Insecticide-treated bednets (ITNs) are recommended for use by 3.4 billion people at risk of malaria world-wide. Policy makers rely on measurements of ITN use to optimize malaria prevention efforts. Self-reports are the most common means of assessing ITN use, but self-reports may be biased in a way that reduces their reliability as a proxy for ITN adherence. This meta-analysis compared self-reported and two methods which are more objective measures of ITN use to explore whether self-reports overestimate actual ITN adherence.A comprehensive search of electronic databases and hand searching reference lists resulted in screening 2885 records and 202 articles were read in full. Sixteen articles with comparable data were chosen for the meta-analysis. Comparable data was defined as self-reported and objectively measured ITN use (observation of a mounted ITN or surprise visits confirming use) at the same unit of analysis, covering the same time period and same population. A random effects model was used to determine a weighted average risk difference between self-reported and objectively measured ITN use. Additional stratified analyses were conducted to explore study heterogeneity.Self-reported ITN use is 8 percentage points  higher than objectively measured ITN use, representing a 13.6% overestimation relative to the proportion measured as adherent to ITN use by objective measures. Wide variations in the discrepancies between self-reports and objective measures were unable to be explained using stratified analyses of variables including location, year of publication, seasonality and others.Self-reports overestimate ITN adherence relative to objectively measured ITN use by 13.6% and do so in an unpredictable manner that raises questions about the reliability of using self-reported ITN use alone as a surveillance tool and a guide for making policy decisions. Despite significant improvements in malaria control over the last 15 years, malaria continues to represent a significant burden for the world’s poorest people . The WorHundreds of millions of ITNs are distributed to households and vulnerable individuals through various mechanisms every year . ConsistAdequate tools for measuring the use of ITNs are essential for assessing the success of interventions to prevent malaria. There is reason to believe that the currently available methods for measuring ITN use are inadequate . To dateThere are a variety of ways to measure adherence to recommended ITN use for the prevention of malaria. A simplistic method is to use ownership of ITNs, with the idea that ownership of ITNs should be valid as a proxy for ITN adherence if individuals value ITNs for the prevention of malaria. This simplistic understanding masks the multiple required ITN-related behaviors in the malaria prevention cascade ). Throughout the process we adhered to the PRISMA reporting guidelines for meta-analyses [We conducted a comprehensive search of the literature using the Embase and PubMed electronic databases in November 2015. The searches combined general terms and Medical Subject Headings related to ITNs and malaria  or discrepant time windows . Objective measures were categorized as either: 1) unannounced night visits confirming ITN use at the time of the visit or 2) visual confirmation of a mounted ITN . In the meta-analysis, a random effects model was used to determine a weighted average risk difference, with 95% confidence intervals (CI), between self-reported ITN use and objectively measured ITN use. This discrepancy between self-reported (SR) and objectively measured (Obj) ITN use was then compared to a weighted average of objectively measured ITN use using the metaprop command for meta-analyses of proportions [All statistical analyses were performed in Stata 10 using the portions .metaninf command [Heterogeneity was assessed using the I-squared statistic. Individual studies were assessed for influence on the final result by omitting each study and re-estimating the pooled estimate using the  command . A L’AbbAdditional stratified analyses were conducted to explore the heterogeneity. First, we compared studies with the year of publication before and after the signing of the Abuja Declaration in 2000, to explore differences due to increasing prominence of ITNs in malaria prevention. Second, we compared unannounced night visits to visual confirmation as the objective measure reasoning that visual confirmation of mounted ITNs may not be as accurate as directly observed ITN use during night visits. Third, we compared studies based on the season of assessment, high vs low transmission, as defined in the paper. Fourth, we compared studies based on the type of study population, defined as general population vs specialized population . Finally, we compared studies according to the unit of analysis.The funding source had no role in the writing of the manuscript nor in the decision to submit it for publication.Sixteen articles, published between 1989 and 2014, were selected for inclusion ,24,27-38In 11 studies (69%), the rate of self-reported ITN use exceeded the rate of objectively measured ITN use. The discrepancy between the self-report and objective measures ranged from 22.8% in favor of self-report in Ethiopia  to 5.7% Online Supplementary Document(Online Supplementary Document)). No systematic difference in the discrepancies was discernible in the L’Abbe plot based on the levels of adherence captured by the two methods ).According to the influence analysis, no single study drove the main result ). The studies using unannounced night visits had a 10.5% discrepancy favoring the self-reported measures compared to 6.3% when using the visual confirmation method, but the comparison was not statistically significant (Online Supplementary Document(Online Supplementary Document)).In general, none of the stratified estimates yielded significantly improved measures of heterogeneity, nor did any yield statistically significant differences in pooled estimates between the groups; all I-squared values were 71% or higher. Stratifying the studies pre- and post-Abuja, the unit of analysis and the season of study identified no significant difference in the pooled estimates  / 59 = 13.6%. This is the first meta-analysis to estimate the comparative accuracy of methods for assessing adherence to ITN use. The magnitude of this finding is in line with other systematic reviews showing a 10%-20% overestimate in self-reported adherence to HIV medications confirmed with electronic monitoring ,40.One study not included in this meta-analysis used an innovative electronic objective monitoring device to assess the state of the ITN and also performed self-reported measures. This study found no significant difference between the objective monitor measurements and self-reports of use when individuals were asked the next morning. The reason it was not included in the meta-analysis, however, is that the participants were aware that their ITN use was being tracked by an objective monitor. These self-reports, then, are potentially biased upward in the same way that self-reported ITN use would be upward biased if participants reported their ITN use the morning after survey assessors had just performed a surprise night visit. Electronic monitoring tools may be able to help assess social desirability bias in ITN use self-reports if the self-reports are measured in a context distinct from the objective monitoring, eg, in a clinic by different personnel.Systematic overestimation inherent in self-reported measures of ITN use may not be surprising given the nature of self-reports of health behaviors . HoweverThis finding has important implications for malaria prevention programs. First, since the majority of data about ITN adherence is based upon self-reported use, policy makers are potentially relying on inaccurate information when they make decisions about the optimal deployment of ITNs. Second, the systematic upward bias in self-reported rates of ITN use implies that ITNs may have greater efficacy than previously thought. A recent study found that ITNs accounted for 68% of a 40% decline in clinical incidence of malaria in Africa from 2000 to 2015 . SimilarTo address these challenges, studies should recognize the identified limitations of self-reported ITN use in interpreting and discussing ITN adherence using that measure. Self-reported measures may provide a “rough” estimate of ITN use, but they may be less useful as estimates of actual ITN adherence. This study’s findings show there was substantial unexplained heterogeneity detected in the discrepancy between self-reported and objective measured ITN use. This heterogeneity makes it difficult to conclude, in any particular case, that self-reports are overestimating ITN use by a specific amount, but the magnitude, statistical significance and consistency of the overestimation when relying on self-reports all suggest that self-reported ITN use should be interpreted with caution.Another possible solution could be efforts to utilize more objective methods in assessments of ITN adherence. Due to cost and time constraints, unannounced night visits and visual confirmation may not be feasible in many situations, although using these objective measures on a smaller scale to validate self-reported estimates of use may be more feasible. New measurement tools are being developed, such as electronic ITN adherence monitors ,43,44, wInterpretation of our findings is subject to several limitations. We were unable to identify study-level variables to explain the high level of heterogeneity, though such heterogeneity is common in the ITN use literature . After aIn this meta-analysis, studies that reported ITN use using both self-reports and at least one other objective measure demonstrated an over-estimation due to self-reports; self-reported ITN use was 8 percentage points higher, representing a 13.6% overestimation relative to objectively measured ITN use. These findings raise questions about the reliability of using self-reported ITN use alone as a surveillance tool and a guide for making policy decisions.


