healthcare but little is known about the impact of paying directly for achieved outcomes. In England, novel purchasing (commissioning) of National Health Service (NHS) stop smoking services, which paid providers for quits achieved whilst encouraging new market entrants, was implemented in eight localities (primary care trusts (PCTs)) in April 2010. This study examines the impact of the novel commissioning on these services.Accredited providers were paid standard tariffs for each smoker who was supported to quit for four and 12 weeks. A cluster-controlled study design was used with the eight intervention PCTs  matched with a control group of all other (n=64) PCTs with similar demographics which did not implement the novel commissioning arrangements. The primary outcome measure was changes in quits at four weeks between April 2009 and March 2013. A secondary outcome measure was the number of new market entrants within the group of the largest two providers at PCT-level.The number of four-week quits per 1,000 adult population increased per year on average by 9.6% in the intervention PCTs compared to a decrease of 1.1% in the control PCTs . Eighty-five providers held ‘any qualified provider’ contracts for stop smoking services across the eight intervention PCTs in 2011/12, and 84% of the four-week quits were accounted for by the largest two providers at PCT-level. Three of these 10 providers were new market entrants. To the extent that the intervention incentivized providers to overstate quits in order to increase income, caution is appropriate when considering the findings.Novel commissioning to incentivize achievement of specific clinical outcomes and attract new service providers can increase the effectiveness and supply of NHS stop smoking services. Smoking is a leading cause of premature mortality and morbidity in developed countries . In 1998Payment incentives influence the behaviour of healthcare providers . Policy In April 2010, commissioners from eight PCTs in one region volunteered to collaborate with their host health authority  and take a unified approach to piloting new contracts for stop smoking services. They adopted qualification-based provider regulations known as ‘any qualified provider’ regulations, which allowed any providers to deliver services that met specified criteria, including adhering to NHS service quality requirements and accepting new payment, contractual and reporting obligations ,3. This The objective of this study was to evaluate the impact of the stop smoking intervention and this study is the first to report the impact of commissioning NHS treatment using payment incentives based solely on achieving specific clinical outcomes combined with opening the provider market using qualification-based regulations .We used a cluster controlled study design in which the eight intervention PCTs were retrospectively matched to a control group of the 64 PCTs with similar demographics which did not implement the outcome-based payment system. The Office for National Statistics (ONS) has categorised geographic areas of the UK based on similar local population characteristics using census data . Each ofOur outcome measures are 1) The number of enrolled smokers per 1,000 adult population. 2) The number of four-week quits per 1,000 adult population. A four-week quit is defined as a treated smoker self-reporting continuous abstinence from smoking from day 14 post-quit date whose quit status within 25 to 42 days of the quit date has been assessed  ,23. Two Our main analysis used published PCT-level data on stop smoking services for the years 2009/10 to 2012/13 . Data onWe began with exploratory plots of the outcome variables over time for each PCT nested within clusters. We used mixed effects Poisson regression models to account for “clustering” effects, with repeated measures nested within PCTs and PCTs nested within clusters (n = 6). PCTs and clusters (n = 6) had random intercept terms and PCTs had a random slope term. The response variable was a given smoking cessation outcome measure (e.g. the estimated number of four-week quits), with an exposure variable reflecting the area of opportunity (e.g. adult population). The model had three covariates—pilot PCT (yes/no), years  and their interaction. The hypothesis of interest (i.e. "effect-size") was represented by the interaction term (rate of change of the incidence rate ratio). If the interaction term was significantly greater than 1 then we can conclude that the intervention PCTs have a higher rate of change than controls. Statistical significance was set at 5%. Plots were produced in the lattice package  in R 2828 and moWe have undertaken sensitivity analysis at an aggregate level to explore whether changing our measure of quits or controls altered the findings. Our baseline analysis assumed that 16% of quits not confirmed by CO testing should be excluded, and we varied this by plus or minus 8%, (a) and (b) respectively, as well including all self-reported quits (c) and only CO-verified quits (d). We made three changes to the control. We reran the analysis taking account of the presence of the pre-existing ‘spearhead’ localities (e). The spearhead group was announced by the Department of Health in 2004, and comprised the 20% of local authorities in England facing the greatest health challenges . The aimThe intervention PCTs experienced an increase in the number of enrolled smokers per 1,000 adult population of 5.5% per year on average compared to a decrease of 2.5% per year in the control PCTs . The intThe number of quits per 1,000 adult population increased by 9.6% per year on average in the intervention PCTs, compared with a decrease of 1.1% per year in the control PCTs, and the difference in the ratio of rates was statistically significant  .The approximately tenfold better rate of improvement in performance of the intervention PCTs compared to the control PCTs was maintained when we varied the percentage of excluded quits not confirmed by CO testing by plus or minus 8%, as well including all self-reported quits and only CO-verified quits . The finThe four-week quit rate used by the Department of Health to monitor performance, defined as the number of CO-validated four-week quits as a percentage of the number of enrolled smokers, increased by 6.8% per year on average in the intervention PCTs and 2.8% per year in the control PCTs, and the difference in change was not statistically significant  . By 2012In 2011/12, 85 providers held stop smoking services contracts in the intervention PCTs under the ‘any qualified provider’ regulations . Six of The intervention’s data on ‘any qualified provider’ contracts show that six providers were the largest PCT-level providers across the intervention, and they