Minnie Sarwal and colleagues developed a gene expression assay using peripheral blood samples to detect patients with renal transplant at high risk for acute rejection.Please see later in the article for the Editors' Summary Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.We developed a novel correlation-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal transplant patients collected across eight transplant centers in the US, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation (AART) study. Gene expression was assessed by quantitative real-time PCR (QPCR) in one center. A 17-gene set—the Kidney Solid Organ Response Test (kSORT)—was selected in 143 samples for AR classification using discriminant analysis , validated in 124 independent samples  and evaluated for AR prediction in 191 serial samples, where it predicted AR up to 3 mo prior to detection by the current gold standard (biopsy). A novel reference-based algorithm (using 13 12-gene models) was developed in 100 independent samples to provide a numerical AR risk score, to classify patients as high risk versus low risk for AR. kSORT was able to detect AR in blood independent of age, time post-transplantation, and sample source without additional data normalization; AUC = 0.93 (95% CI 0.86–0.99). Further validation of kSORT is planned in prospective clinical observational and interventional trials.The kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants.Please see later in the article for the Editors' Summary Throughout life, the kidneys filter waste products  and excess water from the blood to make urine. If the kidneys stop working for any reason, the rate at which the blood is filtered decreases, and dangerous amounts of creatinine and other waste products build up in the blood. The kidneys can fail suddenly (acute kidney failure) because of injury or poisoning, but usually failing kidneys stop working gradually over many years (chronic kidney disease). Chronic kidney disease is very common, especially in people who have high blood pressure or diabetes and in elderly people. In the UK, for example, about 20% of people aged 65–74 years have some degree of chronic kidney disease. People whose kidneys fail completely (end-stage kidney disease) need regular dialysis  or a renal transplant  to keep them alive.Our immune system protects us from pathogens (disease-causing organisms) by recognizing specific molecules (antigens) on the invader's surface as foreign and initiating a sequence of events that kills the invader. Unfortunately, the immune system sometimes recognizes kidney transplants as foreign and triggers transplant rejection. The chances of rejection can be minimized by “matching” the antigens on the donated kidney to those on the tissues of the kidney recipient and by giving the recipient immunosuppressive drugs. However, acute rejection (rejection during the first year after transplantation) affects about 20% of kidney transplants. Acute rejection needs to be detected quickly and treated with a short course of more powerful immunosuppressants because it increases the risk of transplant failure. The current “gold standard” method for detecting acute rejection if the level of creatinine in the patient's blood begins to rise is to surgically remove a small piece (biopsy) of the transplanted kidney for analysis. However, other conditions can change creatinine levels, acute rejection can occur without creatinine levels changing , and biopsies are invasive. Here, the researchers develop a noninvasive test for acute kidney rejection called the Kidney Solid Organ Response Test (kSORT) based on gene expression levels in the blood.For the Assessment of Acute Rejection in Renal Transplantation (AART) study, the researchers used an assay called quantitative polymerase chain reaction (QPCR) to measure the expression of 43 genes whose expression levels change during acute kidney rejection in blood samples collected from patients who had had a kidney transplant. Using a training set of 143 samples and statistical analyses, the researchers identified a 17-gene set (kSORT) that discriminated between patients with and without acute rejection detected by kidney biopsy. The 17-gene set correctly identified 39 of the samples taken from 47 patients with acute rejection as being from patients with acute rejection, and 87 of 96 samples from patients without acute rejection as being from patients without acute rejection. The researchers validated the gene set using 124 independent samples. Then, using 191 serial samples, they showed that the gene set was able to predict acute rejection up to three months before detection by biopsy. Finally, the researchers used 100 blood samples to develop an algorithm  to classify patients as being at high or low risk of acute rejection.These findings describe the early development of a noninvasive tool (kSORT) that might, eventually, help clinicians identify patients at risk of acute rejection after kidney transplantation. kSORT needs to be tested in more patients before being used clinically, however, to validate its predictive ability, particularly given that the current gold standard test against which it was compared (biopsy) is far from perfect. An additional limitation of kSORT is that it did not discriminate between cell-mediated and antibody-mediated immune rejection. These two types of immune rejection are treated in different ways, so clinicians ideally need a test for acute rejection that indicates which form of immune rejection is involved. The authors are conducting a follow-up study to help determine whether kSORT can be used in clinical practice to identify acute rejection and to identify which patients are at greatest risk of transplant rejection and may require biopsy.http://dx.doi.org/10.1371/journal.pmed.1001759.Please access these websites via the online version of this summary at US National Kidney and Urologic Diseases Information Clearinghouse provides links to information about all aspects of kidney disease; the US National Kidney Disease Education Program provides resources to help improve the understanding, detection, and management of kidney disease (in English and Spanish)The chronic kidney disease and about kidney transplants, including some personal storiesThe UK National Health Service Choices website provides information for patients on chronic kidney disease and about kidney transplantation (in English and Spanish)The US National Kidney Foundation, a not-for-profit organization, provides information about UK National Kidney Federation provides support and information for patients with kidney disease and for their carers, including information and personal stories about kidney donation and transplantationThe not-for-profit World Kidney Day, a joint initiative between the International Society of Nephrology and the International Federation of Kidney Foundations, aims to raise awareness about kidneys and kidney diseasekidney diseases, kidney failure, and kidney transplantation; the MedlinePlus encyclopedia has a page about transplant rejectionMedlinePlus provides links to additional resources about  Despite the application of high-throughput discovery methods for improved detection of disease-specific biomarkers All patients gave written informed consent to participate in the study, and the study was approved by the individual institutional review boards at Stanford University, California Pacific Medical Center, Emory University, University of Pittsburgh Medical Center, University of California Los Angeles, University of California San Francisco, Bellvitge University Hospital, and the Hospital Infantil de México Federico Gómez. All procedures were conducted according to the principles expressed in the Declaration of Helsinki n = 162 pediatric samples); Hospital Infantil de México Federico Gómez Laboratorio de Investigacion en Nefrologia, Mexico City, Mexico ; Emory University, Atlanta, Georgia ; University of California Los Angeles, Los Angeles, California ; University of Pittsburgh, Pittsburgh, Pennsylvania ; California Pacific Medical Center, San Francisco, California ; University of California San Francisco, San Francisco, California ; and Bellvitge University Hospital, Barcelona, Spain . Samples were split into a training set of 143 AR and No-AR adult samples collected in non-clinical-trial settings in four centers (AART143 cohort) for gene selection and model training; a first validation set of 124 independent AR and No-AR adult (≥20 y) and pediatric (<20 y) samples (AART124 cohort) collected by each participating center, for validation of selected genes for AR detection across ages, centers, and settings; and a second prospective validation set of 191 adult and pediatric samples serially collected up to 6 mo prior to and after a rejection biopsy (AART191 cohort) for evaluation of AR prediction. Blood samples from these three cohorts were simultaneously measured on the microfluidic high-throughput BioMark QPCR platform  The Assessment of Acute Rejection in Renal Transplantation (AART) study was designed in a collaborative effort in eight renal transplant centers in the United States, Mexico, and Spain, and utilized 558 peripheral blood (PB) samples from 436 adult and pediatric renal transplant patients for developing a simple blood QPCR test for AR diagnosis and prediction in recipients of diverse ages, on diverse immunosuppression, and subject to transplant-center-specific protocols. Patients <20 y of age were included in the pediatric cohort, and patients ≥20 y were included in the adult cohort. The mean age in the pediatric dataset was 13.20 y±4.76 y, and the mean age in the adult dataset was 47.43 y±14.59 y. Blood samples were collected from transplant recipients cross-sectionally during clinical follow-up visits and were matched with a contemporaneous kidney allograft biopsy between 2005 and 2012. Centers that participated in the AART study were Stanford University, Stanford, California  and all cases of No-AR (n = 370), with the exception of 12 samples collected before or after a rejection and 13 samples from clinically stable patients . Blood samples collected at the time of clinically indicated biopsies (drift in serum creatinine >20% of baseline) were included in the analyses if obtained prior to any immunosuppression treatment intensification. Blood samples collected at the point of protocol biopsies were obtained from all pediatric patients at engraftment; at 3, 6, 12 and 24 mo post-transplantation; and additionally at the times of clinically suspected graft dysfunction n = 16) at 6 mo post-transplantation. Multiple PB–biopsy pairs from the same patient were utilized as long as each biopsy had a conclusive phenotypic diagnosis. Each biopsy was scored by the center pathologist at each enrolling clinical site using the latest Banff classification criteria for renal allograft pathology n = 188) or No-AR (n = 370). AR included both cellular and humoral types and was defined as presence of (1) acute interstitial and tubular Banff scores ≥1 or (2) lesions in the glomeruli and peritubular capillary compartments paired with diffuse positive C4d staining at peritubular capillaries . No-AR was defined as an absence of any histological evidence of AR. In the No-AR group, there were 40 samples that either met the Banff criteria for chronic allograft injury  or exhibited chronic calcineurin inhibitor (CNI) toxicity (n = 6), BK polyomavirus (BKV) infection (n = 4), or other graft injury such as acute tubular nephritis (n = 27). While 19 cases met the criteria of humoral rejection, the remaining AR biopsies