Overall, no association was replicated in both cohorts when Y-DNA haplogroups were considered and this suggests that, for BMI at least, there is little evidence of differences in phenotype or SNP association by Y-DNA structure. Further studies using other traits, phenome-wide association studies (PheWAS), other haplogroups and/or autosomal SNPs are required to test the generalisability and utility of this approach.Y-chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits, including cardiometabolic traits, have been reported. In apparently homogeneous populations defined by principal component analyses, there is still Y-DNA haplogroup variation which will result from population history. Therefore, hidden stratification and/or differential phenotypic effects by Y-DNA haplogroups could exist. To test this, we hypothesised that stratifying individuals according to their Y-DNA haplogroups before testing for associations between autosomal single nucleotide polymorphisms (SNPs) and phenotypes will yield difference in association. For proof of concept, we derived Y-DNA haplogroups from 6537 males from two epidemiological cohorts, Avon Longitudinal Study of Parents and Children (ALSPAC) ( The interpretation of genetic association studies ) requires consideration of many potential confounders including population stratification, gene-gene interaction and gene-environment interaction ,2,3. TheAnalyses of the non-recombining regions of the Y chromosome in different populations provide genealogical and historical information ,14. Y-chHaplogroups derived from Y-chromosomal variation can be used to provide information about the paternal ancestry of an individual and population genetic events  ,19,20. GFacilitating the process of linking haplotype assignment to GWAS studies, there is comprehensive information about the SNPs which define each haplogroup, approximate time and (most probable) region of origin, (current) area of highest frequency and the most prevalent (ancient) haplogroup present in different regions.Even for apparently homogeneous populations according to principal component analyses (PCA) utilising autosomal SNPs, there is underlying Y-DNA haplogroup variation. We have previously analysed Y-DNA haplotypes in a large epidemiological cohort in relation to confounding by genetic subdivision .In the present work, we stratify groups of individuals according to their Y-DNA haplogroups to (i) test for presence of additional structure due to Y-DNA haplogroup variation having taken into account PCA using autosomal markers; and if there is (ii) test if this additional structure has any potential confounding effects on genetic association studies . As a proof of concept, we chose to study the association between 32 common SNPs which are known to be robustly associated with body mass index (BMI). The use of these common genetic variants limits our analyses to the largest BMI effect loci.The Avon Longitudinal Study of Parents and Children (ALSPAC) is a longitudinal, population-based birth cohort study that initially recruited >13,000 pregnant women residing in Avon, United Kingdom, with expected dates of delivery between 1 April 1991 and 31 December 1992. There were 14,062 liveborn children. The study protocol has been described previously ,23 and fHeight and weight measurements were performed on children who attended a 9-year focus group clinic (mean age of participant = 9 (±0.32 years)). Ethical approval for all aspects of data collection was obtained from the ALSPAC Law and Ethics Committee . Written informed consent for the study was obtained for genetic analysis.The National Child Development Study (NCDS), otherwise known as the 1958 British birth cohort (1958BC), started as a perinatal mortality and morbidity survey looking at all births in England, Wales and Scotland in a single week in 1958. This included an original sample of 17,638 births (in addition to a further 920 immigrants born in the same reference week). Cohort members were further followed-up by medical examinations  and interviews . The first biomedical assessment was conducted between September 2002 and March 2004 by trained nurses from the National Centre for Social Research, who visited the homes of cohort members at age 44–45 years .A total of 9912 children  were genotyped using the Illumina HumanHap550 quad  genotyping array by 23 and Me. The Wellcome Trust Sanger Institute , and the Laboratory Corporation of America  were subcontracted. Information on the genotyping array can be found on: HumanHap550-Quad+ BeadChip . PLINK sA total of three thousand individuals were genotyped on the Illumina 1.2M chip (Illumina) [Dataset ID: EGAD00000000022]. The Illumina 1.2M genotyping array is a customised version of the Illumina Human1M-Duo BeadChip for which information can be found on: Human1M-Duo BeadChip . Qualityhttps://sourceforge.net/projects/yfitter/), and their respective Y-DNA haplogroup was determined. After removal of individuals with ‘False’ haplogroup determinations , we were left with 5080 individuals. Remaining individuals with a haplogroup result which began with the letter R  were clustered in to a single group named ‘Clade R’, and likewise the same was done with the haplogroups beginning with the other letters. The same procedure was carried out in 1958BC and 1453 male participants’ haplogroups were determined. Only the clades (major haplogroups) R and I were considered in the analyses, since there was not enough power for the less frequent haplogroups.For Y-DNA haplogroup determination in ALSPAC, the Y-chromosomal SNPs of all 5085 male participants in the dataset were used. The pseudo-autosomal SNPs were removed using the PLINK software package . The res2 and the top 10 principal components (PCs) determined by the EIGENSTRAT software [To check for association between BMI and the Y-DNA haplogroups in ALSPAC, a linear regression analysis was carried out using haplogroup R as a baseline (coded 0) and coding haplogroup I as 1. Age, agesoftware  were useThe analysis was repeated in the 1958BC during the replication stage. Production of summary statistics for the two cohorts and all regression analyses were carried out in the STATA statistical package .In order to study whether well-established associations are still present and/or observable within each Y-DNA haplogroup and whether the effect sizes of the SNPs were consistent across the different Y-DNA haplogroups, we analysed 32 common autosomal SNPs previously reported to be associated with BMI . This en2 and the first 10 PCs determined by the EIGENSTRAT software [z-test) was carried out across the two haplogroups to check whether there was a difference in the effect sizes (β coefficient) of the 32 SNPs tested.Body mass index data did not follow a normal distribution and inverse rank transformation was used to transform the data. Single Nucleotide Polymorphism dosage values were determined using MaCH . A lineasoftware . We lookFTO)) SNP.In agreement with the Y-DNA genealogical tree, we also analysed I and J individuals together as a sensitivity analysis for the rs8050136  in ALSPAC  (There was not enough evidence for association between Y-DNA haplogroups (using haplogroup R as baseline) and BMI (n ALSPAC  and in t= 0.107) .Summary statistics of the BMI observed for the two main Y-DNA haplogroups in ALSPAC and 1958BC can be found in z-test) used to compare the effect sizes derived from the two Y-DNA haplogroups. Only one instance of heterogeneity between the two haplogroups was observed after adjusting for Bonferroni correction which was observed for rs8050136 in FTO which yielded a z-heterogeneity test p-value of 0.005. The likelihood ratio test for interaction between the same SNP and haplogroup I yielded a similar p-value of 0.008  compared with haplogroup R .of 0.008 a. In ALSp-value for heterogeneity (as measured by the likelihood ratio test) between Y-DNA haplogroups I and R in relation to the association between rs8050136 (in FTO) and BMI was 0.008. To test whether the possible differential effect of the FTO SNPs replicate in another cohort, the top-hit rs8050136 SNP was analysed in the 1958BC and the results are presented in z-heterogeneity test) yielded a p > 0.05 —with very similar β . In this study we observed no conclusive evidence for differences in SNP/BMI association according to Y-DNA haplogroups in either ALSPAC or the 1958BC.A key aspect about the relevance of the Y-DNA structure is that there can be an effect on the phenotype associations if the structure is also correlated with BMI and if the actual haplogroup interacts directly with the assessed gene variants. Alternatively, other loci would be enough to obscure inference. Our study showed no clear evidence of this correlation and interaction. However, one could argue that the lack of replication could be explained by heterogeneity in the two datasets (ALSPAC and 1958 cohort), since the former includes children and the latter, adults. Therefore, the differences in betas and interaction effects could be due to other differences between the cohorts.A sub-clustering due to Y-DNA haplogroups can be revealed by plotting the Y-DNA haplogroup information versus the top two PCs on a scatter plot , our results are in accordance with evidence showing that gross structure in common variant analysis does not seem to be a problem after PCA. On the other hand, recent evidence suggests that finer structure does exist for people of the British Isles  and is mOur initial results can be explained by chance and hence was not replicated in the replication dataset. However, it also illustrates the potential of Y-DNA haplogroup data which could be tested in phenome-wide association studies (PheWAS) settings to systematically assess the impact of substructure.


Transcript levels do not faithfully predict protein levels, due to post-transcriptional regulation of gene expression mediated by RNA binding proteins (RBPs) and non-coding RNAs. We developed a multivariate linear regression model integrating RBP levels and predicted RBP-mRNA regulatory interactions from matched transcript and protein datasets. RBPs significantly improved the accuracy in predicting protein abundance of a portion of the total modeled mRNAs in three panels of tissues and cells and for different methods employed in the detection of mRNA and protein. The presence of upstream translation initiation sites (uTISs) at the mRNA 5’ untranslated regions was strongly associated with improvement in predictive accuracy. On the basis of these observations, we propose that the recently discovered widespread uTISs in the human genome can be a previously unappreciated substrate of translational control mediated by RBPs. Gene expression is a dynamic program by which the information stored in the genome is rendered functional by production and degradation of two types of macromolecules, RNAs and proteins. mRNAs are templates for proteins; therefore we expect correspondence between quantities of mRNAs and proteins. Genome-wide studies instead indicate a marked discrepancy between them, when considering their steady-state levels or their variations across different conditions. We employed linear regression approaches with paired mRNA/protein datasets in order to develop a model predicting the protein level of a gene from both the mRNA level and the protein levels of RBPs inferred to bind the mRNA untranslated regions. The results of our analyses restricted the utility of RBPs to improve accuracy of predicted protein abundance to a small fraction of the total modelled genes, and identified a novel association of the improvement induced by RBPs with the presence of upstream translation sites. This finding suggests a new avenue of experimental studies aimed at exploring the hypothesis that RBPs could influence protein abundance by changing the preference for certain translation initiation sites. High throughput technologies such as RNA-sequencing (RNA-seq) and mass-spectrometry-based protein analyses provide transcriptomic and proteomic profiles, which are the basis to draft a comprehensive picture of gene expression regulation ,2],3].,[3].2],.2],Several studies have reported a lack of concordance between