accounted for 75% of the total intervention quits in 2011/12 . In fiveA measure of the quality of smoking cessation services is the percentage of self-reported quits validated by CO testing, which increased by 3.0% per year on average in the intervention PCTs compared to 1.3% per year in the control PCTs. Although this difference was not statistically significant  , the intAs noted above, providers in the intervention PCTs increased reach to smokers by getting more individuals to set a quit date. However, they did not maintain contact with the larger group of individuals attempting to quit. The difference in change in the number of individuals not lost to follow-up per 1,000 adult population for intervention PCTs and control PCTs  was not statistically significant . InsteadThe intervention PCTs that adopted novel commissioning policies, comprising pay-for-outcomes and opening the market to new providers, experienced substantial increases in quits compared to the controls: the number of four-week quits per 1,000 adult population increased by 9.6% per year on average in the intervention PCTs, compared with a decrease of 1.1% per year in the control PCTs. This finding is important because despite the growth in stop smoking services, which are viewed as cost-effective, the scale of these services remains small in the face of the challenge to reduce smoking prevalence . The larThe wider adoption of the novel commissioning policies described in our study is being pursued in the NHS and other public sectors ,18, despOur cluster controlled study provides an appropriate quasi-experimental design for this type of policy intervention . Our appBy adjusting the recorded quit data for the over-reporting of non-validated quits, we have attempted to improve the basis for making comparisons over time and between localities. As noted above, we did not use CO-validated quits only in our main analysis because changes in this measure may reflect changes in CO-verification activity rather than changes in the number of smokers quitting. Furthermore, although CO verification is viewed as an important quality indicator, and a comparatively high proportion of four-week quits in the intervention PCTs were validated by CO testing 86% in 2012/13), the test does not provide assurance that abstinence has occurred beyond a few hours, and certainly not since two weeks of the quit dates. To the extent that the intervention introduced a new incentive for providers to overstate four-week quits achieved in order to increase income, caution is appropriate when considering the findings. However, this incentive may have been moderated through the introduction of random audits of cases carried out by commissioners in the intervention PCTs, and this activity suggests that the outcome of the intervention should not be attributed to gaming or changes in record-keeping. The requirement to monitor data supplied by providers paid for successful quit data was recognised as being “especially important” in national guidance . Nationa012/13, tAnother measure of the reported impact of stop smoking services is the number of CO-validated four-week quits beyond those estimated to have occurred with only a prescription for smoking cessation medication and no behavioural support . For theThe intervention PCTs introduced enhanced payments for providers achieving quits in a range of target subpopulations, the assessment of which is constrained by limited relevant published data. Nevertheless, further analysis should explore whether the intervention was associated with ‘cream-skimming’ by providers seeking to avoid treating patients with more complex needs . The intIntervention PCT C in cluster 2 had a comparatively low baseline enrolment rate, and it could be argued that this made it comparatively easy to improve its performance. This may be so, but the majority of intervention PCTs were not in this situation, and it is also apparent that enrolment across the intervention PCTs and controls was typically very low in relation to the 20% of the adult population who smoke . It is pOur findings suggest that the novel commissioning arrangements can improve the effectiveness and supply of the NHS stop smoking services. As the international policy momentum in payment-for-outcomes builds, this large-scale example of its application has relevance for policy makers and clinicians working in a range of healthcare settings and systems.Several aspects of the intervention merit further study. 1) The changes in quits, enrolment and follow-up suggest that practice has changed in the intervention PCTs, but the precise mechanisms associated with the change in financial incentives, remain unclear. 2) We have focused on aggregate results, and study of individual clusters may provide useful insight into implementation of the intervention in local contexts. 3) By introducing payment incentives to achieve 12-week quits, the intervention PCTs have started to explore the provision of longer term support for smokers beyond the four-week threshold used by the Department of Health. This is important because about three-quarters of smokers who have quit at four weeks will relapse within one year . HoweverS1 Box(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


Atopic dermatitis (AD) is a complex disorder characterized by flares of red and itchy skin. Infants with AD often also suffer from food allergy. Both ingestion of and skin contact with the antigen can exacerbate the AD. The affected skin is characterized by oedema formation and shows typical Th2 responses by increased levels of IL-4 and IL-13, but not IFN-γ. Furthermore, there is an influx of eosinophils and CD4+ cells. The CD4+ cells are previously described to originate from the gut mucosa where they developed upon oral sensitization to the food allergen.From both specific oligosaccharides and TLR ligands, it is clinically demonstrated that it can affect the development of AD, by skewing the immune response away from an allergic phenotype.In this study, a mouse model to study the effect of dietary compounds on the development and treatment of food allergy-induced AD will be set-up. For this model, BALB/c mice are orally sensitized with ovalbumin  plus cholera toxin adjuvant once a week, for 5 consecutive weeks. One week after the last sensitization, the mouse skin will be challenged with OVA on tape-stripped back skin. This is repeated 3 days later, and again 4 days later, the mice are sacrificed. Outcome parameters as skin thickness, inflammatory cell influx and skin mucosal cytokine levels will be studied. Following development of the AD model the effect of dietary intenvention with a combination of specific OS and TLR ligands will be evaluated on the different outcome parameters.