were considered cell-mediated rejection or mixed rejection if they had absent C4d staining but detectable DSA. Samples included in the AART study were collected between 5 February 2005 and 15 December 2012.All adult patients included in this study were enrolled in the regular transplant program at each center. Pediatric patients were those participating in an investigator-initiated clinical trial Blood was collected in 2.5-ml PAXgene Blood RNA Tubes , in Ficoll tubes for PB lymphocyte (PBL) isolation, or in heparin-coated tubes for PB mononuclear cell (PBMC) isolation. Total RNA was extracted using the column-based method kits of PreAnalytiX (Qiagen) for PAXgene tubes and RNeasy (Qiagen) for PBLs and PBMCs as per manufacturer's protocol. Total RNA was measured for RNA integrity using the RNA 6000 Nano LabChip Kit on a 2100 Bioanalyzer , with suitable RNA defined by an RNA integrity number exceeding 7 cDNA synthesis was performed using 250 ng of extracted quality mRNA from the PB samples using the SuperScript II first strand cDNA synthesis kit  as per the manufacturer's protocol.Samples were prepared for microfluidic QPCR analysis of 43 genes by taking 1.52 ng of total RNA from cDNA synthesis and processing it through specific target amplification and sample dilution as described by Fluidigm using pooled individual ABI Taqman assays for each gene, excluding ribosomal 18S RNA. Briefly, specific target amplification was performed using 1.52 ng of cDNA with the pooled Taqman assays in multiplex with Taqman PreAmp Master Mix (ABI) to 5 µl of final volume, for 18 cycles in a Vapo-Protect thermal cycler , then diluted 1∶5 with sterile water .Microfluidic QPCR was performed on the 96.96 dynamic array (Fluidigm) using 2.25 µl of the diluted cDNA sample from the specific target amplification, along with Taqman assays for each gene, Taqman Universal Master Mix, and Fluidigm loading reagent as outlined in the manufacturer's protocol, by priming and loading the chip via the HX IFC Controller (Fluidigm) and performing the QPCR in the BioMark QPCR platform (Fluidigm), with default parameters for gene expression data collection as advised by Fluidigm.Standard protocols were used for QPCR reactions on the ABI 7900 Sequence Detection System or the ViiA7 (ABI) under standard conditions , using the same TaqMan gene expression assays (ABI) utilized in the Fluidigm QPCR.T) method 18S) as 18S showed the least variability in gene expression across all samples when compared to the expression of three additional housekeeping genes tested . Visualization of the raw QPCR data and identification of external confounders were done in Partek Genomics Suite version 6.6  by unsupervised principal component analysis, hierarchical clustering, and analysis of covariance, and in R by the Empirical Bayes method, which uses estimations for the least squares parameters (mean and variance) for each gene (ComBat method using the SVA package in R) p<0.05) and were thus included as random categorical factors in the batch removal feature (mixed effect model ANOVA) provided in Partek Genomics Suite for additional data normalization applied to the Fluidigm QPCR data, where factors were chosen at random to correct for an overall variability associated with these factors The relative amount of mRNA expression in each sample was calculated using the comparative threshold cycle  were significantly associated with AR in the pediatric dataset analyzed at Stanford and cross-validated in the National Institutes of Health (NIH) SNSO1 randomized multicenter trial ABTB1, ANK1, CEACAM4, CHST11, EPOR, IL2RB, MAP2K3, NFE2, PCTP, PSMB9, RUNX3, SLC25A37, STAT3, YPEL3) were selected from our initial blood microarray discovery performed across three different microarray platforms C1orf38, GBP1, GBP2, LYST, RARA, RXRA, TNFRSF1A); natural killer cells, T lymphocytes, and dendritic cells ; and endothelial cells (MMP1); significant enrichment was determined as >3-fold increase above the mean expression in all cells (using the GSE1133 dataset). Remaining genes were selected based on other studies by our group on renal transplant AR .The 43-rejection-gene set represents a combination of significant genes in acute renal allograft rejection identified by whole genome microarray analysis of biopsy and paired blood samples transcriptionally profiled from patients with and without biopsy-confirmed AR. The discovery process for these genes has been previously published by us CFLAR, DUSP1, IFNGR1, ITGAX, MAPK9, NAMPT, NKTR, PSEN1, RNF130, RYBP) that have been validated as diagnostic for AR in PB using standardized clinical trial sample collection and processing protocols CEACAM4, EPOR, GZMK, RARA, RHEB, RXRA, SLC25A37), which improved the accuracy of the gene panel for discriminating AR from No-AR samples, for a final selection of 17 genes. These 17 genes were then validated for their discriminatory performance in an independent cohort of both adult and pediatric biopsy-matched blood samples (AART124) collected by non-standardized non-clinical-trial methods as well as by standardized clinical trial methods. The statistical methods used for selection of the 17 genes included t-test statistics with the Bonferroni post-correction or the q-value method to control the false discovery rate ; penalized logistic regression with the lasso and elastic net regularization paths (to define genes with the highest predictive power for AR) p≤0.05. Receiver operating characteristic (ROC) curves were generated in GraphPadPrism vversion 5 . For comparison of the areas under the different ROC curves, the method by Hanley and McNeil was applied with a threshold for significance set at p≤0.05 First, we evaluated the differential expression of ten out of 43 genes, selecting the same ten genes , derived from the mean gene expression signature (centroid) obtained from multiple AR or No-AR samples. When a given sample gene expression profile is analyzed in kSAS, its expression is correlated to the reference profiles for one or more developed gene sets (models). In the Fluidigm dataset, kSAS was used to identify a single model composed of 14 of the 17 genes. For the ABI analyses, multiple equivalent top-performing gene sets (n = 13) were discovered from the analysis of samples from two collection sites (UCSF and UPMC) by randomly selecting and testing the performance of all possible permutations of different-sized gene sets of the 17 genes. Prior to discovery and testing of the ABI samples in the AART100 dataset, reference centroids were developed for different protocols (sample collection/processing methods). All ABI samples were evaluated against this database of reference centroids, with results reported for the centroid pair (AR and No-AR) with the greatest correlation to the evaluated sample (independent of collection methods). Classification of a sample as AR, No-AR, or indeterminate was derived by computing the Pearson correlation coefficients for all sample–centroid comparisons, assigning a score of 1 to samples with a greater correlation to an AR centroid and a score of −1 to samples with a greater correlation to a No-AR centroid. A kSAS aggregated AR risk score (−13 to 13) was then determined by summing the scores for all evaluated gene models. Indeterminate samples were defined as those with a score less than 9 and greater than −9, based on evaluation of sample classification. All gene models are provided in Given the underlying heterogeneity of the AART study samples, due to differences in sample handling and co-morbidities in the adult population versus the pediatric cohort, and to avoid the step of initial data normalization DUSP1, CFLAR, ITGAX, NAMPT, MAPK9, RNF130, IFNGR1, PSEN1, RYBP, NKTR) correctly predicted 87.4% of the samples , with an area under the ROC curve (AUC) of 0.86 (95% CI 0.79–0.93)  were part of the original ten genes analyzed CEACAM4, CXCL10, GZB, IL2RB, RARA, RHEB, SLC25A37, C1orf38, EPOR, GZMK, ABTB1, NFE2, FOXP3, MMP1, MAP2K3) correctly classified 91.6% of the AR and No-AR samples  with a sensitivity of 85.9% and a specificity of 94.2%. However, this approach was unsuccessful in identification of AR in a purely pediatric data subset of the SNSO1 clinical trial EPOR, GZB, NFE2, SLC25A37). To maintain the integrity of the assay to discriminate AR irrespective of recipient age, we retained the original panel of ten genes DUSP1, CFLAR, ITGAX, NAMPT, MAPK9, RNF130, IFNGR1, PSEN1, RYBP, NKTR). The inclusion of seven additional genes  optimized the performance of the gene set for discriminating AR in both adult and pediatric samples. These seven genes were identified by differential expression analyses for AR versus No-AR in both AART143 (adult samples only) and AART124 (adult and pediatric samples)  and/or selected by the elastic net  in the training set of AART143 samples. All 17 genes were selected  as the best gene set for discriminating AR in samples from adult patients (enrolled from four different US centers). The 17 genes predicted 39 of 47 AR samples correctly as AR, and 87 of 96 No-AR samples correctly as No-AR, resulting in a sensitivity of 82.98% (95% CI 69.19%–92.35%) and specificity of 90.63% (95% CI 82.95%–95.62%), with a calculated AUC of 0.94    . Out of p<0.001) ; Table 2p<0.001) . A flow n = 59) and pediatric (n = 65) set of 124 independent samples (AART124). In the AART124 cohort, the 17-gene plsDA model predicted 21 of 23 AR samples correctly as AR, and 100 of 101 No-AR samples correctly as No-AR , we tested its performance in a combined adult , UPMC (n = 81), UCLA (n = 44), and CPMC (n = 35) from AART143 and AART124. The performance of the assay was equivalent at each transplant center (p>0.05), with individual AUCs>0.8 at all four centers  only  and 51 patients with clear cell-mediated rejection (TCMR)  was observed when assessed by the plsDA 17-gene model (p = 0.009). We additionally evaluated the performance of kSORT individually in the AR and in the No-AR groups for time post-transplantation categorized into 0–6 mo, 6 mo–1 y, and >1 y post-transplantation; comparing the kSORT scores using a one-way ANOVA model with Tukey's post-test for multiple comparisons, p-values in each group did not reach significance . Post-test for linear trend analysis in both the AR and No-AR groups confirmed no significant association with time post-transplantation   significantly influenced the outcome for AR versus No-AR  .n = 65) or after  a biopsy-matched AR episode (n = 74) were analyzed. Out of the patients with blood samples 0–3 mo prior to the AR biopsy (n = 35), at time of stable graft function, 62.9% (22 out of 35 samples) had very high AR prediction scores (96.4%±0.8) (p<0.001). Of the patients with blood samples drawn 0–3 mo after AR treatment (n = 31), 51.6% (16 out of 31) still had elevated predicted AR scores (86.00%±0.17); the remaining 15 samples showed AR scores below the threshold θ for AR  at 0–3 mo after AR treatment. As serum creatinine levels in patients with elevated AR prediction scores were 2.04±0.40 mg/dl compared to creatinine levels of 1.8±0.4 mg/dl in patients with decreased AR prediction scores, the latter likely represented patients who responded to AR treatment  drawn either before  ; Table 3reatment .RXRA, RARA, CEACAM4), endothelial cells , and T cells (GZMK) in the peripheral circulation. Eleven of the 17 genes played a common role in cell death and the cell survival network  are considered indeterminate and thus cannot be confidently assigned as AR or No-AR. The kSORT kSAS assay was developed and cross-validated in 100 independent adult and pediatric samples