transcriptome and the proteome profiles ,4],5],,[5],4],,4],The regulatory mechanisms by which the various post-transcriptional processes exert their effects on protein abundance are not well understood. Regulatory features associated with these processes have been identified not only in the coding regions but also in the 5’ and 3’ untranslated regions (UTRs) of mRNAs in multiple species ,24]. Af. Af24]. Here, we developed a model of post-transcriptional control of gene expression by using multivariate linear regression to estimate protein levels from transcript levels. The model is empirically developed from two types of primary data: quantitative transcriptome assays matched with proteome assays, and post-transcriptional regulatory annotations of mRNA untranslated regions (UTRs) obtained by scanning for occurrences of in vitro experimentally determined RBP binding sites . IncludiTo devise a model of protein levels from transcript levels including a quantitative description of the contribution of RBP-mediated post-transcriptional control, we selected three data panels consisting of matched transcript and protein profiles: twelve normal human tissues , 59 cancTable A in S1 Text), confirming previous reports ,,,,6. We explFig 4C). The association was confirmed in the colorectal cancer samples (p = 9 10−3) but not in the NCI-60 panel of cancer cell lines (p = 0.61).We have previously shown that, albeit limited in scope, the predictive value of RBPs was present also in the panels of colorectal cancer samples and NCI-60 cell lines. We assessed the association between improvement in accuracy of predicted protein abundance and number of uTISs in these two panels as well, by Fisher’s Exact Test (−5) and in the colorectal cancer samples (p = 6 10−6) but not in the NCI-60 panel (p = 0.24) . In summary, the association of the improvement in accuracy of predicted protein abundance with the number of uTISs was robust to the platform for identifying the uTISs, and could be partially recapitulated in different biological contexts.The uTIS mapping  used herTable B in S1 Text). Results from this final assay reflected the different extents to which the RBPplus models were found to improve accuracy in predicted protein abundance in the three panels, with the normal tissue panel showing better RBPplus model performances.Furthermore, we reasoned that ribosome profiling  experimeplus model improved predictive accuracy compared to the RNAonly model. Even if this association does not mean a biological link between RBPs and uTISs, it suggests that translational regulation of the main ORF could be exerted by some of the considered RBPs through an uTISs. A potential, direct mechanism for this regulation could be steric control of uTIS elements by local RBP binding. We adopted this hypothesis to attempt an initial prioritization of RBPs. In case of steric control, RBP binding sites need to be in the proximity of a uTIS. No demonstrated example of such a control is present, at the best of our knowledge, in the literature. A functional proximity between uTISs and RBP binding sites has been reported only in one study involving the Drosophila SXL protein, but in this case the uTIS defines a uORF ,[63,[63only Matched transcriptome and proteome profiles were downloaded in the processed form provided by three independent datasets: 1) a panel of twelve human normal tissues , 2) the http://wzw.tum.de/proteomics/nci60. Processed proteome data for TCGA colorectal cancer samples were downloaded from the online Supplementary Information of the study [http://cancergenome.nih.gov/).Processed data derived from gene expression analysis in the normal tissue panel were downloaded from the online Supplementary Information of the study . Normalihe study , while pIn the normal tissue panel and CRC panel, transcript abundance data were obtained by RNA sequencing (RNA-seq) and expressed as Fragments Per Kilobase per Million, log-base-10 FPKM. NCI-60 transcriptome profiles were obtained by microarray. Normal tissue proteome profiles were obtained by the intensity-based Absolute protein Quantification method, and expressed as log-base-10 iBAQ. NCI-60 and CPTAC CRC proteome profiles were based on liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based shotgun proteomic analysis. Intensity- and spectral count-based label-free quantifications were used to obtain protein abundance in the NCI-60 cell lines and in the CPTAC CRC specimens, respectively.In the normal tissue panel we excluded genes and proteins below the detection limit in more than three out of twelve tissues at either transcriptome or proteome level; in the NCI-60 and CRC panels we excluded genes below the detection limit in more than five out the total number of specimens at either the transcriptome or proteome level. Genes below the detection limit were assigned zero values or Not Available (NA) labels in the files processed data were acquired from.Within each panel, we applied inter-sample normalization by mRNA and protein mean-centring per sample.http://cisbp-rna.ccbr.utoronto.ca/). In each panel of matched transcriptome/proteome datasets, the inference of RBP binding sites in mRNA UTRs was restricted to the subset of RBPs which were detected both at the transcript and at the protein level. For each considered RBP, RBP binding sites as well as corresponding q-values were obtained using the FIMO algorithm [http://meme-suite.org/) and retained at the false discovery rate of 20%.We scanned non-redundant 5’ and 3’ untranslated region (UTR) sequences of the genes profiled at both the transcript and protein levels with positional weight matrices (PWMs), which represent RNA sequence binding specificities of RBPs derived from RNAcompete  data andlgorithm  of the Monly) model, where the abundance of protein j in sample i (PROTij) was predicted by the corresponding mRNA level only in a simple linear regression model:0j is the intercept term, βmRNA,j is the regression coefficient for the mRNA predictor and the error term εij is an independent and identically distributed (iid) random variable following a normal distribution of mean 0 and standard deviation σ. This model was fit for each mRNA/protein pair. An RBP-inclusive (RBPplus) multiple linear regression model was also fitted for each mRNA/protein pair:RBP,jk is the regression coefficient for the kth RBP of mRNA j. This model was fitted by maximum penalized likelihood with Ridge or LASSO penalty applied to RBPs but not to mRNA measurements, using the pensim R package [We built two models for each considered gene: a basic  randomly sampling a number of protein predictors equal to the number of actual RBPs inferred to bind the mRNA UTRs, and 2) by permuting the protein levels of inferred RBPs across samples. As the actual RBPplus models, each randomized RBPplus model (by sample permutation or randomly sampling of protein predictors) was fitted by maximum penalized likelihood with Ridge penalty applied to RBPs but not to mRNA measurements in nested cross-validation scheme. Fig 3illustrates the two randomization schemes.Empirical randomization tests were used to determine whether the accuracy in predicted protein abundance achieved by the RBP2 value observed for the actual RBPplus model of each considered gene was defined by the probability of sampling a R2 value from the null distribution of R2 values that is higher than the observed R2. The RBPplus model of a gene was deemed to improve the accuracy in prediction of protein abundance if the RBPplus model accuracy was higher than that of the RNAonly model and if the probability of attaining accuracy higher than that of the RBPplus model by randomly sampling protein predictors was < 0.05. Since our analysis involved multiple hypotheses testing, we reported false discovery rate by the Storey’s q-value method implemented in the qvalue R package [The p-value of the R package .only and RBPplus models using all samples in a training panel, and tested them using all samples in the testing panel. The procedure was repeated for all possible combinations of training and test panels. We estimated model transferability computing Spearman’s correlation coefficient of protein predictive accuracies between the RBPplus models trained in a chosen panel and the RBPplus models trained in each of the other two panels.We studied cross-panel model transferability of models trained using only the RBPs profiled in both of each pair of panels. For each considered mRNA, we developed the RNAhttp://geneontology.org/) and used hypergeometric test. Functional analysis was used 1) to assess over-/under-representation of GO themes in the genes which turned out to be adequately measured relative to the total of genes which were profiled at the mRNA/protein levels, and 2) to assess over-/under-representation of GO themes in the genes where RBPplus models were found to be informative relative to the total of modelled genes.Functional enrichment/depletion analysis was based on the Biological Process categories of the generic Gene Ontology (GO) slim, a cut-down version of the Gene Ontology annotations (Table 1). We quantified the selected features in the mRNAs modelled in the normal tissue panel as follows. Normalized lengths of the coding sequence as well as of the 5’ and 3’ UTRs were calculated for each mRNA according to the sequence annotations (hg38 assembly) available at the UCSC Genome Browser (https://genome.ucsc.edu/). Local folding energy was computed within a window of 30 nucleotides upstream and downstream of the annotated translation initiation site of the modelled mRNAs using the RNAfold algorithm of the Vienna RNA package (www.tbi.univie.ac.at/RNA/). Transcript half-life measures were acquired by two distinct studies which relied, respectively, on biosynthetic labelling of newly transcribed RNA and estimation of newly/total RNA ratio in human B cells [We surveyed appropriate data sources to gather several gene annotations relevant to post-transcriptional regulation of gene expression in mammalian cells  were defined by: (i) an uTIS out-of-frame at the 5’ UTR, with a stop-codon, in the same frame, downstream of it, and with a minimal length of nine nucleotides, (ii) an uTIS in-frame at the 5’UTR with a stop codon in frame after the main stop codon or before the main start codon.plus model was found to be informative in uTIS-containing genes as well as in uORF-containing genes. Testing was performed for uTISs identified by GTI-seq and QTI-seq technologies and for each panel of paired mRNA/protein datasets.We used Spearman’s correlation coefficient to estimate the correlation of the change in accuracy of predicted protein abundance with each aforementioned feature. Furthermore, we used Fisher’s test to assess the enrichment of the genes where the RBPRBPs were prioritized by an analysis of the frequency at which the binding sites of an RBP occur in the proximity of uTISs of mRNAs. We identified the closest RBP binding site to each uTIS present in the 5’ UTR of each mRNA. We then quantified the frequency of the binding sites of each RBP in the binding sites situated nearest to the uTISs overall mRNAs. RBPs were ordered according to the number of genes where they were found to recognize the binding sites closest to the uTISs.In the contexts where multiple tests were performed, raw P-values were adjusted by the Benjamini-Hochberg method for controlling false discovery rate at 5%.S1 Fig(TIF)Click here for additional data file.S2 FigA) Distributions of the median logarithmic mRNA abundances of all genes (dashed line) and of the genes selected on the basis of the detection frequency across the samples in each panel (solid line). (B) Distributions of the median logarithmic protein abundances of all genes and of the genes selected on the basis of the detection frequency across the samples in each panel.((TIF)Click here for additional data file.S3 FigFunctional Gene Ontology enrichment analysis of the genes selected for modelling in each panel, showing depleted or enriched GO slim categories (p < 0.05). A Gene Ontology category is shown if false discovery rate meets threshold in at least one panel.