The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations . Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms  and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies. Copy Number Variations (CNVs) have been recently receiving growing attention as a steadily increasing number of CNVs in the human genome has been identified Although increasing efforts have been put into the refinement of methods for the accurate and reliable detection of CNVs Like microsatellites and single-nucleotide polymorphisms (SNPs), CNVs represent a specific form of genetic variation. Global distribution of CNVs largely accords with population structure analysis for SNP data sets In this study we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings.To account for differences in variation patterns between populations, we created two population-specific CNVR-maps and a cross-populational standard CNVR-map . In ordeThe CNVR-maps represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.Our goal was to create CNVR maps that can provide references for population specific frequencies and the genetic localization of common CNVs as assessed by commonly used SNP arrays. Therefore we analyzed a Sub-Saharan African population (N = 450) and a sample of European ancestry (N = 717). We applied two different copy number finding algorithms (Birdseye and PennCNV) to data generated by the Affymetrix Genome-Wide Human SNP Array 6.0, an array that was specifically designed to assess copy number states throughout the genome For evaluation of CNV-finding algorithms we checked whether CNAM, PennCNV and Birdseye We decided not to use CNAM for further analysis because this algorithm is computationally expensive and dependent on subjective cut-off values, which have to be defined by the user after visual inspection of data. Processing a sample with the CNAM algorithm, which is of equal size to the one used in this study, would take up to 4 weeks , whereas applying PennCNV and Birdseye to this amount of data is accomplishable in a few days.Therefore we processed the two populations with the computationally more efficient algorithms PennCNV and Birdseye. Interestingly, albeit PennCNV and Birdseye generally report an unequal amount of copy number events, population differences in CNV characteristics are well reflected by the data. Both algorithms consistently report a higher number of CNVs that are of shorter length in the African sample . This isAlbeit obvious differences in sensitivity and specificity between Birdseye and PennCNV, it is possible to use the combination of both algorithms to identify Copy Number Variable Regions (CNVRs). For this purpose we first scanned data on an individual basis for CNVs. The total number of CNVs reported by the Birdseye algorithm for both samples was 310835 and 60532 for the PennCNV algorithm, respectively. We analyzed which CNVs were reported by both algorithms and overlapped, merging these into one CNV. Also direction of effect (gain vs. loss) had to be equal for CNVs to be used for CNVR creation. This procedure was applied to the Swiss and the Rwandese sample and yielded a total of 42669 CNVs. A more detailed description of this process can be found in the MAPK1 on chromosome 22: 20443947-20551970, NEGR1 on chromosome 1: 71641213-72520993, PARK2 on chromosome 6: 161688580-163068824). Complete lists of genes affected by CNVRs in all three maps are given in Maps were screened for genes that overlap with CNVRs. A total of 862 genes were affected by CNVRs in the CP-Map  of these genes are found to map to CNVRs in both populations. One fourth (25%) were found to be affected by copy number variation in the Rwandese sample only, whereas 41% of these genes were found to be copy number variable only in the Swiss population. A detailed overview and lists of genes are available in Comparison with data downloaded from the Database of Genomic Variants (DGV – current version: hg18.v7.aug.2009) revealed substantial overlap between our CP-map and entries of single CNVs in the DGV. Out of 56497 autosomal copy number events reported in the DGV, 11545 clustered in CNVRs as defined by the CP-map. Separate comparisons for the CNVR maps created on the single population basis (SW-map and RW-map) resulted in 13773 overlapping CNVs for the Rwandese based CNVR-map and 14369 for the Swiss based CNVR-map . We restThe SW-map and the RW-map contain 80 and 191 CNVRs, respectively, that have not yet been reported to the DGV. We further compared CN-events that were detected by both algorithms to a map comprising 229 autosomal CNVRs constructed through analyzing 700 Chinese individuals with the Affymetrix 500K processed with the CNAT algorithm We also could observe overlaps between our CP-map and a list of 161 autosomal CNVRs identified with CNV-seq that used direct sequencing reads of Dr. C. Venter and Dr. J. Watson For further validation of our CP-map we processed 13 individuals with the Illumina Human 1M-Duo BeadChip,  and 9 