collected from 44 patients with AR and 56 patients without AR . To mitigate the data normalization required to control for batch effect and sample handling differences, we developed a new algorithm called kSAS  to classthout AR .n = 95 and a test set of n = 48 by random selection. From the 17 AR genes evaluated, kSAS identified a single gene set model composed of 14 genes  representing a combination of eight of the ten genes from the Li et al. study To develop the kSAS algorithm, we utilized the Fluidigm QPCR dataset of the AART143 cohort and split the samples into a training set of n = 32, UCSF and UPMC) confirmed as AR (n = 16) or No-AR (n = 16) were evaluated by kSAS and tested for the performance of over 130,000 different possible gene combinations (3–17 genes per model). This analysis yielded 13 different 12-gene combinations utilizing a total of 15 of the 17 genes , with seven core genes present in all 13 12-gene combinations   were calculated from known AR and known No-AR samples for the major experimental covariates (sample collection/processing methods) to create a panel of possible reference AR and No-AR profiles. For each evaluated sample, kSAS automatically selects the most correlated reference set for downstream classification. In contrast to other classification algorithms, kSAS does not apply fixed mathematical coefficients, and thus its output is not affected by differences in RNA preparation and handling variances, which can affect QPCR data ranges. Blood samples (LC25A37) . As our n = 15) were predicted as indeterminate (n = 42) were significantly higher than those in the No-AR samples  by two-sided Student's t test, with p<0.001 . The large majority of samples were classified as AR or No-AR with all kSAS gene models in agreement (81 out of 100). Given the large agreement in model predictions, to minimize false positive AR predictions, if three or more disagreeing calls (∼1/4 of the models) occurred, the sample was classified as indeterminate, otherwise the sample was predicted to be high risk for AR , or low risk for AR  . Among perminate . The com p<0.001 . The AUC p<0.001 .Clinical availability of molecular tests for accurate disease diagnosis and patient stratification for treatment regimens is a pressing issue in medicine. Molecular classification of acute transplant rejection independent of the invasive biopsy procedure is a much needed clinical assay In the present study we developed a simple yet sensitive and specific noninvasive QPCR test, kSORT, together with a novel algorithm, kSAS, for the detection and prediction of acute renal allograft rejection from a PB sample, irrespective of the rejection grade or pathological diagnosis of antibody- or cell-mediated rejection. kSORT accurately indicated the risk and prevalence of acute renal rejection in 558 PB samples from 436 adult and pediatric renal transplant patients based on the expression of 17 genes . The kSORT assay performance was independent of age, time post- transplantation, transplant-specific patient management, and sample processing. The kSAS algorithm, which correlates the expression of an individual patient sample to multiple gene AR and No-AR reference profiles across multiple overlapping gene models, was successful at classifying samples with known sample handling variables.n = 279; ages 1–21 y) as part of an NIH-funded randomized trial in 12 US centers The approach we applied here combined our previous findings in pediatric transplant patients in a clinical trial setting with a novel discovery in adult and pediatric transplant patients from a “real-life” setting. We advanced our recent transcriptional panel of ten genes previously shown to be differentially regulated in blood from patients with AR in different solid organs  (93.21%) for detecting AR in a PB sample. The only PB diagnostic test that is currently available for detecting transplant rejection in transplant patients A constraint of the kSORT assay in its current assay design is its inability to discriminate between TCMR and ABMR in our study. Two potential explanations could account for this observation; on the one hand, a likely link lies in a common finding of monocyte activation in both types of AR, as the kSORT genes were highly enriched in monocytes. Indeed, monocytes are critical cells that can directly mediate the injury of tubulitis in TCMR in the allograft; additional evidence exists that donor-specific HLA class I antibodies in ABMR activate endothelial cells, which in turn can recruit and activate monocytes and leukocytes, increasing graft inflammation n = 11 genes) in tissue across heart, liver, lung, and kidney allografts Similar to our recent observation of a common rejection module Click here for additional data file.Figure S2Discovery of kSORT.(DOCX)Click here for additional data file.Figure S3Performance of kSORT by transplant center.(DOCX)Click here for additional data file.Figure S4kSORT detects antibody- and cell-mediated acute rejection; kSORT is independent of time post-transplantation.(DOCX)Click here for additional data file.Figure S5The biological basis of the 17 genes.(DOCX)Click here for additional data file.Table S1kSORT performance.(DOCX)Click here for additional data file.Table S2The exact number of samples and patients used from each of the participating centers in the AART study.(DOCX)Click here for additional data file.Checklist S1STARD checklist.(DOCX)Click here for additional data file.


An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium concept in game theory, are sometimes too broad and at other times ‘overlook’ good solutions to fundamental social dilemmas and coordination problems. The authors propose a new theory of social decision-making—virtual bargaining—in which individuals decide among a set of moves on the basis of what they would agree to do if they could openly bargain. The core principles of a formal account are outlined (vis-à-vis the notions of ‘feasible agreement’ and explicit negotiation) and further illustrated with the introduction of a new game, dubbed the ‘Boobytrap game’ . In the first empirical data of how individuals play the Boobytrap game, participants' experimental choices accord well with a virtual bargaining perspective, but do not match predictions from a standard Nash account. Alternative frameworks are discussed, with specific empirical tests between these and virtual bargaining identified as future research directions. Lastly, it is proposed that virtual bargaining underpins a vast range of human activities, from social decision-making to joint action and communication. Where this is so, it appears that, to determine whether any particular action will successfully achieve an agent's goals depends on inferring what the other agents will do. Yet a moment's reflection reveals that inferring the likely actions of other agents appears problematic. Any of the agents may reasonably wonder: ‘How can the other agents possibly infer what I'm going to do, when I don't yet know myself?’ There seems to be the danger that each agent may be ‘lost in thought’ forever, attempting to puzzle out what the others will do.One way out of this puzzle is to abandon the attempt to think in terms of goals at all. So, for example, it is widely assumed that many aspects of group behaviour in animals emerge from the application of algorithmic rules, partially determining the action of a given animal, based on the actions of nearby animals. For instance, a variety of models of flocking show that a coherently moving flock may emerge from very simple local rules for adjusting the flight of each bird in the light of the movements of neighbouring birds –3; and mBut if our focus is the theory of goal-directed behaviour, then a different approach is required. We need to face, squarely, the question of how several agents may attempt to pursue their own goals, in the knowledge that the consequences of their actions may be mutually interdependent. By far the most well-developed approach to this problem stems from the notion of Nash equilibrium and best-reply reasoning, developed in game theory.a) game, there is just one Nash equilibrium of  in which both players ‘Defect’, even though it would be better for both players to attain  by playing ‘Cooperate’. Mutual cooperation  is not a Nash equilibrium because either player can unilaterally switch from C to D and gain an improved pay-off (3 rather than 2). But  is a Nash equilibrium, because neither player can unilaterally improve their pay-off by switching .In a Nash equilibrium, each agent makes a ‘best response’ to the action of the other agent—where ‘best’ is determined entirely by the agents' own pay-offs. For example, in a social interaction with the structure of the canonical Prisoner's Dilemma PD; a game, tb). Each player must choose between Heads and Tails. One player wins if both choose identically; the other player wins if both choose differently from each other. No set of deterministic choices constitute a Nash equilibrium in this game, because the losing player is clearly not responding best to the other's move; indeed, the losing player can unilaterally switch to become the winner by changing their response. But Matching Pennies does have a mixed strategy Nash equilibrium: each player chooses Heads and Tails with 50% probability, literally by spinning a coin.So far we have considered ‘pure’ Nash equilibria, but many games also have ‘mixed strategy’ equilibria, where one or more players choose their actions probabilistically, rather than deterministically. For example, consider the Matching Pennies game b. Each pc) in which, unlike PD or Matching Pennies, the players' interests align, rather than conflict. Here, there are two ‘pure strategy’ equilibria of  and  .Nash ,7 famousfigure 1selecting one equilibrium among others. The notion of a Nash equilibrium needs to be ‘refined’, to pick out one such equilibrium over the others.In social interactions that can be modelled as a game with multiple equilibria, producing predictions about what players should choose requires actually send such messages—it is in this sense that the bargaining is ‘virtual’). To provide some initial motivation for this approach, consider another coordination game, Hi–Lo , where e). That is, imagine players who have to bargain concerning whether they should choose an option which has mutually good pay-offs (9 and 10 units) or one which has very asymmetrical pay-offs (12 and 1 units). Both players know that it is very unlikely they would mutually agree to choose the second, as the disadvantaged player will never concur. Thus, according to virtual bargaining, both players in this Battle-of-the-Sexes game will know that this is true, know that the other knows it, etc.; and hence both will spontaneously choose A, not B, alighting on the  equilibrium.We will consider how this can be modelled more formally below. But for now, let us provide some intuitions favouring the virtual bargaining approach. In particular, note that the account presumes that factors that would influence actual bargaining will tend to influence the virtual bargaining, which might involve the ‘mental simulation’ of an actual bargaining process. In real bargaining, for example, consider how one would react when faced with the two pure strategy equilibria in the ‘Battle-of-the-Sexes’ game e. That if, which provides an example where the summed pay-offs are so unfairly divided that they would not be agreed by real bargainers. The virtual bargaining account accordingly predicts that this equilibrium would not be selected.So far, one might wonder