(TIF)Click here for additional data file.S4 Fig(A) Distribution of Fragments Per Kilobase per Million (FPKM) from RNA-seq experiments of all 12 normal tissue samples. (B) Distribution of mRNA intensity from microarray profiling experiments of all 59 NCI-60 cell lines. (C) Distribution of Fragments Per Kilobase per Million (FPKM) from RNA-seq experiments of all 87 CPTAC CRC samples.mRNA expression data are unmodified with respect to the original publication. (TIF)Click here for additional data file.S5 Fig(A) Distribution of protein intensity from proteome profiling experiments of all 12 normal tissue samples. (B) Distribution of protein intensity from proteome profiling experiments of all 59 NCI-60 cell lines. (C) Distribution of spectral counts from proteome profiling experiments of all 87 CPTAC CRC samples.Protein expression data are unmodified with respect to the original publication. (TIF)Click here for additional data file.S6 Fig2 achieved by the RNAonly (dashed line) and RBPplus (solid line) models according to different types of inter-sample normalization. Shown are p-values of Wilcoxon signed-rank tests to assess differences in the ranks of predictive accuracy between the RNAonly and RBPplus models based on each type of inter-sample normalization.Distribution of R(TIF)Click here for additional data file.S7 FigHeat maps display Cook’s distance values for each gene and sample.(TIF)Click here for additional data file.S8 FigDistribution of number of RBPs inferred per mRNA using the thresholds of 5% or 20% to the false discovery rate on RBP binding sites.(TIF)Click here for additional data file.S9 FigA) Node colour distinguishes source (RBP predictor) and target (modelled gene) nodes. An edge indicates that the RBP is predicted to bind the mRNA. A target node weight is introduced to represent the improved accuracy in the protein abundance prediction of the RBPplus model in comparison to the RNAonly one, whereas an edge weight represents the regression coefficient of the RBP in the RBPplus model of the target mRNA. Only statistically significant modules totalizing mean edge weight and entropy values above median values are displayed. (B) Gene-wise correlations between experimental protein levels and protein levels predicted, respectively, by the RBPplus and the RNAonly models are shown for each module. The RBPplus model improves the correlation between inferred and observed protein levels in all modules. The modules where the improvement is statistically significant display pincers on the top of the corresponding pairs of boxplots.((TIF)Click here for additional data file.S10 Fig2) obtained by the RNAonly models as well as by the RBPplus models using RBP-mRNA interactions inferred at different false discovery rates (FDRs). We tested differences in rank of protein predictive accuracies between RNAonly models and RBPplus models at different FDR values by the Wilcoxon signed-rank test. P-values are shown and colour-coded in figure.Shown are the distributions of protein predictive accuracy (R(TIF)Click here for additional data file.S11 Fig2) for the RBPplus models fitted with Ridge and LASSO penalty are shown with the R2 distribution for the RNAonly models. Wilcoxon signed-rank test was used to test differences in rank of the protein predictive accuracy for the RNAonly models and the RBPplus models, which were fitted by either penalty. Test’s P-values are colour-coded according to the penalty used to fit RBPplus models.The distributions of protein predictive accuracy (R(TIF)Click here for additional data file.S12 Fig(A) RBPplus models fitted with Ridge or LASSO penalty ensure comparable protein predictive accuracies. Shown are the distributions of R2 obtained by the RBPplus models fitted with Ridge or LASSO penalty. Wilcoxon signed-rank test was used to test differences in rank of the protein predictive accuracy for the RBPplus models fitted by Ridge or LASSO penalty. Test’s P-values are shown. (B) Distribution of the fraction (%) of predictors selected by the RBPplus models fitted with LASSO penalty with respect to the predictors used in the RBPplus models fitted with Ridge penalty.(TIF)Click here for additional data file.S13 Fig(TIF)Click here for additional data file.S14 FigCorrelation is estimated by Kendall’s tau coefficient in all three panels.(TIF)Click here for additional data file.S15 FigJaccard index of modelled genes between each pair of panels included in our analysis.(TIF)Click here for additional data file.S16 Figplus models show some transferability across tissue panels. Better transferability is observed from NCI-60 and CPTAC CRC panels to normal tissue panel. All the possible combinations of training and test panels are grouped by test panel. Shown is the Spearman’s correlation coefficient between R2 of RBPplus models trained in the testing panel  and R2 of RBPplus models trained in the remaining two panels .RBP(TIF)Click here for additional data file.S1 Fileonly and RBPplus models as well as the p-value by randomization of protein predictors in the RBPplus model.The table shows accuracy in predicted protein abundance achieved by the RNA(XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


An 83 year-old-man with left atrial appendage occluder (LAAO) developed Pseudomonas Aeruginosa (PA) infective endocarditis. MRI at day 3 of onset showed distal small infarcts in both middle cerebral arteries and left postero-inferior cerebellar artery territories. MRI at day 6 revealed two sites of convexity subarachnoid hemorrhage (cSAH). MRA and CTA failed to reveal a Mycotic aneurysm.The radiologic findings favor the assumption of necrosis of distal branches of mca or of pial arteries wall.This case present three unusual features: the presence of localized cSAH after initiation of antibiotherapy without mycotic aneurysm being individualized; the late occurrence of infective endocarditis after LAAO implantation; the very rare occurrence of PA in prosthetic infections. Subarachnoid hemorrhage (SAH) complicating a mycotic aneurysm (MA) has been reported in only 2- 5% of patients with infective endocarditis (IE) , while SWe report a case of an IE of the left atrial appendage occluder (LAAO), caused by Pseudomonas Aeruginosa (PA) and complicated by cerebral ischemia and 2 sites of convexity SAH (cSAH).An 83 year-old-male presented to the emergency room with fever and confusion. He had a stable ischemic heart disease, permanent atrial fibrillation (AF). After an unsuccessful radiofrequency ablation of AF three years earlier, he had an endovascular occlusion of the left atrial appendage (LAA) 30 months before and was left on antiplatelet therapy .At admission, confusion, neck stiffness, left sided hemiparesis and endophtalmia with purulent right eye secretions were noted. Body temperature was 40°C and arterial pressure was 153/93. A systolic aortic murmur without abolition of B2 and a systolic mitral murmur were present. An arbitrary antibiotherapy was started on (cefotaxim + ciprofloxacin + amikacin).The initial work-up revealed in the peripheric blood a WBC count of 9200/mm3, an Hb of 10g/dl, a platelet count of 165 000G/l, a CRP at 182 mg/l, and an isocoagulant status . PA was detected in blood cultures (time of positivity after inoculation of 10 and 15 hours).CSF showed a cell count of 40 cells/µl (90% of neutrophils), and rare colonies of PA.1-a-b). There was no hemorrhagic lesion.Non-enhanced Brain CT at admission was normal. MRI at day 3 showed multiple small foci of ischemic lesions within the distal middle cerebral arteries (MCA) territories, the pons and the left postero-inferior cerebellar artery territory Fig. (Transthoracic echography (TTE) and transesophageal echography (TEE) showed a huge mobile tissular lesion located on the LAAO, corresponding either to septic vegetation or to thrombi. The mitral leaflets were heavily calcifiedA definite IE by PA – according to the modified Duke’s criteria - complicated with endophtalmia, cerebral emboli and meningitis was diagnosed and adequate antibiotherapy was started on.1-c-d-e) 3D-TOF MR Angiography of the Willis circle showed a dolicho-basilar trunk.Repeat MRI at day 6 showed besides the lesions described before, two sites of localized SAH on FLAIR and T2-GRE images at the left temporal convexity -extending to the external part of the sylvian fissure- and at a right temporal sulcus, and revealed a minor hemorrhagic transformation of the rolandic infarct Figs. (Brain CT-Angiogram (CTA) failed to reveal an MA or other cause of SAH, and showed a stenosis (50%) of both internal carotid arteries.The 18-F-FDG PET scan was compatible with the diagnosis of IE of the LAAO with septic localizations at the right eye.Per-operative TEE confirmed the diagnosis of a huge plurilobular formation fixed to the LLAO, a moderate to severe mitral regurgitation because of a P2-A2 mitral prolapse and images of small vegetations on P2. The LAAO and the native mitral valve apparatus were removed and a mitral biological prosthesis was implanted.The patient was rapidly discharged from the operative room with stable hemodynamics. However, he died 3 days later from a refractory cardiogenic shock.The reported case presents three unusual features: firstly, the presence of localized cSAH without MA being individualized; secondly the late occurrence of IE after LAAO implantation; finally PA being an infrequent bacterium responsible for prosthetic infections.SAH without associated MA has been rarely reported in IE.Krapf  reportedChukwudelunzua  identifiSAH are usually attributed to ruptured MA, even when no aneurysm is demonstrable because they are sometimes obliterated by the hemorrhage that they produce, hence their angiographic and even pathologic demonstration is not always possible.Our patient had no image confirming rupture of MA on CTA. The SAH was mild to hide any aneurysm and the patient was only under antiplatelet therapy, ruling out any suspicion of anticoagulation overdose responsible for the bleeding.Septic emboli can be responsible for erosive arteritis causing rupture of the artery wall and intracerebral hemorrhage. This eventuality occurs during uncontrolled infection, particularly with virulent organisms such as PA, which can be the case of our patient who continued to have septic signs although antibiotherapy had been instituted.Furthermore, in the current case, the concomitance of multiple distal infarcts and 2 sites of cSAH lead us to believe that an erosive arteritis secondary to the bacterial infection was responsible for wall rupture of distal arteries. Nevertheless, rupture of small pial artery as a cause of SAH had been previously demonstrated in an autopsy report . RuptureFurthermore, CAA is the most commonly reported cause of cSAH and in this eventuality, the most common location for cSAH is the fronto-parietal area . In reveOn the other hand, this patient was followed for ischemic heart disease and was under aspirin for long years. He had never presented hypersensitive clinical manifestations and no worsening of his coronary artery disease. That why laboratory tests were not indicated for him. Recent studies have shown that only short-term use of low-dose aspirine is associated with increased risk of SAH .staphylococcus aureus) and was related to lack of sterile conditions during the procedure [IE located at the LAAO is an extremely rare complication. Two recent studies about feasibility and safety with the fourth generation watchman LAAOs failed to detect device infection . Only onrocedure . IE due rocedure .via ocular contamination, as he had a severe endophtalmia.PA endocarditis is usually associated with the use of intravenous drugs and with prosthetic devices . PA infeTo the best of our knowledge, this is the first reported case of localized SAH secondary to PA IE in a patient after LAAO system implantation.Localized cSAH without MA and in the absence of anticoagulation is a very rare neurological complication of IE.This case of late prosthetic IE demonstrates that patients with LAAO are at risk to develop infection of their device and should benefit from the same preventive recommendations as the high –risk populations.Finally, localized cSAH should suggest IE when occurring in patients with implanted heart devices.