samples of the Swiss population with the Nimblegen 385K aCGH array. We detected a total of 930 CNVs applying PennCNV on the Illumina 1M-Duo BeadChip. Comparison of these 930 CNVs to the CP-map showed 501 overlapping copy number events with the CNVRs in the map.The 385K data from Swiss individuals was analyzed using the wuHMM algorithm Segmental Dups Track from UCSC table browser  and analyzed whether the CNVRs as reported in our maps overlap with known segmental duplications. Out of 36119 annotated segmental duplications mapping to “non-random” chromosomal regions, 8911 overlapped with 178 CNVRs comprised in the CP-Map.As segmental duplications are known to play an important role in CNV formation First, we note that all tested algorithms were able to detect large-scale genomic aberrations ranging from a 14 Mb deletion to a whole chromosome triplication. We therefore conclude that the SNP-Array under study can be used in cytogenetic research. Yet, as depicted in To decrease the probability that false positive CNV-calls are used for generating the CNVR-maps, we only took concordant CNV-calls of two independent algorithms into account that overlapped in at least two individuals. On the other hand we screened hundreds of individuals, thus increasing sensitivity.The maps created this way contain information on CNV hotspots and frequency distributions of CNVs. They represent first drafts of population-specific maps as well as a cross-populational map, probably comprising phylogenetically older CNVRs. We report a substantial overlap with CNVR-regions created from previously published data and also were able to validate our maps using different array types, which vary in resolution and their mode of operation due to different chip-architectures.We argue, that detection accuracy of CNVs can benefit from information on base rates of CNVs in the general population as provided in our CNVR-maps. Two ways to integrate prior knowledge about common CNVRs are plausible: On the one hand, the restriction of CNV-analysis to markers known to be located in CNVRs allows the use of computationally more expensive algorithms that could outperform current methods. On the other hand information gained from CNVR-maps can serve as prior probabilities in Bayesian terms that can be incorporated in CNV-detection algorithms. In order to alleviate assessing how common CNVs relate to variation in phenotypes, higher sensitivity and specificity rates in CNV-detection are needed. We suggest that the use of prior knowledge about CNVRs as provided by our maps, could yield the necessary refinement of methods. We note that our CNVR-maps do not depict structural variations on the p-arms of chromosome 13, 14, 15, 21 and 22 due to lack of markers in these areas on the Affymetrix 6.0 SNP Array. These regions only contain non-chromosome specific highly repetitive DNA sequences and are usually not covered by microarrays. It is important to stress that the array design for the Affymetrix Human SNP Array 6.0 relied mainly on variation detected in non-African populations an thus our Rw-map might underestimate the total variation present in the Rwandese sample. Specially designed custom arrays that take population specificity into account by making use of information provided by large scale sequencing endeavors such as the 1000 Genomes Project In summary, we generated comprehensive CNVR-maps using micro-arrays in two cohorts of ethnically distinct individuals from Switzerland and Sub-Saharan Africa. The maps represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.After complete description of the study to the participants, written informed consent was obtained. The ethics committee of the Canton of Zurich, Switzerland specifically approved this study.Because most of the study subjects were analphabetic, they were fully informed verbally before participation. The informed consent was also in verbal form for the same reason. Both the ethical boards of Mbarara University of Science and Technology, Uganda and the University of Konstanz, Germany specifically approved this study.Research permit for the study was obtained by the Ugandan government:PRESIDENT'S OFFICERESEARCH SECRETARIATP.O BOX 7168KAMPALA, UGANDAResearch Identity Cards were issued to the researchers by the Uganda National Council for Science and Technology, P.O. Box 6884 Kampala granting them access to the Nakivale refugee camp, which is a restricted area that can only be accessed for persons in possession of the according permits.We recruited a total number of N = 1167 individuals from two ethnically distinct populations from Switzerland and Rwanda. The Swiss population consisted of 717 healthy, young individuals. The Rwandese sample is comprised of 450 survivors of the Rwandese Civil War recruited in the Nakivale refugee camp, Uganda Two randomly chosen subsets consisting of 9 individuals of the Swiss population and a subsample comprising 7 Swiss and 6 Rwandese individuals were processed on the