whether, rather than engaging in bargaining, both players are simply choosing the equilibrium of the greatest summed pay-offs. For example, rather than seeing the players as bargaining to achieve a solution, we might consider the players to reason instead as part of a ‘team’ whose objective is to maximize the total pay-off to the team members see ,9). Howe. Howe9])figure 1same item , then this may be a particularly natural suggestion for the ‘virtual agreement’ equilibrium [The virtual bargaining account, as developed here, makes predictions based on the ‘goodness’ of virtual bargains, not how such bargains are arrived at. Thus, it is a type of equilibrium explanation, in the tradition of the Nash equilibrium and its refinements. But understanding the psychological processes that lead to a particular virtual bargaining outcome remains an important challenge. Indeed, a process-model may turn out to have important implications for the equilibria attained. For example, in games where people are rewarded when they independently choose the  colour; ,11), comilibrium . Thus, c2.Thus far, it has appeared that the notion of a Nash equilibria is too broad. Interesting social interactions often have multiple Nash equilibria and require additional constraints for selecting one equilibrium above the others. Virtual bargaining provides one way to do this, among many others , and which we will explore experimentally below. The Boobytrap game is a modification of PD, in which an additional ‘move’ is added: the ‘boobytrap’. If a player chooses Boobytrap (B), their pay-offs will be precisely the same as if they had chosen Cooperate (C), except for a small reduction in each pay-off that can be conceptualized as the cost of ‘buying’ the boobytrap. Moreover, playing B is the same as playing C with respect to its effect on the other player's outcomes, with one crucial exception: if the other player chooses Defect (D), then that player receives a steep negative pay-off.We illustrate this type of case by introducing a new game, which we call the ‘Boobytrap game’ shown in a, and whThe Boobytrap game has just one Nash equilibrium of . The cost of ‘buying’ the boobytrap means that B yields a slightly lower pay-off than C, regardless of the action of the other player. Therefore, no pair of strategies involving B from either player can possibly be a Nash equilibrium, because switching from B to C will always be a better response to the other's move—whatever that move might be. By this logic, the players can infer that neither of them will choose B; and hence the game collapses back into a standard PD game, with the usual suboptimal  equilibrium.unenforceable. Assuming no altruism or similar, each player knows that, if they go through with their side of the bargain, the co-player may exploit them by Defecting—that is, the other player will be able to profit at their expense. But no such exploitation is possible for . Each player knows that if they go through with their side of the bargain, playing B, then no exploitation is possible; if the other plays D, then this will be mutually damaging. If the other plays B or C, then the first player obtains a good pay-off in either case.But there is a better ‘bargain’ to be had: . Consider, for a moment, that the players can bargain face-to-face. They can immediately see that  is a better outcome than . Now  is better still, but the problem is that it is So, there is a possible, non-exploitable bargain that has better pay-offs for both players than the Nash equilibrium. By playing B, a player deters the other from playing D. And hence the social interaction works to the benefit of both players. As both players know that this bargain is mutually advantageous, they can play it without having to explicitly bargain.Here, then, the virtual bargaining account diverges sharply from most accounts which assume the Nash equilibrium as a starting point; in this game, a good virtual bargaining solution may not be a Nash equilibrium at all. We shall examine how people actually play the Boobytrap game presently.The possibility of virtual bargaining between antagonists marks the division between the virtual bargaining account and an important set of related ideas developing the notion of ‘team-reasoning’  and the 3.feasible agreement picks out a set of possible ‘virtual bargains’, from which the players choose. The second step is the claim that, except in unusual circumstances which we shall not consider here, virtual bargaining is governed by the same principles that govern explicit negotiation.The theory of virtual bargaining can be made precise in a number of different ways. Here, we provide a sketch of the structure of such a theory, leaving technical developments and alternative analyses for future research. There are two core ideas. First, the notion of a feasible, if neither player can exploit the other to their own advantage. That is, given the strategy of the other, neither player can, by varying their strategy, gain advantage for themselves, to the disadvantage of the other.(i) A pair of strategies  is The rudiments of a formal account can be encapsulated as follows, for the case where there are two agents. in any way at all; this immediately implies, of course, that neither player can unilaterally change their strategy to exploit the other .Note that the set of feasible strategies is weaker than, and contains all, Nash equilibria. At a Nash equilibrium, neither player can unilaterally change their strategy to their own advantage (ii) The goodness of a feasible bargain is, following Nash's theory of bargaining, the product of the utility gains to each player (relative to a no-agreement baseline) of adhering to that agreement .How can the ‘goodness’ of a bargain be evaluated? In the absence of a well-developed formal theory of explicit negotiation (and perhaps with the expectation that such theory may not be possible), we should similarly not expect a complete formal theory of virtual bargaining. Nonetheless, we shall see that existing formal accounts of explicit bargaining, such as Nash's theory of bargaining , while iare feasible, including the pure strategies ,  and, of course, the Nash equilibrium .Let us illustrate these steps by considering PD and then its modification, the Boobytrap game. For conventional PD, consider cases in which both players agree to adopt strategies with a non-zero probability of C. Then each player can exploit the other by not going through with their side of the bargain, but by playing D with probability 1. So no such pairs of strategies are ‘feasible’. Conversely, all strategies in which one player plays D with probability 1 negative expected pay-off for the second player compared with the ‘baseline’ of also playing D. So the product of utility gains of the pair of players for any such bargain will be negative. The bargain  will have a 0 gain over this default option  is the default option for each player). So the ‘best’ feasible option is . So bargaining does not lead to an improved outcome: the theory of virtual bargaining does not, directly at least, help explain why people so frequently cooperate in one-shot PD.Note that, by choosing D, each player can guarantee a pay-off of 20 units. But any bargain in which one player chooses D, but the other has a non-zero probability of playing C, will have a a, the pay-offs are . Can bargaining lead to a better outcome? It turns out that this is possible. Suppose each player ‘buys’ a Boobytrap for certain. The pay-offs are now , and this is a feasible bargain, as neither player can exploit the other. To be sure, if one player knows that the other will play Boobytrap, then the first player can simply cooperate, thereby saving the ‘cost’ of the Boobytrap . But this leads to no disbenefit to the other player, who obtains pay-off 29 nonetheless. So  is a feasible bargain , and it is a better bargain for both players than that achieved by the Nash equilibrium.Now let us consider the Boobytrap game. The game also has a single  equilibrium, which we shall take as the default outcome, where no bargaining occurs. In the specific Boobytrap game in figure 2p − 100(1 − p), where p is the probability of playing C) is less than from playing C (i.e. 30), so that there is no incentive to play D. Simple algebra reveals that this holds when p < 13/14, and hence the probability of playing B is at least 1/14. Under this solution, expected pay-offs for each player are very nearly 30, and far better than the  pay-offs obtained in the Nash equilibrium.Indeed, the players can do even better. They just need to buy the Boobytrap sufficiently often that the other player has no incentive to Defect. It turns out that the best possible bargain for each player in the specific boobytrap game we are considering is achieved when each player plays B with probability  1/14; and otherwise plays C. This figure is obtained simply by considering the fraction of Bs required of a B/C mixture such that the co-player's pay-off by playing D M = 21.4, s.d. = 2.1) were recruited from the University of Warwick through an online participant recruitment panel. Participants were paid £5, in addition to any earnings  determined proportionally from their pay-offs in one randomly selected game.Eighteen undergraduate and postgraduate students ,  and  equilibria) over games, whereas Nash equilibrium theory predicts the ‘CD’ outcome  or  equilibria; see also discussion below). Therefore, these ‘reduced’ versions of the Boobytrap game capture relevant dimensions of interest in the full Boobytrap version, while also making contrasting predictions from Nash and virtual bargaining perspectives.The specific pay-off values from the Boobytrap game matrix in figure 2z-Tree (Zurich Toolbox for Readymade Economic Experiments) software [Using the software  to preseRow gets 40’, ‘Column gets 10’). To control for position effects, the locations of strategies in the 2 × 2s were randomized and counterbalanced for each subject, so that on any given trial, each subject saw a different configuration of the same matrix. To avoid label effects, participants did not see strategy labels  for any matrices, but instead clicked on unlabelled buttons alongside each column or row to indicate their selection. Participants made their choice without knowledge of the other player's decision and received feedback on their screen regarding the outcome of their joint decision after each game.The four 2 × 2 matrices were each presented 20 times, in random order, for a total of 80 games. This was then followed by 10 presentations of the full 3 × 3 matrix. To make the pay-offs easier to read, some minimal text was added within cells (e.g. ‘(b)The mean rates for choosing the various strategies per game were calculated for each participant and then averaged to obtain the group means displayed in . Because C should clearly be the preferred choice in this context, participants’ selections provide a basic check that they understood how to interpret the abstract matrices. For Matrix 2, participants chose the Defect, over Cooperate, strategy a vast majority of the time (92.5%); thus, without the Boobytrap strategy, the baseline rate of defection for the standard PD game is very high. For the reduced versions of the Boobytrap game in Matrix 3 and 4, this preference reverses; when deciding between C or D in the context of the other's C or B, participants chose Cooperate 86.1% of times. Further, when deciding between C or B in the context of the other's C or D, participants chose Boobytrap about a third of the time .As displayed in t17 = 2.79, p = 0.01); but