In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both  The discovery of expression Quantitative Trait Loci (eQTL) from the analysis of genome-wide genotype and gene expression data has played an important role in the study of cellular processes and complex disease. Here, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis, an analysis framework that has been designed to identify eQTL with broad impacts on the expression levels of many genes. The CONFETI framework takes advantage of Independent Component Analysis (ICA) to separate putative genetic and non-genetic factors in a confounding factor mixed model analysis, such that broad impact eQTL are not corrected out of the analysis as confounding variation. We show that CONFETI has better performance for identifying broad impact eQTL compared to the most widely applied confounding factor correction methods when applied to simulated data. We also applied CONFETI and these same methods to identify eQTL that replicate between twin pairs from the MuTHER consortium, the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and common tissue type pairs in the Genotype-Tissue Expression (GTEx) consortium. Surprisingly, while CONFETI had comparable replication performance compared to other methods, we were able to identify and replicate a very small number of broad impact eQTL overall. We discuss reports of broad impact eQTL in humans and suggest that they should be interpreted with caution. Suchans-eQTL , 17, 18,ans-eQTL , 20. Genans-eQTL , which ians-eQTL –26. Discans-eQTL –30. As aans-eQTL –51.trans-regulators [trans-eQTL networks [For studies that leverage eQTL as a foundation for network modeling or for identifying candidate disease risk loci, eQTL that are associated with multiple genes can be particularly valuable. For directed network modeling, the value of such broad impact eQTL is clear, since the network inference depends on tracking the impact of eQTL through multiple genes –56. Whengulators , and tranetworks  could renetworks , 62 or fnetworks . Broad inetworks –65 and mnetworks , but havnetworks .trans-genes, statistical power has been suggested as a possible reason for the relatively lower reporting of broad impact eQTL in humans, since trans-eQTL tend to have relatively weak associations in humans compared to model organisms [9 to 1010 for array based studies [11 to 1012 for data collected by next-generation sequencing technologies [trans-pairs [trans-associations at all [trans- compared to cis-eQTL genome-wide, making the identification of broad impact eQTL with multiple trans-gene effects almost impossible.Since broad impact eQTL are expected to primarily affect rganisms . Further studies  and 1011ns-pairs , 67, 68 s at all . A consecis- and trans-eQTL by increasing detection power [A promising analysis strategy that could partially alleviate the statistical difficulties in identifying broad impact eQTL is the use of confounding factor analysis . Confounon power , 77, a kon power , 79. Preon power , and usion power . HoweverIn this study, we describe a new framework that is designed to improve on the performance of confounding factor methods to identify broad impact eQTL. The CONFETI  framework makes use of the machine learning method Independent Component Analysis (ICA) to separate genetic components from non-genetic components learned from multivariate gene expression variation. ICA is a widely used blind source separation method applied to problems such as voice and image separation, and more recently to high dimensional gene expression data to estimate non-Gaussian generative sources from an observed mixture –84. CONFTo show the potential of CONFETI for the discovery of broad impact eQTL, we evaluated performance using simulated genome-wide data. For these simulated datasets, we show that the CONFETI framework successfully corrects for the effects of the confounding factors without explaining away broad impact eQTL. We also show that CONFETI has considerably increased performance compared to the most commonly applied confounding factor analysis methods.cis- and trans-eQTL. While we found that CONFETI had better or comparable performance to other methods in the replication of both cis- and trans-eQTL with individual gene impacts, after careful modeling and consideration of population structure, confounding factors, annotation inconsistencies, read alignment artifacts, and visual inspection of false positive indicators, we were able to identify only a few replicating broad impact eQTL at a genome-wide significance threshold in the MuTHER lymphoblastoid cell line (LCL) dataset. Taken together, these results suggest that robustly identifiable broad impact eQTL in humans have considerably smaller effects per gene than the bulk of eQTL. We discuss the implications of these results when considering factors such as sample size that can impact broad impact eQTL discovery, as well as for the use of previously reported broad impact eQTL as a foundation for making biological inferences.We then assessed the ability of the CONFETI framework and these other methods to identify and replicate eQTL between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER) , betweenThe CONFETI framework is constructed to systematically avoid the tendency of other confounding factor analysis methods to model broad impact eQTL as confounding variation. This is accomplished by leveraging Independent Component Analysis (ICA) to identify generative sources of multivariate gene expression variation and then screening candidates based on component correlations with genotypes, which are then omitted from the confounding factor correction . ICA is g-dimensional vector of gene expression values for a single sample, and independent components g-dimensional vectors of gene weights that are shared among all samples and the scalar component coefficients aij represent the contribution of each independent component i  or any other factor analysis method , ICA is The complete CONFETI framework involves running ICA on multivariate gene expression data, an automated detection step to identify candidate broad impact eQTL by assessing associations with genotypes, and omission of these factors for the construction of the random effect sample covariance matrix used in a mixed model confounding factor analysis . While ttrans-eQTL by jointly estimating the covariance matrix with genotype effects to avoid including those effects in the correction [We note that our framework differs from ICA methods for eQTL detection that treat the identified ICs as meta-genes, where these methods cannot reliably distinguish the specific gene effects of individual eQTL , 93. Therrection . Howeverrrection , which thttps://github.com/jinhyunju/confeti). The independent component estimation features are using functions adopted from the fastICA R package [To apply ICA to gene expression data and generate a sample covariance matrix, we developed a custom R package  and all genotypes. As in fixed effect eQTL models, we fit a linear regression model with the IC coefficient as the dependent variable and the genotype values as independent variables. After calculating p-values for each IC coefficient and genotype pair, we identified candidate broad impact eQTL using a global Bonferroni corrected p-value threshold of 0.05. Components with at least one significant association are marked as candidate genetic components. After filtering out r (0 ≤ r < k) components with significant genotype association, we reconstruct expression matrix Y* originating from non-genetic factors using the remaining k − r components:Y* is an n × g matrix, A* is a n × (k − r) matrix and S* is a (k − r) × g matrix.After decomposing the observed data Given that the overall CONFETI method makes use of the phenotype and genotype data both in the filtering out of candidate genetic effects and in the identification of significant genotype-gene expression associations, using the full dataset could lead to model over-fitting impacts in the selection and removal of ICs. To assess this issue, we compared the approach of using CONFETI on the full dataset to a strategy where we split the genotype data into two random subsets. For the splitting strategy, we used one of the genotype subsets for filtering candidate genetic effects and the remaining genotypes for the eQTL analysis, we then repeated the analysis flipping the subsets that are used for filtering and eQTL analysis, and the combined the results. With this splitting strategy, genotypes used for the removal of candidate genetic effects do not overlap with the genotypes that are being tested for eQTL, such that each genotype is only accessed once in each subset.From the analysis of multiple datasets, we found that the results obtained by using the full dataset and the splitting strategy largely overlapped with only minor differences . A possiK for the random effect part of the mixed model. Our first approach was to use a simple location-scale normalization of each gene of Y*:We used two approaches to construct the sample covariance matrix analysis  by estimC is an n × Q matrix initialized by projecting the observed data onto the first Q principal components explaining 95% of the variance and is further optimized in the process, and θ is the set of hyperparameters consisting of qth column of C, Cq⋅ in constructing the sample covariance matrix:K.For our second approach, we couple CONFETI with PANAMA (Probabilistic ANAlysis of genoMic dAta)  that est package  for the p in n individuals are:n is the number of samples, g the number of genes, s the number of SNPs, and v the number of covariates. Each gene expression vector n × 1 and is mean centered. The n × (1 + v) genotype and covariate matrix X contains a single genotype as the number of minor alleles coded as 0,1,2 and any additional v number of covariates. v) × 1 dimensional coefficient vector representing the fixed effect of the SNPs and covariates on gene p. The confounding effect is included in the model as a n × 1 random effect K is the n × n sample covariance matrix constructed the corresponding confounding correction method, K in the random effect, and n × 1 vector representing the independent error for gene p with scalar weight We model the genetic effects from SNPs and covariates as fixed effects and confounding factor effects as random effects, such that the expression levels for gene K), which is kept constant when testing individual genotypes against phenotypes, to avoid the computational burden of recalculating K for every phenotype.We compared CONFETI-I and CONFETI-P to a simple linear regression with no confounding factor correction (LINEAR), including PCA projections as fixed effects (PCA), probabilistic estimation of expression residuals (PEER) , and mixlrgprApply function from the R package lrgpr [glmApply function from lrgpr and used a Wald test for significance testing.For each comparison of methods on simulated or real data, we ran each method to be as equivalent as possible, including the same covariates and using the same linear mixed model fitting function. For CONFETI-I, CONFETI-P, PANAMA, and ICE we used ge lrgpr  to fit tge lrgpr , the numcis- and trans-eQTL relationships. To simulate eQTL, a matrix with a dimension of number of genotypes × number of expression phenotypes was first created that marks genotype and phenotype pairs cis- if the starting position of the gene and the genotype were within 100,000 base pairs distance and trans- if the distance was greater. From this matrix we sampled 2500 genotype and phenotype pairs which consisted of 80% cis- and 20% trans-genotypes. In total, for each simulated dataset, we included 2000 cis-eQTL, 500 trans-eQTL, and 10 broad impact eQTL. We simulated each broad impact eQTL to affect 10% of the expression phenotypes. Effect sizes for cis-eQTL were sampled from trans-eQTL and broad impact eQTL were sampled from trans-effects) to reflect observed effect sizes in real data. After the eQTL effects were simulated, we added normally distributed random noise sampled from To mirror real cases where a reasonable number of broad impact eQTL have been repeatedly identified, we used yeast as a model –65. To ccis-, trans- or broad impact and the recovery rate for each category at different FDR thresholds was calculated by dividing the number of true genotype phenotype pairs that were called significant by the total number of true genotype phenotype pairs in each category. To provide an upper bound metric on how well methods could recover each of these eQTL types, we also simulated