Nimblegen 385K aCGH array and the Illumina 1M-Duo BeadChip, respectively. Data analysis of the Nimblegen 385K aCGH array was conducted applying the wuHMM algorithm The screening sample that was used for gross validation of methods consisted of three individuals with previously verified large-scale aberrations. DNA for these three subjects was isolated from whole blood and hybridized to the Affymetrix Genome-Wide Human SNP Array 6.0. Data of the validation sample was analyzed using Birdseye, PennCNV and the CNAM algorithm of the commercial Software package Helix Tree Statistical analysis and plots were done using R Figure S1This workflow chart is depicting the proceedings of the CNV congruency check and the merge process. We first applied an overlap-check for the CNVs reported by the different algorithms. If CNV events had overlaps, copy number state (gain vs. loss) was also evaluated. Out of all CNVs detected, only 1916 events in the Swiss sample and 324 CNVs in the Rwandese sample had to be removed due to incongruent direction of effect. This means that in the Swiss sample a total of 858 overlapping CNVs had been reported as loss by one algorithm and as gain in copy number variation by the other algorithm. In the Rwandese sample, this was the case for only 162 events. All CNV events that had not at least partially been detected by the second algorithm were discarded. A total of 22,700 “merged” CNV events survived this quality control in the Swiss sample and 19,969 CNVs in the Rwandese population, respectively.(EPS)Click here for additional data file.Methods S1(DOC)Click here for additional data file.Table S1(XLSX)Click here for additional data file.Table S2(XLSX)Click here for additional data file.


The whole process of magnetosome biogenesis can be divided into four sequential steps; (i) cytoplasmic membrane invagination, (ii) magnetosomes alignment, (iii) iron crystal nucleation and (iv) species-dependent mineral size and shape control. Since both magnetite and greigite are a mix of iron (III) and iron (II), iron redox state management within the magnetosome vesicle is a key issue. Recently, studies have started pointing out the importance of a MTB-specific c-type cytochrome domain found in several magnetosome-associated proteins . This magnetochrome (MCR) domain is almost always found in tandem, and this tandem is either found alone (MamT), in combination with a PDZ domain (MamP), a domain of unknown function (MamX) or with a trypsin combined to one or two PDZ domains (MamE). By taking advantage of new genomic data available on MTB and a recent structural study of MamP, which helped define the MCR domain boundaries, we attempt to retrace the evolutionary history within and between the different MCR-containing proteins. We propose that the observed tandem repeat of MCR is the result of a convergent evolution and attempt to explain why this domain is rarely found alone.Magnetotactic bacteria (MTB) can swim along Earth's magnetic field lines, thanks to the alignment of dedicated cytoplasmic organelles. These organelles, termed magnetosomes, are proteolipidic vesicles filled by a 35–120 nm crystal of either magnetite or greigite. The formation and alignment of magnetosomes are mediated by a group of specific genes, the  These magnetosomes are aligned within the cytoplasm of magnetotactic bacteria (MTB), acting as a compass needle for orientation. A tentative selective advantage would be an efficient localization of the cells in vicinity of the oxic-anoxic transition zone in the water column at their preferred position in the oxygen  gradient. Since their first scientific description by RP Blakemore in 1975  the Nitrospirae phylum, including several uncultured strains and, (iii) the Planctomycetes-Verrucomicrobia-Chlamydiae (PVC)  genes. The mam genes are organized in clusters in the genome of MTB, in some cases defining a bona fide magnetosome genomic island (MAI)  the Komeili, . CurrentKomeili, . A core c-type cytochromes motif endemic in MTB and potentially play a role in the iron biocrystallization process that takes place inside the magnetosome , whereas chemical synthesis requires mixing iron(II) and iron(III) in appropriate proportion .In a recent study focused on the biochemistry of MamP and its structural characterization, it was found that MamP displays ferroxidase activity . The phenotypes of the mamX, mamZ and mamH mutants led the authors to propose a functional MamXZH interaction that would form an iron oxidoreductase and transport complex through the magnetosome membrane. The understanding of this system and the role of the MCR-containing protein MamX need further study. For example, electron transfer partners and directionality of electron flow remain unknown for MamX.The situation is somehow clearer in Altogether the bioinformatics and experimental data available on MCR-containing Mam proteins suggest their involvement in iron redox chemistry to ensure the proper mineralization of