the mean rate of boobytrapping does not differ (p = 0.21). There are no significant changes evidenced for the other game matrices, including the full 3 × 3 Boobytrap game. Thus, even with the change noted above, performances in boobytrap games do not conform to standard Nash predictions throughout (more on this below). Importantly, for the boobytrap matrices, defection is low and boobytrapping is present from the onset . As none of the individual choice patterns in this section change when considering experimental halves separately, we report subsequent analyses across the whole of the experiment.To check whether potential learning during the experiment may have influenced results, we recomputed the means displayed in ps > 0.05. This resulted in similar group frequencies to the reduced versions  and indicates that neither the mere availability of a third option as such, nor the added complexity of the matrix in 3 × 3 form are principally driving participants’ results. Yet, there was a significant difference in defection across Boobytrap games (reduced and full versions) and PD games in F2,34 = 199.59, p < 0.001), with planned comparisons revealing a dramatic reduction in the proportion of defection from PD to that in the reduced Boobytrap game (when constrained to choices C and D), CI95 = 0.66 to 0.92, p < .001, and from PD to the full Boobytrap game, CI95 = 0.70 to 0.98, p < 0.001.Accordingly, the overall distribution of choices in the full, symmetric version of the Boobytrap game (Matrix 5) also shows a high proportion of Cooperate and Boobytrap strategies relative to Defect: 69.4% C, 21.7% B and 8.9% D. Individuals' selection rates for each of these strategies did not differ from their rates for the strategies' counterparts in the reduced Boobytrap games shown in Following §3, virtual bargaining predicts a mixed strategy of ‘cooperative’ choices (C and B) in which B is played sufficiently frequent so as to deter the other player from D. For example, consider Matrix 3. Row should only need to play the Boobytrap strategy with some probability infinitesimally above 1/14 to rationally deter the Column player from Defect. As boobytrapping increases above this frequency, the expected utility to the Column player from choosing D decreases and defection becomes increasingly disadvantageous. The same holds true for Matrix 4, but with roles reversed. And indeed, in our experimental Boobytrap games, participants did choose B at rates well above the threshold of ≈7%. Strategy D was correspondingly chosen the least often, at rates representing more than a 75% decline from those in the standard PD game.CD outcome in the reduced boobytrap matrices; C dominates B for the player with these available strategies (i.e. C provides better pay-offs to B irrespective of what the other player chooses), whereas the other player's best reply to their opponent's expected C is D. So when constrained between C and B, a standard Nash account predicts that one always chooses a pure strategy of C; when constrained between D and C, a Nash account predicts that D should always be chosen. However, this is not observed in participants’ choices from t17 = 4.41, p < 0.001). And when deciding D or C, participants chose C significantly more than D , in direct contrast to the Nash equilibrium prediction.Alternatively, Nash equilibrium theory predicts the 5.We have focused on the contrast between our virtual bargaining account of the Boobytrap game and the standard Nash equilibrium in the game: . But there are, of course, many other models in game theory that might be applied to the Boobytrap game.One approach is to enrich the agent's utility functions to include ‘social preferences’  by assumoutcomes received by each player that matter, then the process by which those outcomes are achieved should not be important. If the first player sufficiently prefers the  outcome to the  outcome , then that player will choose B rather than C.If this is correct , then the decision to choose the Boobytrap can also be maintained if the choice of the other player is randomly determined. Specifically, suppose that in the full 3 × 3 Boobytrap game, the co-player's move is determined by flipping a coin, such that the co-player chooses C or D with probability 1/2 and is constrained to not play B. If it is the By comparison, though, completely different results are expected from a virtual bargaining viewpoint. A player cannot form a virtual bargain with a second player whose behaviour is being determined by a random device, as the second player is in no position to implement any virtual agreement. Hence, other things being equal, the virtual bargaining account would predict that the frequency of the non-random player deciding to choose B would be substantially reduced under these circumstances. This contrast in predictions thus provides the basis for a strong empirical test between the present virtual bargaining account and accounts based purely on outcomes, which awaits future experimental work.best response (i.e. the response yielding the maximum expected utility), given the probability distribution over the other's moves. Rather, players are more likely to choose a response to the extent that it has a high expected utility. Depending on a free parameter, which determines the noisiness of responding, QRE's predictions range from completely random responding (with each response having a probability of one-third for both players) to collapsing back to the Nash equilibrium as the level of decision noise tends to zero.A second alternative way to explain the choice of B in the Boobytrap game is as a result of ‘noise’ in the choice process. For example, a popular generalization standard Nash equilibrium is so-called Quantal Response Equilibrium (QRE ), accordThus, with sufficient noise, QRE can predict B is played fairly frequently—and where it is played frequently enough to successfully deter D, then D will be suppressed, as observed in our experiment. Note, though, that QRE makes the very strong prediction that the frequency of C responses must always be greater than the frequency of B responses. This is because C has a higher pay-off than B whatever the other player does; that is, C dominates B, and frequencies are positive monotonic functions of expected pay-off.Now consider the Boobytrap game in By contrast, QRE predicts a very different pattern. Because B is constrained to be less frequently chosen than C, it cannot be chosen sufficiently often to be an adequate ‘deterrent’ to the choice of D. So D will be the most frequent choice, followed by C, and finally B. Exploring what happens when the boobytrap is less ‘potent’ is thus a critical test between QRE (and noise-based models more broadly) and the virtual bargaining account, and the natural topic for future experimental work.6.In humans, whether we achieve our goals often depends not only on our own actions, but on the actions of others. In such cases, successful goal-directed behaviour often requires two or more people to coordinate their actions successfully, whether in passing each other in the corridor without collision, playing a duet or a game of football, collaborating on a research project or a business venture, or dividing up and carrying out tasks within a family unit. The theory of virtual bargaining aims to provide the starting point for an account of how this is possible.We have considered virtual bargaining in cases where the goals of the players may be diametrically opposed . But virtual bargaining is also applicable in contexts where players have common goals, but the challenge is to infer how to coordinate their behaviour to achieve those goals. Arguably, such problems of coordination are central to a vast range of human activities, in which people must jointly attend to the same information, jointly plan how to react to that information and jointly engage in appropriate actions—as exemplified in a range of activities including conversation, collaborative musical or theatrical performance, many types of improvised dance and collaborative projects from putting up a tent to writing an academic paper.As noted earlier, a particularly simple example of such coordination games is the Hi–Lo game, in which players must independently choose to respond H (‘Hi’) or L (‘Lo’). Recall that if the players choose different options, neither obtains any reward. However, if the players both choose Hi, they both receive a large prize, and conversely, if they both choose Lo, they both receive a small prize. Experiments consistently show, as intuition would expect, that Hi–Hi is overwhelmingly chosen .A full evaluation of the virtual bargaining theory would, of course, require a much broader review of the experimental literature on games, and this would go far beyond the scope of this paper. We note, though, that the scope of the present account is quite broad. Consider, for example, the Ultimatum Game. In one version, Player 1 announces a ‘split’ of, say, $100; Player 2 announces a ‘minimum’ sum to which they will agree. If Player 1's split is less than Player 2's minimum, neither player gets anything. Otherwise, the players receive the split as suggested by Player 1. Under fairly natural symmetry assumptions, the best bargain (judged by Nash bargaining) will be a 50–50 split; and virtual bargaining then suggests that this bargain will be implicitly ‘agreed’ by the players. Indeed, the 50–50 split is the modal outcome in many experiments. This provides an alternative, or perhaps complement, to standard economic accounts based on fairness . A naturIt is natural to view communication itself as a coordination game of the type above, but where both sender and receiver of a communicative ‘signal’ must coordinate on the same interpretation of that signal, within the space of a vast number of possible interpretations. Facial expressions, gestures and utterances all, notoriously, hugely underspecify the content that is to be communicated. Only by extremely complicated processes of ‘pragmatic enrichment’ is it possible to interpret a raised eyebrow as, perhaps, expressing the implausibility of a specific philosophical position, or to interpret a brief shudder as a request to close a draughty window . Communisame interpretation as each other. This contrasts with animal signalling such as ‘stotting’ where a gazelle's jumps in the presence of lions may signal its fitness [pointing to a defender only gives information about the likely pass if the point is interpreted by the defender as communicative , rather than, say, as a random wave of the arm. Empirically clarifying the boundary between cases where signal is sent intentionally, which can be very intricate [common knowledge of the intention of the signal is an important topic for future work. The present approach can be viewed as a step towards providing a formal underpinning for the theory of psychological communication developed by Clark and co-authors [Note that, in the current framework, communication requires common knowledge that the action to be interpreted is communicative: both sender and receiver aim to infer the  fitness  and hencntricate , and tho-authors ,43,44 an-authors .We see the foundations of understanding human communication as a particularly rich area for the application of the theory of virtual bargaining. Moreover, if we are right, then virtual bargaining underpins the ability to bargain explicitly, using linguistic or other modes of communication; without virtual bargaining, we suggest, communication, and the complex cultural structures built upon it, would not be possible.