the same scenarios without any confounding factors and reported the ROC curves after running LINEAR. We labeled these results ‘TMR’ for ‘Theoretical Maximum Recovery’ since these represent the maximum recovery expected in theory if confounding factors were perfectly modeled by the confounding factor methods.We ran each of the methods CONFETI-I, CONFETI-P, PANAMA, ICE, PEER, PCA, and LINEAR on each of the 100 datasets using the method settings and parameters as described above. To evaluate performance for each method, we ranked the eQTL for each method according to their p-values and then calculated the True Positive Rate (TPR) and False Positive Rate (FPR) and generated Receiver Operating Characteristic (ROC) curves for each method, where we also calculated the area under the curve for each method across the simulation scenarios. True eQTL were further labeled as We analyzed data from the Multiple Tissue Human Expression Resource (MuTHER)  project,We ran eQTL analysis on the adipose, lymphoblastoid cell line (LCL), and skin datasets obtained through the MuTHER project . Based oWe also analyzed data from the DGN  and NESDr2 > 0.99 and removed SNPs which were deprecated in dbSNP .For the analysis of the GTEx datasets, we selected 4 pairs of tissues  from GTEx release v6 (dbGaP Accession phs000424.v6.p1) with over 150 samples that have both RNA-seq gene expression and SNP array genotypes . For genmp asp values for every gene to calculate λpdiff, = 1 − λp.We fit CONFETI-I, CONFETI-P, PANAMA, ICE, PEER, PCA, and LINEAR for every phenotype and genotype pair in each of the datasets using the method settings and parameters as described above. To control for population structure, we included principal components derived from the genotypes, using the first five , three , and two  principal components as covariates in each analysis. While a permutation approach is often applied to avoid any systematic inflation or deflation of the p-values, this was computationally infeasible for this study given the number of datasets analyzed and number of methods applied to each dataset. We therefore calculated the genomic inflation factor λ for each expression phenotype, a statistic which has been shown to provide a good metric for assessing model fit and appropriate p-value distributions , 72. Thecis- if the SNP and gene were located on the same chromosome within 1 Mb, and trans- otherwise.After calculating p-values for all phenotype and genotype pairs, we adjusted the p-values using Benjamini-Hochberg multiple hypothesis correction. The corrected p-values represent upper bounds on False Discovery Rate (FDR) . We usedtrans-eQTL using two methods. First, we used annotated gene relationships available from NCBI (ftp://ftp.ncbi.nih.gov/gene/DATA/gene_group.gz) to identify trans-eQTL where the SNP was within 1Mb of a gene related to the eQTL gene . Because not all gene relationships were captured in the NCBI annotation, we searched for additional, potentially unannotated pseudogenes using the BLAT tool [trans” screening revealed that a number of the replicating trans-eQTL were artifacts arising due to incorrect/ambiguous mapping of RNA-seq reads that are in fact caused by cis-regulation of a gene, which shares sequence similarity with the eQTL gene. We also visually inspected eQTL for artifact or false positive indicators .To avoid potential artifacts caused by ambiguous RNA-seq alignment we screened LAT tool  to aligncis- and trans- SNP per cytoband per gene. Using this criteria, we measured the replication of eQTL between and across different tissues counting the overlapping cytoband and gene pairs that were called significant in each dataset. We marked broad impact eQTL by searching for genotypes that showed more than a single trans-eQTL associations on different chromosomes that replicated between at least one twin or tissue pair.In order to avoid double-counting eQTL associated with multiple linked SNPs, we selected at most one significant cis-, trans- and broad impact, in the presence of confounding factors. We also included the theoretical maximum recovery (TMR) as an upper limit of eQTL detection for each eQTL category, where the phenotype data has only normally distributed random noise added without any confounding factor effects. For both sparse and dense confounding factor effects, all methods showed significant improvements over LINEAR (linear regression without confounding factor correction), and CONFETI-I correctly identified the most eQTL at every FDR threshold . The enrichment of regulatory signals in the LCL dataset on the region of chromosome 6 which rs3817963 is located is proximal to the major histocompatibility complex (MHC) region, which is critical in immune cell function. A cluster of replicating eQTL on the same region of chromosome 6 was also found in the Adipose and Skin twin pair, however, we only found replicating eQTL associated with genes on different chromosomes in the LCL dataset  to avoid over-correcting genetic effects in eQTL mixed model confounding factor analysis. CONFETI provides an easy to implement solution for a known problem with eQTL confounding factor methods: the tendency of these methods to model the effects of eQTL with broad impacts on many genes as confounding variation. In sum, the CONFETI approach provides a method for finding broad impact eQTL while leveraging the advantages of confounding factor analysis for eQTL discovery, a capability that has not been systematically implemented in currently available confounding factor analysis software.in vivo for MuTHER adipose and skin datasets, and the even great heterogeneity across distinct studies of DGN and NESDA, it seems likely that different levels of sampling heterogeneity are also influencing broad impact eQTL discovery.In our real data evaluation of CONFETI and other methods, we found that confounding factor correction methods, especially linear mixed model based methods, increased the findings of replicating eQTL. This was also the case for identifying broad impact eQTL that replicated at a genome-wide significance level between datasets. While we did not find any replicating broad impact eQTL for the GTEx tissue pairs, we did find a number of broad impact eQTL when analyzing the MuTHER LCL dataset. Given that broad impact eQTL appear to have relatively small per gene impacts and the larger sample size of MuTHER compared to the GTEx datasets we analyzed, this supports power, and therefore sample size, as being a critical issue when detecting broad impact eQTL. However, this is clearly not the only critical factor, since only one broad impact eQTL was identified by PEER in the MuTHER Adipose dataset, only one was identified by ICE in the Skin dataset, and no broad impact eQTL were identified when comparing results for the considerably larger DGN and NESDA studies. Given LCL are likely to allow a more controlled and homogeneous measurement of gene expression variation compared to the mixed cell populations sampled trans-eQTL compared to the FDR of less than 1% threshold used in our study. Small et al. narrowed down the targets to investigate by testing a single genotype rs4731702, which significantly lowered the multiple testing burden [−8 which corresponded to an FDR threshold of less than 10% in Grundberg et al. [trans associations are the most prone to statistical false positives, it seems reasonable to view these previous reports of broad impact eQTL with caution.We were not able to replicate a small number of broad impact eQTLs reported by previous studies in the MuTHER Adipose dataset , 61. Onetrans-eQTL seem less dispersed [In contrast to humans, broad impact eQTL have been easier to detect in model organisms and ispersed . Given tS1 FigThe components that had the strongest association with gender labels estimated by ICA (left) and PCA (right) for the Skin-Leg GTEx dataset . Gene we(PNG)Click here for additional data file.S2 Fig(A) The overlap of eQTL identified for the full dataset and splitting strategy. (B) Comparison of -log10 p-values for significant eQTL identified with the full dataset (x-axis) and splitting strategy (y-axis).This figure shows a typical result obtained when comparing CONFETI-I using the full dataset analysis to the splitting strategy. The MuTHER Adipose subset1 was analyzed using CONFETI-I with both strategies. A total of 2,633 hits were identified by both approaches and only 99 and 79 unique hits were identified for the splitting and full dataset analyses respectively. (PNG)Click here for additional data file.S3 Fig(A) The recovery rate of simulated cis- (left), trans- (middle) and broad impact eQTL (right) for a range of FDR significance thresholds for each method averaging over the 50 simulated datasets with a mix of dense and sparse confounding factors. The theoretical maximum recovery (TMR) shows the recovery when no confounding factors are included. (B) The Area Under the Curve (AUC) for the receiver operator characteristic (ROC) curves. (C) Box-plots of genomic inflation factors calculated for each method for each method across the 50 simulated datasets with a mix of sparse and dense factors.(PNG)Click here for additional data file.S4 Fig(A) MuTHER and (B) GTEx.Boxplots of the range of median λ genomic inflation factors calculated for each expression phenotype for each method in every dataset of (PNG)Click here for additional data file.S5 Figcis- and trans-eQTL versus FDR for each of the methods applied to every dataset.Plots showing the counts of (PNG)Click here for additional data file.S6 Figcis-eQTL and (Bottom) trans-eQTL in the DGN dataset and one of the two twin subsets of the NESDA study is shown for a range of FDR for all confounding factor correction methods.The number of identified (Top) (PNG)Click here for additional data file.S7 Figcis- and trans-eQTL versus FDR for each of the methods applied to every (A) MuTHER and (B) GTEx dataset.Plots showing the counts of replicating (PNG)Click here for additional data file.S8 Figcis-eQTL and trans-eQTL between the DGN dataset and two twin subsets of the NESDA study is shown for a range of FDR for all confounding factor correction methods.The number of replicating (PNG)Click here for additional data file.S9 Figcis- and trans-eQTL found by each method in the respective tissue types are ordered on the x-axis by the amount of overlap between methods. Colored bars corresponding to their method indicate that the particular eQTL replicated. The total numbers of replicating eQTL for each method is shown on at the end of each bar. Results are shown for (A) Adipose, (B) LCL, and (C) Skin twin pairs.Replicating (PNG)Click here for additional data file.S10 Figcis- and trans-eQTL found by each method in the respective tissue types are ordered on the x-axis by the amount of overlap between methods. Colored bars corresponding to their method indicate that the particular eQTL replicated. The total numbers of replicating eQTL for each method is shown on at the end of each bar. Results are shown for A. Adipose, B. Artery, C. Heart, and D. Skin tissue pairs.Replicating (PNG)Click here for additional data file.S11 Figcis-eQTL shown in gray bands within the next layer, and replicating trans-eQTL as blue bands in the innermost layer where red lines connect each trans-eQTL to the associated gene with gene annotations labeled in blue outside the circle. Replication shown for (A) Adipose, (B) LCL, (C) Skin twin pairs.Chromosomes are plotted in the outermost circles with replicating (PNG)Click here for additional data file.S12 Figcis-eQTL shown in gray bands within the next layer, and replicating trans-eQTL as blue bands in the innermost layer where red lines connect each trans-eQTL to the associated gene with gene annotations labeled in blue outside the circle. Replication after removal of pseudogenes are shown for (A) Adipose, (B) Artery, (C) Heart, and (D) Skin tissue pairs.Chromosomes are plotted in the outermost circles with replicating (PNG)Click here for additional data file.S1 Tablecis and trans-eQTL count and genes are shown for replicating broad impact eQTL.Detailed information about the dataset, significant SNP, method, (TXT)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