magnetosomes. By taking advantage of new genomic data available and recent structural data on MCR domain, we attempt to retrace the evolutionary history of this domain within and between the different MCR-containing proteins. We also hypothesize on the reasons why this domain is rarely found alone but rather in tandem repeats.c-type cytochrome domains of MamP were already proposed to define a novel domain that is only found in MTB . The overall fold of MamP in the crystal revealed a dimer with both monomers mainly stabilized by numerous contacts between their PDZ domains. The first magnetochrome domain (MCR1) is in contact with its own PDZ domain, while the second (MCR2) is projected above the PDZ domain of the other monomer. This structural study allowed the first fold description of a MCR domain, substantiating its uniqueness at the structural level. Indeed, a structural homology search with DaliLite v.3 returns no significant hits, demonstrating the specificity and the uniqueness of these domains . A detailed examination of the structure identified two hydrophobic residues, which delineate the N-terminal and C-terminal regions of the MCR domain. In the fold, these two residues interact hydrophobically to close off the domain. Based on these observations, we proposed a more accurate delineation of a typical MCR domain: ψ 1X5−9PHX5−9CX2CHψ2  closes the MCR fold by interacting with the ψ1 residue. Being composed of 19–28 residues, it represents the smallest mono-heme cytochrome known to date (the mono-heme cytochrome c-553 from Bacillus pasteurii contains 71 residues surrounding the heme moiety). Overall, this results in a highly solvent-exposed heme moiety, with all four solvent edges exposed  and separated them into MCR1 and MCR2 as described above. A protein sequences alignment was computed with all the individual MCR using the Muscle algorithm Edgar,  and a phA rather simple evolutionary scheme can be proposed for the MCR-containing Mam proteins where the basic scheme is provided by model #2: an initial sequence divergence event followed by domain duplication. It is interesting to note that even based on short MCR domain sequences, one can relatively easily infer the nature of the protein to which it belongs .Alphaproteobacteria, suggesting that it evolved relatively recently. However, its evolutionary history based on the MCR domain only suggests that it did not emerge from the tandem of another MCR-containing protein like previously existing MamP or MamE. What we may be witnessing here is an example of convergent evolution where the tandem repeat is linked to the functional role of the protein. Indeed, this domain is almost always found in tandem and there are only rare examples where it is found either, alone (MamE of Candidatus Magnetobacterium bavaricum and MamE' of SS-5) or in triplicate (MamE of strain MC-1). It is tempting to link this pattern to iron and magnetite (or greigite) chemistry. Both magnetite and greigite are a mix of one iron(II) and two iron(III) equivalents. The possibility to abstract or give two electrons by a pair of magnetochrome domains suggests its involvement directly in magnetite or greigite crystal production, not just the iron chemistry that requires a single electron. Such use of two monoheme cytochromes was also suggested to evolve in order to adapt to the storage of the two electron generated from sulfite oxidation  and the magnetite/greigite crystals have diversified since then. Because magnetochrome-containing proteins are the only redox proteins associated to the magnetosome this raises interesting possibilities possibly linking magnetite/greigite crystal shape to the evolution of these magnetochrome-containing proteins.A hypothesis concerning the evolution of magnetotactic bacteria suggests they all evolved from a common ancestor 2.5–3.0 billion years ago when levels of atmospheric oxygen were low and anaerobic to microaerobic environments dominated. At this time, magnetite/greigite crystals likely did not serve a role in magnetotaxis but rather in scavenging reactive oxygen species and later, when atmospheric oxygen levels increased, served to aid MTB in navigation , Magnetospirillum gryphiswaldense MSR-1 (GenBank: CU459003.1), Magnetospirillum magnetotacticum MS-1 (JGI project 402922), Magnetococcussp. MC-1 (GenBank: CP000471), Magnetovibrio blakemorei MV-1 (GenBank: FP102531), Magnetospira sp. QH-2 (EMBL: FO538765), strain SS-5 , Candidatus Magnetobacterium bavaricum , Candidatus Desulfamplus magnetomortis BW-1 (GenBank: JN830627–JN830646 and JN845570–JN845575).The complete genomes or contigs discussed in this paper include: c nitrite reductase.From a structure based sequence alignment we devised a protein pattern that harvests almost all the magnetochrome domains without many false positive. This pattern is the following:Initial multiple alignment were generated using the MUSCLE program Edgar, , applyinThe 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.