CCK-AR  polymorphism is associated with a defective splicing of the primary transcript of CCK-AR mRNA, which may result in the lower expression of the CCK-AR. Therefore, we evaluated the role of genetic polymorphism of CCK-AR gene  in FD.Functional dyspepsia (FD) is characterized by epigastric pain, burning, early satiety and post-prandial fullness in absence of organic or metabolic causes. Cholecystokinin receptor-A (CCK-AR) is known to modulate satiety signal and delay gastric emptying, which are associated with FD. CCK-AR gene polymorphism (PCR-RFLP). Patients with FD were sub-classified into epigastric pain syndrome (EPS), post-prandial distress syndrome (PDS) and EPSPDS overlap.237 consecutive patients with FD (Rome III) and 250 healthy controls (HC) were genotyped for CCK-AR (rs1800857) was infrequent among patients than HC . However, genotypes distribution was comparable among patients with different subtypes of FD (p=0.44).Patients with FD  were comparable with HC  with respect to age and gender. 26/237 (11%) had EPS, 55 (23.2%) PDS and 156 (65.8%) EPSPDS overlap. Among 237 patients with FD, CC (variant) genotype of CCK-AR polymorphism is protective for FD. EPSPDS overlap was common among patients with FD.CC genotype of


In this study, we present PAIDB v2.0, whose functionality is extended to incorporate REIs. PAIDB v2.0 contains 223 types of PAIs with 1331 accessions, and 88 types of REIs with 108 accessions. With an improved detection scheme, 2673 prokaryotic genomes were analyzed to locate candidate PAIs and REIs. With additional quantitative and qualitative advancements in database content and detection accuracy, PAIDB will continue to facilitate pathogenomic studies of both pathogenic and non-pathogenic organisms.Pathogenicity is a complex multifactorial process confounded by the concerted activity of genetic regions associated with virulence and/or resistance determinants. Pathogenicity islands (PAIs) and resistance islands (REIs) are key to the evolution of pathogens and appear to play complimentary roles in the process of bacterial infection. While PAIs promote disease development, REIs give a fitness advantage to the host against multiple antimicrobial agents. The Pathogenicity Island Database (PAIDB,  Escherichia coli and Hrp PAI in Pseudomonas syringae), superantigen (e.g. SaPI1 and SaPI2 in Staphylococcus aureus), colonization factor (e.g. VPI in Vibrio cholerae), iron uptake system (e.g. SHI-2 in Shigella flexneri) and enterotoxin (e.g. espC PAI in E. coli and she PAI in S. flexneri). PAIs confer virulence upon the recipient, resulting in the dissemination and diversification of bacterial pathogens  resulted in the emergence of methicillin-resistant S. aureus  is associated with the multiple-drug-resistant form of Salmonella typhimurium (Pseudomonas aeruginosa genomic island 1 (PAGI-1) is found in the majority of clinical isolates  are another class of GIs that are linked to pathogenesis by conferring simultaneous resistance to multiple antibiotics and facilitating the emergence of multidrug-resistant pathogens –8. For e. aureus . The Salhimurium . Pseudomisolates . AbaR1 w decades .Pathogenomic studies necessitate specialized data resources related to pathogens. Public database servers have been developed for searching virulence factors  and contains at least one virulence gene homolog from the PAI loci ,25. If aGenBank accession numbers for PAI and REI loci were collected via an exhaustive search of GenBank and academic literature using a variety of terms related to ‘pathogenicity island’ and ‘resistance island’ (Supplementary Table S1). We also added PAIs and REIs that were reported in genome sequencing papers in a GenBank-like flat file format (Supplementary Table S2). Via expert review, we collected 223 types of PAIs, consisting of 1331 accessions for complete or partial PAI loci previously described in 804 pathogenic bacterial strains. Similarly, we collected 88 types of REIs with 108 accessions from 99 bacterial strains  had been downloaded from the NCBI FTP server (Supplementary Table S3). To determine the pathogenicity of the retrieved organisms, we referred to related publications and to the Genomes Online Database (GOLD) (To detect candidate regions in genome sequences, we modified the method previously described in  Figure . In a giP-value for codon usage deviation was calculated assuming a normal distribution of codon frequencies, which was later suggested to be suboptimal (GIs are a heterogeneous class of mobile elements that contain a large collection of genes acquired by HGT. Various methods have been suggested for their detection in microbial genomes . In the boptimal . Hence, boptimal , which mboptimal , which uboptimal  and wereboptimal . HGT regboptimal .In our detection scheme, the presence of virulence- or resistance-related genes is a crucial criterion to identify candidate regions in a genome Figure . We tagghttp://www.paidb.re.kr. The web-based database was redesigned to offer a user-friendly graphic interface with clear visualization of PAIs, REIs and candidate regions in bacterial genomes. The organization of the website follows the previous version of PAIDB (PAIDB is freely accessible at of PAIDB . The webof PAIDB . The menof PAIDB . The cirThe ‘Search’ menu enables users to retrieve PAI and REI data stored in PAIDB through text- and homology-searches Figure . Along wIn addition to discovering candidate PAI regions in query sequences, ‘PAI Finder’ was modified to also locate candidate REI regions. The overall detection scheme follows Figure Virulence factors and resistance factors are over-represented in large mobile genetic elements of PAIs and REIs present in bacterial pathogens ,5,34. PAP. syringae (S. typhimurium (E. coli (S. typhimurium (P. aeruginosa (A. baumannii (GIs are hotspots for the stepwise insertion of different genetic fragments carrying virulence and resistance determinants . PAIs ofsyringae , SPI-2 ihimurium  and PAI (E. coli . This ishimurium , PAGI-1 ruginosa  and AbaRaumannii . We haveaumannii . It shouaumannii  (Figure aumannii . The algaumannii . In thisaumannii , potentiVirulence factors involved in bacterial pathogenesis are often found in genomes of non-pathogenic bacteria ,41. CompAs the number and diversity of sequenced microbial genomes rapidly accumulate, this web-based, user-friendly resource will continue to contribute to the investigation of genomic regions related to pathogenicity and to give insight into the evolution of pathogenesis. We envision that PAIDB will be of significant use in detecting PAIs and REIs in newly sequenced genomes and mining virulence determinants from metagenomic analyses. Furthermore, as a unique resource for experimentally verified and computationally predicted PAIs and REIs, PAIDB should be particularly useful to design clinical biosensors for pathogen detection and infectious disease diagnostics. PAIDB will continue to incorporate newly discovered PAIs and REIs in a timely manner to keep pace with the rapidly developing field of pathogenomics.Supplementary Data are available at NAR Online.


Triple negative breast cancer (TNBC) is a heterogeneous collection of biologically diverse cancers, which contributes to variable clinical outcomes. Previously, we identified a TNBC subtype that has a luminal phenotype and expresses the androgen receptor (AR+). TNBC cells derived from these luminal AR + tumors have high frequency phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations. The purpose of this study was to determine if targeting phosphoinositide 3-kinase (PI3K) alone or in combination with an AR antagonist is effective in AR + TNBC.We determined the frequency of activating PIK3CA mutations in AR + and AR- TNBC clinical cases. Using AR + TNBC cell line and xenograft models we evaluated the effectiveness of PI3K inhibitors, used alone or in combination with an AR antagonist, on tumor cell growth and viability.PIK3CA kinase mutations were highly clonal, more frequent in AR + vs. AR- TNBC (40% vs. 4%), and often associated with concurrent amplification of the PIK3CA locus. PI3K/mTOR inhibitors had an additive growth inhibitory effect when combined with genetic or pharmacological AR targeting in AR + TNBC cells. We also analyzed the combination of bicalutamide +/- the pan-PI3K inhibitor GDC-0941 or the dual PI3K/mTOR inhibitor GDC-0980 in xenograft tumor studies and observed additive effects.While approximately one third of TNBC patients respond to neoadjuvant/adjuvant chemotherapy, recent studies have shown that patients with AR + TNBC are far less likely to benefit from the current standard of care chemotherapy regimens and novel targeted approaches need to be investigated. In this study, we show that activating PIK3CA mutations are enriched in AR + TNBC; and, we show that the growth and viability of AR + TNBC cell line models is significantly reduced after treatment with PI3K inhibitors used in combination with an AR antagonist. These results provide rationale for pre-selection of TNBC patients with a biomarker (AR expression) to investigate the use of AR antagonists in combination with PI3K/mTOR inhibitors.The online version of this article (doi:10.1186/s13058-014-0406-x) contains supplementary material, which is available to authorized users. Over the past decade, the term triple-negative breast cancer (TNBC) has been used to classify tumors that lack detectable expression of the estrogen receptor (ER) and progesterone receptor (PR) and amplification of human epithelial growth factor receptor 2 (HER2). TNBC tumors are generally more aggressive than their ER + counterparts, with higher rates of relapse in the early stages and decreased overall survival in the metastatic setting ,[2]. AltPIK3CA (10.2%), the gene that encodes the p110α catalytic subunit of phosphatidylinositol-3 kinase (PI3K). Through integrated analyses of numerous world-wide gene expression (GE) datasets and a panel of TNBC lines, our laboratory provided insight into the heterogeneity of TNBC disease by identifying distinct molecular subtypes displaying unique biology that includes two basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype . TP5. TP56].  subtype  and AR . Th. Th23]. in-situ hybridization (FISH), and age) and corresponding androgen receptor (AR) protein and mRNA levels and PIK3CA mutation status. (XLSX 59 KB)Additional file 1: Table S1.: Triple-negative breast cancer (TNBC) molecular subtyping of The Cancer Genome Atlas (TCGA). Table displays TCGA patient barcode with TNBC subtype with clinical parameters (estrogen receptor (ER), progesterone receptor (PR), human epithelial growth factor receptor 2 (HER2) immunohistochemistry (IHC), HER2 fluorescent A) Panels display the frequency of PIK3CA mutations (H1047R) in both DNA and RNA sequencing reads from the same tumor (The Cancer Genome Atlas (TCGA)-CE-A12L). (B) Panels display RNA sequencing reads from two additional AR + TNBC tumors with PIK3CA mutations. (PDF 1 MB)Additional file 2: Figure S1.: PIK3CA mutations have a high clonal frequency in androgen receptor (AR) + triple-negative breast cancer (TNBC) tumors.  