The phenomenon of hematogenous tumor cell dissemination in patients with solid tumors has been extensively explored over the last decades. Breast cancer research investigated at first disseminated tumor cells in the bone marrow; however, the focus soon moved to circulating tumor cells (CTCs) in the peripheral blood as blood is easily accessible without an invasive procedure. The prognostic significance of CTC presence has been shown in large studies both in adjuvant and metastatic setting and commercially available detection assays have been evaluated for monitoring in clinical trials. Beyond detection and enumeration of CTCs, the characterization of single tumor cells may enhance our knowledge on disease progression and thus optimize treatment choices. Breast cancer (BC) is the most common cancer type in women; its mortality is mostly due to distant metastatic growth. The phenomenon of hematogenous spread of single tumor cells shed from the primary tumor was first demonstrated in nineteenth century , 2. TumoThe prognostic significance of MRD in BC was first demonstrated in studies on disseminated tumor cells (DTCs) in BM. In 2005, a multicenter meta-analysis of BM aspirates collected from 4703 non-metastatic BC patients at time of diagnosis confirmed that DTC presence significantly correlates with shorter disease-free survival (DFS) and overall survival  for guiding treatment decisions and evaluating therapy response in metastatic BC is being currently investigated within the German DETECT trials (NCT01619111). Moreover, the possibility to provide an analysis of CTCs in metastatic BC on a DNA, RNA, and protein level including next-generation sequencing has been demonstrated by recent research , 73. CTCWhile the prognostic relevance of CTC detection has been proven in large clinical trials, its clinical utility remains to be demonstrated , 47. SinThe question whether the choice of systemic treatment in hormone receptor-positive HER2-negative metastatic BC patients might be driven by CTC levels has been raised by the STIC-CTC trial . In this randomized Phase III trial, the treatment decision will be left at the discretion of the clinicians or according to the number of CTCs in PB: endocrine therapy in case of a CTC count <5 CTCs/7.5 ml PB or chemotherapy in case of a CTC count ≥5/7.5 ml PB.The worldwide largest trial for therapy guidance according to the CTC status and/or CTC phenotype in metastatic BC patients is the DETECT study concept. In this multicenter study, women with HER2-negative metastatic disease and at least one HER2-positive CTC are enrolled in the DETECT III trial (NCT01619111), and women with HER2-negative BC (hormone receptor-positive or triple-negative) and exclusively HER2-negative CTCs are eligible for the DETECT IV trial (NCT02035813). The recently initiated DETECT V/CHEVENDO trial  completes the DETECT study program with a clinical trial for HER2-positive hormone receptor-positive metastatic BC patients. In the DETECT III trial, patients are randomized to standard systemic treatment of physician’s choice ± additional therapy with lapatinib. In the DETECT IV trial, patients with hormone receptor-positive tumors are treated with endocrine therapy plus everolimus, as a study medication, whereas patients with hormone receptor-positive tumors and indication for chemotherapy as well as patients with triple-negative tumors are treated with eribulin. Metastatic BC patients with HER2-positive hormone receptor-positive tumors are treated with dual HER2-targeted therapy (pertuzumab/trastuzumab) either in combination with chemotherapy or endocrine therapy within the DETECT V/CHEVENDO trial. In DETECT III and DETECT IV, presence of CTCs is mandatory for study inclusion and changes in CTC levels during the treatment are evaluated by repeated blood sampling during the therapy. The accompanying translational research projects of all DETECT studies try to generate additional knowledge of CTCs, their biology and their role in predicting treatment response using methods like single cell analysis, SNaP-Shot technology and next-generation sequencing. This may help to identify new treatment targets and provide more individualized therapy and finally further clarify the clinical value of CTCs in metastatic BC. Currently, ongoing trials on therapeutic utility of CTCs in metastatic BC are summarized in Table Evaluation of CTCs is one of the most promising biomarkers in solid tumors. In BC, presence of CTCs is an independent predictor of poor clinical outcome in both early and metastatic setting. Numerous potential clinical applications have been proposed and are currently being investigated Table . In caseIn metastatic disease, one of the most exciting possibilities is the concept of CTC-guided treatment. Two settings are currently under investigation: first, since several studies have shown that persistently high CTC levels under systemic therapy reflect disease progression sooner than imaging-based monitoring, CTC monitoring might help to identify patients who do not benefit from cytotoxic treatment. However, since an early switch to another chemotherapy regimen in case of high CTC levels after the first cycle of palliative treatment has not shown any survival benefit, it remains to be clarified which therapy options should be favored in such case. Second, a number of clinical trials aim at clarifying whether metastasized patients benefit from targeted therapy based on the molecular profile of CTCs rather than that of the primary tumor.MB-P, NK, and TF contributed significantly to this manuscript. All the authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


As a result, cardiac evaluation is not normally ordered before its use. We describe a 50-year-old female with newly diagnosed MM and no risk factors for cardiac disease that unexpectedly developed florid heart failure after 2 cycles of bortezomib and low-dose dexamethasone. 2-D echocardiogram showed dilated cardiomyopathy with severely decreased LVEF; no changes consistent with amyloid deposits or myocardial scarring were described. Coronary angiogram ruled out coronary artery disease. The mechanism of bortezomib-induced cardiomyopathy has been postulated to be through fluid retention. Based on literature review we hypothesize that the disruption of the ubiquitin-proteasome system by bortezomib may cause cardiomyopathy and severe cardiac failure. As Bortezomib is a new and promising therapy for MM patients, we recommend routinely monitoring cardiac parameters in patients undergoing this treatment.The clinical manifestations of anti-cancer drug associated cardiac side effects are diverse and can range from acutely induced cardiac arrhythmias to severe contractile dysfunction, and potentially fatal heart failure. Anthracyclines and trastuzumab cardiac toxicity have been well described and left ventricular ejection fraction (LVEF) evaluation is commonly performed before their use. Bortezomib (Velcade), a potent, specific and reversible proteasome inhibitor is approved for treatment of multiple myeloma (MM). The incidence of cardiac failure associated with bortezomib therapy in clinical trials remains incidental. Acute exacerbation of pre-existing congestive cardiac failure has been associated with this therapy but  Multiple Myeloma, a neoplastic proliferation of an aberrant colony of plasma cells, has historically been difficult to treat with a previous complete remission rate of less than five percent . SubsequBortezomib, a dipeptidyl boronic acid, is a potent, specific and reversible inhibitor of the chymotrypsin-like activity of the 26S proteasome , approveIn the SUMMIT and CREST phase II trials, the most common grade 3 and 4 adverse effects in MM patients receiving bortezomib included thrombocytopenia, fatigue, peripheral neuropathy and neutropenia , 6. Inte2 combined with cisplatin and gemcitabine who subsequently developed significant heart failure. In the conclusion, Voortman and Giaccone hypothesized that the presence of sub-clinical cardiomyopathy and inhibition of the UPP could predispose their patient to severe cardiac side effects [Only one case of bortezomib-induced cardiomyopathy with severely decreased left ventricular ejection fraction (LVEF) has been described in the literature in our knowledge. In their case report, Voortman and Giaccone described a patient with non-small cell lung cancer treated with four 3-week cycles of bortezomib 1.0 mg/m effects .2 on days 1, 4, 8, 11 of each cycle and low dose dexamethasone.A 50-year-old female patient diagnosed with stage IIIB Multiple Myeloma by Durie-Salmon criteria who was subsequently treated with bortezomib combined with low dose dexamethasone. The patient received two 3-week cycles of bortezomib 1.3 mg/mThe disease had a good response to therapy with normalization of serum electrophoresis. The patient developed tumor lysis syndrome and acute chronic renal failure after the first cycle of therapy with recovery of the renal function to previous values. During the entire course of the treatment, the patient had been on direct oncologic supervision and no new adverse effects were noticed.However, after the second cycle of therapy the patient reported increased fatigue, severe dyspnea, orthopnea and mild lower extremity edema. She did not experience any chest pain or myalgia. The patient had no documented history of coronary artery disease, hypertension, dyslipidemia or pre-existing congestive heart failure. She had no history of smoking, alcohol or drug abuse.The physical examination of the heart elicited a lateral displacement of apical impulse without any auscultatory abnormalities. Pulmonary auscultation revealed bi-lateral basilar crepitation. Central venous pressure as determined by neck vein distention was elevated at 10 cm and no hepato-jugular reflux was noted. There was mild peripheral pedal edema up to ankles bilateral.An ECG did not show signs of ischemia. The chest X-Ray showed an increased heart-chest ratio of 0.56, compared with 3 months old baseline of 0.47, and mild signs of pulmonary vasculature congestion .Troponin as well as CK-MB levels were not elevated in the serial blood tests. The brain natriuretic peptide (BNP) was high at 979. She was diagnosed clinically with congestive heart failure by Framingham criteria and improved remarkably upon administration of furosemide. All chemotherapy was discontinued and being considered as a possible cause for fluid overload. The 2D echo-cardiogram performed on the day following admission showed severe decreased LVEF to less than 10-15%, mild bi-ventricular and bi-atrial enlargement, and severe global hypokinesis . No chanThe patient underwent heart catheterization which showed severe systolic dysfunction without evidence of coronary artery disease, and was diagnosed with New York Heart Association (NYHA) class III congestive heart failure. She was treated with furosemide, beta-blockers, hydralazine and digoxin, and discharged upon symptomatic improvement. A follow-up 2-D echocardiogram, one month after the initial diagnostic echo, showed an improvement of the systolic function with an improvement in her EF to 30%.Research demonstrates that in long-term survivors of cancer, cardiovascular disease is one of the leading causes of morbidity and mortality. This is generally attributed to anti-cancer drugs’ wide range of cardiac side effects ranching from induction of cardiac arrhythmias to severe contractile dysfunction, and fatal heart failure . The carThe ubiquitin-proteasome system has been shown to play a significant role in the biology of eukaryotic cells through its degradation of cellular proteins . Inhibitde novo’ dilative cardiomyopathy.We attribute the severe reduction of the LVEF in this case to the drug therapy with bortezomib. The negative findings of coronary angiography and the lack of cardiac risk factors exclude ischemia as the cause for congestive heart failure. The patient had no prior documented history or symptoms of heart disease and the new onset cardiomegaly on heart chest X-Ray, in only three months, rules out pre-existing dilative cardiomyopathy. The lack of fever, chest pain, normal EKG and cardiac enzymes, and the clinical course made the diagnosis of viral myocarditis unlikely. Relative newly diagnosed MM in the absence of typical myocardial changes like hypertrophy, granular sparking infiltrates on the 2D echocardiogram excludes amyloid deposition from the etiology of this ‘We hypothesize that the cardiomyopathy in our patient was induced by Bortezomib through inhibition of the ubiquitin-proteasome system. The improvement of EF after just one month from the diagnosis and treatment of congestive heart failure may prove the reversibility of the effect of proteasome inhibition by this chemotherapeutic drug. As bortezomib is a new and promising therapy for multiple myeloma we recommend close monitoring of the cardiac parameters in patients undergoing this therapy.