Cytoplasmic class XI myosins are the fastest processive motors known. This class functions in high-velocity cytoplasmic streaming in various plant cells from algae to angiosperms. The velocities at which they process are ten times faster than its closest class V homologues.viz. CDD search, string-based searches and phylogenetic clustering. We have identified nine myosin XI genes in sorghum and seven in grape by sequence searches. Both the plants possess one gene product each belonging to myosin type VIII as well. During this process, we have re-defined the gene boundaries for three sorghum myosin XI genes using fgenesh program.To provide sequence determinants and structural rationale for the molecular mechanism of this fast pace myosin, we have compared the sequences from myosin class V and XI through Evolutionary Trace (ET) analysis. The current study identifies class-specific residues of myosin XI spread over the actin binding site, ATP binding site and light chain binding neck region. Sequences for ET analysis were accumulated from six plant genomes, using literature based text search and sequence searches, followed by triple validation Molecular modelling and subsequent analysis of putative interactions involving these class-specific residues suggest a structural basis for the molecular mechanism behind high velocity of plant myosin XI. We propose a model of a more flexible switch I region that contributes to faster ADP release leading to high velocity movement of the algal myosin XI. Myosins play many important roles within plant cells such as organelle trafficking ,2, remodetc ,7. Thougetc , only thetc , derivedetc  might haMyosins, in general, function through an ATP hydrolysis cycle by converting the hydrolysis energy to allosteric conformational changes within the motor head as well as the neck region leading to motion. Swinging cross-bridge hypothesis, proposed by H. E. Huxley, has been the most popular model to explain the molecular mechanism of energy transduction in myosins. Numerous biochemical and biophysical experiments thereafter helped to improve it to the present day swinging lever arm model , accordiIn plants, from algae to angiosperms, the high velocity cytoplasmic streaming (of the range 40-60 μm/s) is driven by myosins . Myosin Due to the difficulties in crystallization of actin-myosin complex, the actual actin binding residues are not known even in well-characterized myosins like myosin II and V. However, docking studies have revealed the actomyosin interface residues . The members of this database were used as queries in PSI-BLAST against sorghum and grape genomes. A relatively relaxed E-value cut-off of 10-5 and a query length coverage filter of 25% were used in PSI-BLAST in order to retrieve even distantly related myosins. Hits were filtered for myosins through a three-fold validation procedure. In the first level, string-based scripts were used to gather completely or partly annotated myosin hits (Category 1) from respective GENEPEPT flat files. Mis-annotated or unannotated hits (Category 2) were gathered by using perl scripts based on the percentage identity of the hit with the query sequence. An identity cut-off of 35% was used here. The two categories of myosin hits obtained from sorghum and grape were checked for their domain architecture using PFAM [A database of 143 myosin sequences belonging to 19 different classes was developed through text search in NCBI and by consulting a database of myosins - The myosin home page  and clustering of conserved residues were analysed.Class-specific residues were identified using ET method for myosin V and XI members. Multiple sequence alignment for 30 sequences from 10 genomes was done using CLUSTALW . SequencChara corallina) was obtained using MODELLER [1W7J) was used as a template [viz. WHATIF [Homology model of the algal myosin XI (MODELLER . Myosin template . Sequenc. WHATIF  and VERI. WHATIF . The mod. WHATIF .Sorghum bicolor and Vitis vinifera ) were scanned for myosin types using PSI-BLAST preceding a three-fold validation as mentioned in Methods. Additional myosin class V and XI sequences were obtained from three different plant genomes: Arabidopsis thaliana, Oryza sativa and Zea mays. Motor domains of validated hits, including those with new predicted boundaries . There is a high level of sequence conservation between the two classes at the active site