from The Cancer Genome Atlas (TCGA) breast cohort. Vertical color bars indicate androgen receptor (AR) + triple-negative breast cancer (TNBC) (green) and tumors in which PIK3CA is also mutated (blue). (PDF 869 KB)Additional file 4: Table S2.: Linear normalized median-centered (log2) reverse-phase protein array (RPPA) values for triple-negative breast cancer (TNBC) cell lines. Table shows median-centered RPPA values for 179 proteins for each of the cell lines, performed in duplicate. (XLSX 195 KB)Additional file 5: Figure S3.: Triple-negative breast cancer (TNBC) cell lines express both androgen receptor (AR) and p-AKT by immunofluorescence. Immunofluorescent images of MFM-223 (top) and SUM-185 (bottom) co-stained for AR (red), p-AKT(S473) (green) and counterstained with DAPI (blue). Altered cellular morphology is due to cytospin preparation. (PDF 1 MB)Additional file 6: Figure S4.: Triple-negative breast cancer (TNBC) cell lines express both androgen receptor (AR) and p-AKT by immunofluorescence. Immunofluorescent images of MDA-MB-453 (top) and CAL-148 (bottom) co-stained for AR (red), p-AKT(S473) (green) and counterstained with DAPI (blue). Arrows indicate a percentage of AR- cells that stain positive for p-AKT. (PDF 1 MB)A) FACS scattergrams for AR + triple-negative breast cancer (TNBC) cell lines incubated with alexa fluor 488 secondary antibody alone  or with anti-AR antibody (bottom panels). (B) CAL-148 cells were flow sorted into ARlow (bottom 20%) and ARhigh (top 20%) populations in which DNA was isolated and PIK3CA evaluated by Sanger sequencing. (PDF 2 MB)Additional file 7: Figure S5.: PIK3CA mutation in CAL-148 cells is present in both androgen receptor (AR) + and AR- cells sorted by fluorescence-activated cell sorting (FACS). Additional file 9: Figure S6.: shRNA targeting of androgen receptor (AR) is additive in combination with PI3K inhibitors. Line graphs display relative viability of luminal androgen receptor (LAR) cell lines transduced with nontargeting (shNT) or shRNAs targeting AR (shAR-1 and shAR-2) after 72 h treatment with the pan-PI3K inhibitor NVP-BKM-120 (top) or the dual PI3K/mTOR inhibitor NVP-BEZ235 (bottom). Data represent the average of three replicates. (PDF 865 KB)A) Line graphs show relative viability of AR-expressing cell lines treated with an increasing concentration of BKM120 (top) or NVP-BEZ235 (bottom) as single agents (blue) or in combination (red) with CDX (25 μM). Dashed black line depicts the theoretical line of additivity determined from the effect of CDX alone and either BKM120 or NVP-BEZ235 alone. Error bars represent SD for three independent experiments. (B) Immunoblots from AR-expressing TNBC cell lines treated with either CDX, BKM120 (1 μM), NVP-BEZ235 (100 nM) alone or CDX in combination with either BKM120 or NVP-BEZ235 for 24 h or 48 h analyzed for AR, p-AKT, AKT, p-S6, S6 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein. (PDF 2 MB)Additional file 10: Figure S7.: Pharmacological targeting of androgen receptor (AR) with bicalutamide (CDX) is additive in combination with PI3K inhibitors in AR + triple-negative breast cancer (TNBC). (A) Bar graphs display relative caspase 3/7 activity (RLU) normalized to viable cell number 48 h after treatment with vehicle, positive control (3 μM ADR), bicalutamide (CDX) (50 μM), GDC-0941 (3 μM) or GDC-0980 (1 μM) as single agents or in combination with CDX. Error bars represent SD for three independent experiments. (B) Representative cell cycle histograms of the MDA-MB-453 cell line treated with similar conditions as described above. (C) Bar graphs indicate percentages of sub-G1 DNA, indicative of late-stage apoptotic DNA fragmentation. (PDF 1009 KB)Additional file 11: Figure S8.: Combined inhibition of androgen receptor (AR) and PI3K increases apoptotic cell death in AR + triple-negative breast cancer (TNBC) cell lines.  and PI3K decreases viability of AR + triple-negative breast cancer (TNBC) cell lines grown in a 3-D forced suspension assay. Line graphs display relative viability of 3-D cell aggregates treated with BKM120 (top) or NVP-BEZ235 (bottom) as single agents (blue) or in combination (red) with CDX (25 μM). Dashed black line depicts the theoretical line of additivity determined from the effect of bicalutamide (CDX) alone and either BKM120 or NVP-BEZ235 alone. Error bars represent SD for three independent experiments. (PDF 1 MB)Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Authors’ original file for figure 5Authors’ original file for figure 6Below are the links to the authors’ original submitted files for images.


Available data assessing the efficacy of pulmonary rehabilitation for patients with chronic sarcoidosis are scant; for Germany, there are none at all.To gain information about the benefit of in-house pulmonary rehabilitation for patients with chronic sarcoidosis and for the health care system, we intend to collect data in a prospective multicenter “real-life” cohort trial.ProKaSaRe (Prospektive Katamnesestudie Sarkoidose in der pneumologischen Rehabilitation) [Prospective Catamnesis Study of Sarcoidosis in Pulmonary Rehabilitation] will assess a multimodal 3-week inpatient pulmonary rehabilitation program for adult patients with chronic sarcoidosis over a 1-year follow-up time. Defined specific clinical measurements and tests will be performed at the beginning and the end of the rehabilitation. In addition, questionnaires concerning health-related quality of life and the patients’ symptoms will be provided to all patients. Inclusion criteria will be referral to one of the 6 participating pulmonary rehabilitation clinics in Germany for sarcoidosis and age between 18 and 80 years. Patients will only be excluded for a lack of German language skills or the inability to understand and complete the study questionnaires. To rule out seasonal influences, the recruitment will take place over a period of 1 year. In total, at least 121 patients are planned to be included. A descriptive statistical analysis of the data will be performed, including multivariate analyses. The primary outcomes are specific health-related quality of life (St George’s Respiratory Questionnaire) and exercise capacity . The secondary outcomes are several routine lung function and laboratory parameters, dyspnea scores and blood gas analysis at rest and during exercise, changes in fatigue, psychological burden, and generic health-related quality of life .Funding was obtained on October 12, 2010; enrollment began on January 15, 2011 and was completed by January 14, 2012. Results are anticipated late summer 2015.Due to the large number of participants, we expect to obtain representative findings concerning the effectiveness of pulmonary rehabilitation for patients with sarcoidosis and to provide a dataset of assessed objective and subjective short- and long-term changes due to pulmonary rehabilitation. The results should form the basis for the planning of a randomized controlled trial.German Clinical Trials Register: DRKS00000560; https://drks-neu.uniklinik-freiburg.de/ drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00000560 (Archived by WebCite® at http://www.webcitation.org/6dKb5X87R) Sarcoidosis is a granulomatous systemic disease of still unknown etiology  that seeThe incidence and prevalence of sarcoidosis have not been precisely compiled for Germany. Estimates using data from the Bonn Sarcoidosis collective give a prevalence of approximately 44 per 100,000 inhabitants . SarcoidDepending on the affected organs, sarcoidosis may cause functional limitations and reduce quality of life. It is mostly accompanied by a pronounced fatigue -11 and mRehabilitation in Germany is a statutorily regulated part of the health care system and is only approved when social participation and inclusion, daily life, and the professional activities of the patient are restricted by illness. Access to inpatient rehabilitation is only possible following a thorough investigation by physicians specializing in social medicine. Proof that all outpatient treatment options have been exhausted must also be provided.ProKaSaRe (Prospektive Katamnesestudie Sarkoidose in der pneumologischen Rehabilitation) [Prospective Catamnesis Study of Sarcoidosis in Pulmonary Rehabilitation] is a “real-life” cohort trial and a prospective multicentric follow-up (catamnesis) study of sarcoidosis in pneumological rehabilitation. It is designed to assess the impact of a multimodal 3-week inpatient pulmonary rehabilitation on symptoms, exercise capacity, and health-related quality of life (HRQL) for adult patients with chronic sarcoidosis. ProKaSaRe has a 1-year follow-up time.We intend to rule out seasonal influences by including patients over a recruitment phase of 12 months. By assessing changes over a long period in this cohort of chronic sarcoidosis patients, so far the largest worldwide, we hope to gain reliable insights into and sound evidence of objective and/or subjective short- and long-term health changes caused by pulmonary rehabilitation. The most important questions that should be answered with the ProKaSaRe study are:What are the characteristics of patients treated in inpatient pulmonary rehabilitation?What are the short- and the long-term findings in view of symptoms, exercise capacity, and HRQL?Will the HRQL change after the patients are discharged and how will the HRQL develop in the 12 months following rehabilitation?The aim of the ProKaSaRe study is to create a solid basis of empirical real-life data on the effectiveness of pulmonary rehabilitation for patients with chronic sarcoidosis, including clinical parameters, exercise capacity, quality of life, and psychological burden via a prospective, multicentric, real-life observational study.ProKaSaRe is a prospective, multicentric observational study. All participants will be observed undergoing the usual structured, multidisciplinary rehabilitation program , which rEthical approval was granted by the Ethical Committee of the Hannover Medical School, Hannover, Germany (Nr 877).Rehabilitation clinics specializing in pulmonary rehabilitation of sarcoidosis patients that have already showed interest during a planning conference initiated by the Bad Reichenhall clinic, Bad Reichenhall, Germany, will be formally invited to participate in the trial. All centers have proven sarcoidosis expertise and the selection will be made so that a variety of differing organizational structures, including private clinics and clinics under the auspices of the German statutory pension insurance scheme will be represented.Based on the referral rate of previous years, we estimate that the participation of 6 pulmonary rehabilitation–specialized centers will be necessary to recruit a sufficient number of patients in 12 months to perform meaningful statistical data analysis.To be eligible for inclusion in the ProKaSaRe study, patients must be referred for sarcoidosis to one of the 6 participating study centers for inpatient pulmonary rehabilitation by their physicians or by the hospital doctors following acute inpatient hospital treatment. If the leading rehabilitation diagnosis of sarcoidosis is confirmed by a pneumologist expert in sarcoidosis on admission to the rehabilitation program, every patient who is younger than 80 years and aged at least 18 years will be invited to participate in the trial. On receipt of written consent, the patients will be consecutively recruited to and enrolled in the study regardless of their type of health insurance to assess a representative study population.Patients will only be excluded for poor German language skills, the inability to understand and complete the study questionnaires (due to lack of linguistic proficiency), or if no written consent can be obtained.0-T4. Defined specific medical measurements and tests will be undertaken at the beginning and the end of the pulmonary rehabilitation program and a questionnaire concerning symptoms and HRQL will be provided at different points in time during the inpatient program and the follow-up period of 12 months (0) and at discharge from the rehabilitation program (T1) [Data will be collected at 5 time points: T2 months . Clinicaram (T1) .0, the core data will be compiled comprising sociomedical status, age, sex, body mass index (BMI), severity of sarcoidosis, and organ involvement and damage. Previous treatment will also be assessed.At T0) and end (T1) of the rehabilitation program at each clinic using medical survey forms: lung function, gas exchange, physical fitness, and laboratory parameters. This includes the following measurements:The following clinical findings will be recorded by the doctors at the start (TBMI 1), specific airway resistance (sRaw), mouth occlusion pressure 0.1 seconds after onset of inspiration (P0.1), and maximal inspiratory mouth pressure (Pimax), and Tiffeneau-Index (the FEV1/VC ratio) [Lung function values: vital capacity (VC), residual volume (RV), total lung capacity (TLC), forced expiratory