Staphylococcus aureus by host factors may lead to the emergence of mutants better adapted to the evolving conditions at the infection site. This study was aimed at identifying the changes that occur in S. aureus exposed to the host defense mechanisms during chronic osteomyelitis and evaluating whether these changes affect the virulence of the organism. Genome assessment of two S. aureus isolates collected 13 months apart (HU-85a and HU-85c) from a host with chronic osteomyelitis was made by whole genome sequencing. Agr functionality was assessed by qRT-PCR. Isolates were tested in a rat model of osteomyelitis and the bacterial load (CFU/tibia) and the morphometric osteomyelitic index (OI) were determined. The ability of the isolates to trigger the release of proinflammatory cytokines was determined on macrophages in culture. Persistence of S. aureus within the host resulted in an agrC frameshift mutation that likely led to the observed phenotype. The capacity to cause bone tissue damage and trigger proinflammatory cytokines by macrophages of the agr-deficient, unencapsulated derivative (HU-85c) was decreased when compared with those of the isogenic CP8-capsulated parental strain (HU-85a). By comparison, no significant differences were found in the bacterial load or the OI from rats challenged with isogenic Reynolds strains , indicating that lack of CP expression alone was not likely responsible for the reduced capacity to cause tissue damage in HU-85c compared with HU-85a. The production of biofilm was significantly increased in the isogenic derivative HU-85c. Lack of agr-dependent factors makes S. aureus less virulent during chronic osteomyelitis and alteration of the agr functionality seems to permit better adaptation of S. aureus to the chronically infected host.Selection pressures exerted on  Staphylococcus aureus is an ubiquitous opportunistic pathogen that can infect, replicate and persist in humans thus making this species a worldwide threat to public health. Although S. aureus may colonize mucosal surfaces of healthy humans with unnoticeable or mild clinical features, it has the invasive potential to generate diverse life threatening infections. S. aureus is one of the most prevalent and difficult-to-eradicate pathogens causing prosthetic device-associated osteomyelitis   of a 20-year-old man with chronic osteomyelitis   and its isogenic derivatives Reynolds CP8 and Reynolds NT  was injected in the left tibia as describe previously  was assessed as detailed previously , Buenos Aires. Animal care was in accordance with the recommendations of the guidelines set forth by: the 11 report of the BVAAWF/FRAME/RSPCA/UFAW Joint Working Group on Refinement  was measured in a microplate reader . The culture medium was then removed and plates were washed twice with phosphate buffered saline (PBS). The biofilms were fixed with 100% methanol, stained with 0.5% crystal violet and washed with tap water. After addition of 30% glacial acetic acid biofilm biomasses were measured by reading the optical density at 595 nm (ODB). The intensity of crystal violet staining was expressed relative to the final culture density measured prior to the biofilm assay (biofilm: ODB)/ODG) and termed “biofilm” in the text for the sake of clarity. Agr-deficient strain S. aureus SA113 , according to the manufacturer's protocol. RNA was subjected to DNAse treatment using a RQ1 RNAse free DNAse (Promega). cDNA synthesis was performed with an ImProm-II™ Reverse Transcriptase kit (Promega). Quantitative RT-PCR was performed using the SYBR Green PCR Master Mix (Applied Biosystems) equipment and kits. cDNA was subjected to Real time PCR using the following primer pairs: rnaIII-Fw 5′-TTC ACT GTG TCG ATA ATC CA−3′, rnaIII-Rv 5′-TGA TTT CAA TGG CAC AAG AT-3′ . The (−ΔCT) value represents the difference in threshold cycle (Ct) between the target and control (16S) genes . Mouse IL-6 and TNFα were determined quantitatively at 24 h after stimulation by ELISA using specific antibody pairs (Beckton-Dickinson) as described previously  were performed by the Kruskal-Wallis test for non-parametrics. The correlation analysis of CFU vs. OI data was performed with the Spearman test for non-parametrics. Data from in vitro cytokine and biofilm production was analyzed using the Student t-test. The Prism GraphPad software (version 5.0) was used for all statistical analysis.Groups of data were statistically compared with the Mann-Whitney test for non-parametrics. S. aureus isolates HU-85a and HU-85c revealed that both were MSSA, ST188, CC1, spa type t189 and agr type I, suggesting that these longitudinally collected isolates shared a recent common ancestor at the infection site. Since HU-85a was the initial isolate and HU-85c was collected 13 months later from the same infection site, isolate HU-85a is considered to be more reflective of the “parent” isolate. Phenotypic analysis revealed that HU-85c had lost not only the capacity to express capsular polysaccharide 8 (CP8) but also α- and β-haemolysin, compared with HU-85a. These phenotypic features were stable since HU-85c did not regain CP8 or haemolysin expression upon six passages on TSA or blood agar.The characterization of agr-deficient derivative, the expression of RNAIII from the agr locus was assessed by qRT-PCR. The results showed that the relative level of RNAIII expression was significantly decreased in HU-85c when compared with HU-85a  were compared with rats infected with the derivative isolate HU-85c (agr-deficient). The bacterial load in the rat tibias and the OI of HU-85c by 14 weeks were significantly lower than those of HU-85a , compared with cells stimulated with strain HU-85a   expression but also the lack of expression of secreted virulence factors  expression (Cocchiaro et al., S. aureus during experimental infection (Tuchscherr et al., S. aureus for persistence at the infection site is multifactorial, as much as virulence, and that further research is needed to identify the specific host factors that exert selective pressure leading to the emergence of bacterial variants better adapted to persistence.Long-term persistence in the host leads to changes in the S. aureus to support unfavorable conditions is to adopt the biofilm lifestyle, which plays a relevant role in chronic persistent infections, such as prosthetic implant associated infection (Archer et al., S. aureus adaptation to the host to cause severe infection has been strengthened in a recent review article (Dastgheyb and Otto, agrC led to significantly increased biofilm production. It is known that Agr negatively controls biofilm formation and that agr mutant strains of S. aureus produce thicker biofilms (Vuong et al., agr mutants are frequently isolated from biofilm-covered prosthetic implants, it can be assumed that robust biofilms confer an advantage to bacteria during chronic infection. Agr mutants, therefore, likely represent a dead end of infection, because they lack the capacity to disseminate within the body or establish infection in other hosts (Periasamy et al., A major strategy displayed by S. aureus osteomyelitis from acute into chronic may be benefited from evolution within the host from an aggressive phenotype, perhaps better adapted to cause bone damage at the start of the infection process into a less aggressive phenotype better adapted to persistence and responsible for chronic infection refractory to antibiotic treatment. The role of the evolutionary change in S. aureus during the progression of infection from acute into chronic has gained interest over the past few years. Expression of traits that permit adaptation of S. aureus to the host may initially be due to regulatory mechanisms but, in the long run, these traits may be fixed by mutation and may be considered endpoints in microevolution. One of these endpoints in S. aureus microevolution within the host is the stable SCV, which emerge during infection (Proctor et al., In vitro studies with host cells revealed that SCVs are phenotypes that hold low virulence, but are particularly adapted to the intracellular environment for long-term persistence (Tuchscherr et al., agr deficient thus failing to produce agr-regulated factors (Fraunholz and Sinha, S. aureus ability to trigger inflammation. In addition, the loss of SSRs was significantly higher in S. aureus persisting at the infection site in patients with chronic osteomyelitis or cystic fibrosis compared with patients with acute infection (Garofalo et al., S. aureus in the bone and decreased ability to cause tissue damage in a rat model of osteomyelitis was associated with reduced capacity of S. aureus to cause inflammation, in this case through decreased triggering of proinflammatory cytokine release by macrophages. Therefore, the loss of RNAIII expression due to an agrC mutation and the reduction in virulence of the resulting variant makes functional impairment of agr another endpoint in microevolution within the host.The progression of S. aureus was able to adapt to the bone during chronic infection. Stable agr mutants arising during human chronic osteomyelitis were less able to trigger inflammation, and to cause bone damage as ascertained in the rat model of osteomyelitis. Lack of agr-dependent factors turned S. aureus less virulent and therefore it is suggested that mutations that alter the agr functionality seem to permit better adaptation of S. aureus to infection niche, making the reduction of agr functionality yet another endpoint in S. aureus microevolution within the chronically infected host.Our study demonstrates that FB, MG, and DS, conceived and designed the experiments; MN and SL, performed experiments involving the osteomyelitis rat model and data analysis; CS, DR, and DS, performed sequencing experiments and data analysis; LA and CS, performed qRT PCR experiments; MG and CG, performed cytokine experiments and data analysis; DS and CS, wrote the manuscript; FB, DR, and MG, critically revised the manuscript; DS, MG, DR, and FB, procured funding; All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.