residues. Nevertheless, the two classes differ considerably at functionally important sites and mechanistic details, according to the literature (as mentioned in Introduction). Myosin XI head contains 21 class-specific residues, out of which six appear on the actin binding sites. Known actin binding sites on myosin II fall into three regions: A primary binding site comprising of a helix-loop-helix and an adjacent helix, a secondary site which is a charged loop and a tertiary site lying at the front or nose of the myosin molecule . The corIt was recently shown that the ATP-induced motor head dissociation in myosin XI occurs at a very fast rate of 2200s-1, resulting in stunningly fast movement of myosin XI [Apart from the three key actin binding sites, surrounding residues may also get involved in actin binding. Few of the class-specific residues observed near the actin binding site may also contribute to binding strength  and it stabilizes the switch I through a favourable interaction with Glu256. In plant myosin XI, there is an interesting co-evolution at this location, where a spatially proximate Thr212 is replaced by Val and Glu255 by Pro Figure  &6B. As Chara coralina, which are stabilised by weak dispersion forces, and hence the closure of the cleft may become inefficient in Chara myosin.A closer investigation of the ATP binding site Figure  &6D of tWe have also examined the neck region  for class-specific residues and the conservation of amino acid types. We identified a cluster of three class-specific residues, two of which  have potential interactions with the converter domain in the case of myosin V  Ile762  Spatially close to this "hydrophobic triad" in myosin V , the side chain of Lys766, the second class specific residue, interacts with the backbone oxygen of Val713 located at the converter domain by forming a hydrogen bond. In plant myosin XI, Lys766 is replaced by a Valine in a class specific manner. As a result of this change, a weak hydrophobic interaction alone could prevail between the neck and the converter domains of myosin XI  at the neck region are shown on the multiple sequence alignment.Click here for file


Inflammatory changes on the endothelium are responsible for leukocyte recruitment to plaques in atherosclerosis. Noninvasive assessment of treatment-effects on endothelial inflammation may be of use for managing medical therapy and developing novel therapies. We hypothesized that molecular imaging of vascular cell adhesion molecule-1 (VCAM-1) with contrast enhanced ultrasound (CEU) could assess treatment effects on endothelial phenotype in early atherosclerosis.VCAM) and control microbubbles (MBCtr). Aortic wall thickness was assessed with high frequency ultrasound. Histology, immunohistology and Western blot were used to assess plaque burden and VCAM-1 expression.Mice with atherosclerosis produced by gene deletion of the LDL-receptor and Apobec-1-editing protein were studied. At 12 weeks of age, mice received 8 weeks of regular chow or atorvastatin-enriched chow (10 mg/kg/day). At 20 weeks, CEU molecular imaging for aortic endothelial VCAM-1 expression was performed with VCAM-1-targeted , but not in statin-treated animals .Plaque burden was reduced on histology, and VCAM-1 was reduced on Western blot by atorvastatin, which corresponded to less endothelial expression of VCAM-1 on immunohistology. High frequency ultrasound did not detect differences in aortic wall thickness between groups. In contrast, CEU molecular imaging demonstrated selective signal enhancement for MBNon-invasive CEU molecular imaging detects the effects of anti-inflammatory treatment on endothelial inflammation in early atherosclerosis. This easily accessible, low-cost technique may be useful in assessing treatment effects in preclinical research and in patients. Large primary and secondary prevention trials have consistently shown a risk reduction for cardiovascular events when patients with established vascular disease, diabetes or hyperlipidemia are treated with statins Endothelial expression of the inflammatory cell adhesion molecule VCAM-1 plays an important role in the pathogenesis of atherosclerosis by regulating leukocyte recruitment to nascent atherosclerotic plaques in the arterial wall in vivo ultrasound molecular imaging of the expression of VCAM-1 could be used to assess the effects of pharmacologic therapy on vascular endothelial