volume in 1 second (FEVC ratio) Gas exchange: blood gas analysis at rest and during exercise (25/50 Watt), transfer factor of the lung for carbon monoxide (Tlco) in a single breath (Tlco SB) and per alveolar volume (Tlco/Va)Physical fitness: 6MWT distance Laboratory parameters: angiotensin-converting enzyme (ACE) and C-reactive protein (CRP)0 using the Hospital Anxiety and Depression Scale (HADS-D) [0 and T1 will be submitted on site, the ones at T2 to T4 will be sent by post. The patients will be instructed to complete all questionnaires independently.The psychological burden will be also documented at T(HADS-D)  and qual(HADS-D) . The fat(HADS-D) ,29 and d(HADS-D) -32. At 31). Moreover, an evaluation of the program from the patients’ perspective will be included in the final questionnaire at T4.In addition, the patients’ views of the components of the individual rehabilitation programs and their perceived importance will be assessed at discharge  exercise capacity / physical ability investigated with the 6MWT at T1 and T2 [2max as a measure of physical ability is because of the multicentric real-life study design.As the primary outcome, the following parameters are defined: (1) HRQL determined using the SGRQ ,33,36 at1 and T2 ,37-39. T1 to T4 [1 to T4 [1 to T4 [1; and (5) an evaluation of the program from the patients’ perspective at T1.The secondary outcome parameters are: (1) fatigue, the leading symptom of sarcoidosis, investigated using the FAS at T1 to T4 ,31,40-43[1 to T4 ; (3) HRQ[1 to T4 ,44-47;  after 3, 6, or 12 months is planned as part of a RCT follow-up study.Data management will be carried out by the Centre for Public Healthcare and statistical analysis by the Institute for Biostatistics at the Hannover Medical School.The questionnaires will be anonymized using a clinic identifier and a consecutive patient number and will be digitized in duplicate at the Hannover Medical School and entered in Microsoft Access. The database tables will then be virtually superposed to identify differences between entries. When discrepancies are found, the data entry will be compared with that of the original questionnaire and corrected so that an accurate master dataset used for the descriptive analysis and the calculation of effect sizes will emerge.0 and T1 will be evaluated with a paired t test and a 2-sided type I error of 2.5%.The main analyses will be performed on the total study population. Missing values will be reported and not replaced initially. Changes in both primary endpoints between TP values will be assessed descriptively and considered to be of importance for P<.05. Therefore, no adjustment for multiplicity will be performed. Descriptive evaluation of clinical characteristics will be conducted. Changes from baseline for continuous data will be compared by paired t tests and given with 95% confidence intervals for the mean difference. Categorical parameters will be compared for T1 to T4 by chi-square test. Duration of sick leave and results of the sociomedical status documented by the physician  and by the patient , the evaluation of rehabilitation from the patients´ perspective (T1) and patient-attributed value to the components of the individual rehabilitation program will be presented exploratively. For the questionnaires, a covariate analysis will be run using participating centers and baseline values as independent variables and the change from T1 and T2 to T0 as dependent variables. Further multivariate analyses are planned.Because ProKaSaRe is an exploratory study, for all analyses except for the primary analysis, All statistical analyses will be conducted using the SAS 9.3 software  to show the impact of the rehabilitation program on the patients’ lives.The planned double data entry and the possibility of referring to the clinical files in case of runaway values and spikes concerning lung function, gas exchange, and laboratory parameters should ensure high data quality.The intervention is an inpatient rehabilitation program lasting 3 weeks. The program is modular, with obligatory and optional components. All components are available at all study centers . The reaRehabilitation is oriented according to the nature and extent of the difficulties resulting from single (sarcoidosis) or multiple illnesses (comorbidities) that the individual patient suffers that affect professional and daily life. According to these disabilities and functional deficiencies (as defined by the rehabilitation admission tests and assessments), the therapy components are individually and quantitatively adapted.Obligatory componentsSpecialist confirmation of diagnosis and checks of current medication.Endurance training: from 3 to 5 training sessions per week at approximately 60-80% of maximum capacity .Strength training: 3 medical training therapy sessions per week.Respiratory physiotherapy: at least three group sessions per week and at least two individual respiratory therapy sessions during the rehabilitation program.Patient education: modular sarcoidosis education program.Optional componentsmax. Sessions supervised at least once a week.Inspiratory muscle training: at least five sessions (each 30 minutes) per week, starting at 30% PIRelaxation treatment: autogenic training or progressive muscle relaxation.Social counseling.Nutrition counseling.Psychological individual and group therapy.Structured smoking cessation.Ergotherapy and advice on devices and equipment.Patient education: additional courses .Endurance training is intended to improve performance, shortness of breath, mood, and quality of life. It also compensates for cortisone adverse effects, such as osteoporosis, muscle degeneration, weight gain, diabetes, and high blood pressure ,48,49, aRespiratory physiotherapy supports the learning of coughing techniques, positions which make breathing easier, handling shortness of breath on exertion, stretching positions to maintain the flexibility of the bony thorax (particularly with fibrosis), correct breathing techniques, and improvements in the respiratory muscles.Patient education provides the sick person with information on the clinical picture, helps them to cope with their illness, and deal with all the information on adverse effects of their therapies . It puts sarcoidosis into perspective and helps to reduce anxiety and depression by generating feelings of informed safety. It explains the reasons for and frequencies of check-ups and helps patients to differentiate between required and unnecessary diagnostic tests. Alternative therapies and their evaluation with respect to sarcoidosis treatment could be addressed. Finally, it generates opportunities for strong interaction and bonding with other patients.Inspiratory muscle training is known to improve the strength of respiratory muscles . RelaxatSocial counseling provides advice relating to societal participation, level of disability, severe disability, career reintegration according to social statutes and the ICF, taking early retirement, and applying for benefits, etc ,52,53.Nutrition counseling will also be offered on demand, particularly with reference to the adverse effects of cortisone therapy, such as osteoporosis, increased weight, diabetes, and high blood pressure, including counseling on the subject of a healthy diet.Psychological individual and group therapy will be offered when needed. Individual therapy includes counseling and advice when anxiety, depression, insecurity, and problems coming to terms with the illness occur due to the chronic nature of the illness . StructuFunding was obtained on October 12, 2010; enrollment began on January 15, 2011 and was completed by January 14, 2012. Results are anticipated late summer 2015.Sarcoidosis is a systemic disease of unknown etiology that can affect all organs. Therefore, it presents a heterogeneous clinical picture associated with multiple comorbidities. It can occur at any age with a peak between the ages of 20 and 40, with women having a higher frequency of illness than men do, and it can show a chronic or chronic-relapsing pattern. Significant functional limitations include reduced quality of life and an often-debilitating fatigue that hinders the patients’ participation in “normal” daily life. Sarcoidosis is often insufficiently treatable by drugs alone. Can rehabilitation slow down disease progression and accelerate recovery and return to employment? ProKaSaRe seeks to find out whether and to what extent a rehabilitative intervention can substantially contribute to the care of patients with sarcoidosis.This paper outlines the rationale and design for a prospective, “real-life” multicenter study using obligatory and optional components administered by a multiprofessional team. The aim is to explore the effects of a 3-week inpatient multiprofessional pulmonary rehabilitation program. ProKaSaRe will supply lacking evidence as to the degree to which a training-based multimodal rehabilitation can produce or enhance a significant and sustained improvement to HRQL, physical fitness, and psychological distress. This is particularly relevant in light of the myasthenia and fatigue mentioned previously, which are common symptoms of sarcoidosis.During 2011, 929 sarcoidosis patients were admitted for inpatient rehabilitation in Germany . We planProKaSaRe will help to define the patient group currently being referred for pulmonary rehabilitation in Germany, while simultaneously documenting the effect of pulmonary rehabilitation in terms of clinically relevant outcome parameters, if any.The inpatient rehabilitation that will be assessed consists of a clearly defined set of components developed and approved by experts in the field accredited by the German health system following assessment of their professional development and experience as heads of specialized centers. The detailed agreed-upon choice of components and the broad assessment of HRQL reflecting the subsequent patients’ reaction to their chosen and advised measures represent the strengths of ProKaSaRe.Scientists and physicians tend to define therapeutic success by only the improvement in pulmonary function, whereas patients equate successful treatment with tangible effects in their daily lives and an improved sense of well-being. This latter ultimately determines long-lasting positive effects, as stated by the ICF . For thiAlthough ProKaSaRe could contain some bias because it assesses only the sarcoidosis population willing to undergo pulmonary rehabilitation and is not a RCT, it is nevertheless a real-life study. Therefore, it is likely that the results generated in ProKaSaRe will be highly generalizable.In contrast to studies focusing on single nonpharmaceutical components of rehabilitation programs, ProKaSaRe considers the entirety of the pulmonary rehabilitation program. The participants represent a real-life sample of sarcoidosis patients rather than a selected subpopulation thereby adding to the importance of the findings for medical and economical deliberations.If the results of ProKaSaRe show effectiveness of the rehabilitation over a long period of time, rehabilitation as documented by the study would offer perspective and justified hope to concerned patients and their families and also evidence to stakeholders in the health care system. The documented efficacy of pulmonary rehabilitation for sarcoidosis patients will fill a gap in the reference literature used to develop guidelines and efficient long-lasting treatment procedures. It may establish rehabilitation as an irreplaceable and necessary additional therapeutic option for sarcoidosis, with highly relevant outcomes, which is only effective in its entirety and is more than the sum of single administrated components.Depending on the outcome of ProKaSaRe, an RCT will be planned to advance knowledge and funding will be applied for. The definition of patient groups to which rehabilitation offers the greatest benefit and the estimation of the financial savings by the prescription of a rehabilitation program would be the next topics to be investigated.