phenotype in a mouse model of early atherosclerosis. To examine how molecular imaging of the endothelial phenotype compares to assessment of vessel wall morphology, high frequency ultrasound imaging of the aortic arch was also performed.In the present study, we therefore investigated whether All experiments were performed in accordance with Swiss Federal Legislation and were approved by the local Animal Care and Use Committee of the University Hospital of Basel and the ethics committee of the Veterinary Office of the Canton of Basel. Mice with a double knockout for the LDL receptor and the Apobec-1 editing enzyme on a C57Bl/6 background were used. These mice develop atherosclerosis in a predictable, time dependent fashion while on a normal chow diet VCAM) were prepared by conjugation of biotinylated rat anti-mouse VCAM-1 antibody (MK 2.7) to the microbubble surface using biotin-streptavidin-biotin linking as previously described Ctr) bearing a non-specific isotype control antibody  were also prepared. Microbubble concentration and size were measured by electrozone sensing . There was no size difference between MBVCAM-1 and MBCtr .Biotinylated, lipid-shelled decafluorobutane microbubbles were prepared by sonication of a gas saturated aqueous suspension of distearoylphosphatidylcholine , polyoxyethylene-40-stearate , and 1,2-distearoyl-sn-glycero-phosphoethanolamine-N-[biotinyl(polyethylene glycol)-3400] . Microbubbles targeted to VCAM-1 (MBVCAM or MBCtr (1×106 microbubbles per injection) were injected intravenously via a cannulated jugular vein in random order, while ultrasound imaging was paused. Eight minutes after microbubble injection, imaging was resumed at a mechanical index of 0.87. The first acquired image frame was used to derive the total amount of microbubbles present within the aorta. The microbubbles in the ultrasound beam were then destroyed with several (>10) image frames. Several image frames at a long pulsing interval (10 sec) were then acquired to measure signal attributable to freely circulating microbubbles. Data were log-linear converted, and frames representing freely circulating microbubbles were digitally subtracted from the first image to derive signal from attached microbubbles alone. Contrast intensity was measured from a region of interest encompassing the sinus of valsalva, the ascending aorta and the initial portion of the aortic arch, extending into the origin of the brachiocephalic artery. The selection of the region of interest was guided by fundamental frequency anatomic images of the ascending aorta acquired at 14 MHz at the end of each individual imaging sequence.Ultrasound imaging  was performed with a high-frequency linear-array probe (15L8) held in place by a railed gantry system. The ascending aorta of the mouse was imaged in a long axis plane from a right parasternal window, care was taken to include the sinus of valsalva and the take-off of the brachiocephalic artery in the image. Contrast enhanced ultrasound (CEU) was performed with power modulation and pulse inversion (Contrast Pulse Sequence) imaging at a centerline frequency of 7 MHz and a dynamic range of 50 dB. The gain settings were adjusted to levels just below visible noise speckle and held constant. MBHigh frequency imaging of the aortic arch in long axis was performed with a Vevo 2100  imaging system equipped with an MS550D transducer operating at 40 MHz at the shortest possible pulse length. This system has an axial resolution of 40 µm All histological preparations were done on sections from the base of the aorta at the sinus of valsalva and from the ascending aorta at the take off of the brachiocephalic artery. Due to the limited amount of tissue available and different embedding techniques, it was not possible to perform every histological assessment in every animal. For assessment of plaque burden, Movat’s pentachrome staining was performed. Paraffin-embedded aortas were sectioned in short axis from the aortic valve to the distal portion of the ascending aorta at the take off of the brachiocephalic artery and stained using a commercially available Movat’s pentachrome staining kit . Histological plaque burden was measured as the plaque area in relation to the vessel area defined by the internal elastic lamina using Image-J.Fluorescent immunohistochemistry was performed to qualitatively assess the endothelial expression of VCAM-1. Frozen aortic sections were mounted on glass slides, fixed in −20°C Acetone, air-dried, blocked with 10% goat serum in TBS/FSGO and incubated overnight at 4°C with anti-VCAM-1 . Goat anti-rat Alexa-633  was added and incubated for 1 hour at room temperature. Sections were mounted with Prolong gold antifade mounting medium and imaged on a Zeiss LSM 710 confocal microscope.Immunostaining for Mac-2 was performed on paraffin-embedded aortic sections after heat-induced antigen retrieval with 10 mM NaCitrate Buffer (pH = 6). Sections were blocked with 3% BSA in PBS and incubated overnight at 4°C with anti-Mac-2 . Goat anti-rat Alexa-633  was added and incubated for 1 hour at room temperature. Sections were mounted with Prolong gold antifade mounting medium containing DAPI  and imaged on a Zeiss LSM 710 confocal microscope. For quantitative immunocytochemical comparisons of macrophage content, the number of positively stained pixels was counted on thresholded pictures and normalized to total number of pixels of the vessel area encompassed by the internal elastic lamina using Image-J. Thresholds were defined as the mean background intensity plus 40 times the standard deviation in each individual picture. For each mouse at least two sections on different slides were imaged and quantified for both the base and ascending aorta.http://rsbweb.nih.gov/ij/).Western blotting for the expression of VCAM-1 was performed in 5 statin treated animals and 5 non-treated animals that had not undergone ultrasound imaging. The ascending portion of the aorta was homogenized in lysis buffer  containing 80 mmol/L Pefabloc SC plus (Roche). Protein concentration was measured using the Micro BCA (bicinchoninic acid) protein assay kit (Thermo Scientific). 10 µg of protein were resolved on SDS-PAGE and transferred to Polyvinylidene fluoride (PVDF) membranes (Amersham). Membranes were probed with monoclonal rat anti-mouse VCAM-1  and monoclonal anti-α-tubulin  antibodies. Blots were subsequently incubated with horseradish peroxidase-conjugated secondary antibodies (Jackson Immuno Research) and band intensities were detected by enhanced chemiluminescence  and quantitated using NIH ImageJ software . Data are expressed as mean±SEM unless stated otherwise. Single comparisons between the two animal groups were performed with a Mann Whitney test. Friedman’s repeated measures ANOVA with Dunn’s post hoc test was used to compare differences in microbubble signals within and between animal groups. A p value <0.05 (2-sided) was considered statistically significant.Statin treatment did not significantly influence total cholesterol or HDL-cholesterol levels, despite a trend towards decreased levels for both . In contMovat’s Pentachrome stains in non-treated animals showed macrophage-rich type III atherosclerotic lesions in the sinus of valsalva that occupied close to 8% of the vessel lumen area. Statin treatment resulted in a significant reduction in plaque burden in the sinuses of valsalva. In the ascending aorta, there was a similar, albeit statistically nonsignificant reduction in plaque area in animals that were receiving statin treatment .Expression of VCAM-1 on Western blot was reduced by 47%  in statiThe aortic wall thickness was measured at atherosclerosis-prone sites throughout the ascending aorta. Despite histological differences in vessel wall thickness (59±22 µm) between statin treated and non-treated animals in plaque areas in the sinuses of valsalva that were above the theoretical axial resolution of the ultrasound system, blinded assessment of vessel wall thicknessess did not differ between the treatment groups at any of the measured points .Left ventricular fractional shortening, aortic diameter, and aortic peak flow velocity did not systematically differ between statin treated and non-treated animals, indicating that shear rate conditions that oppose selective microbubble attachment in the ascending aorta did not differ between animal groups .CEU molecular imaging showed selective signal enhancement for microbubbles targeted to VCAM-1 in non-treated animals, with a 120% signal increase relative to signal from control microbubbles in the same animal group. In contrast, signal for microbubbles targeted to VCAM-1 was not increased over control microbubble signal in animals on statin treatment .Given the differences in plaque burden between the sinus of valsalva and the distal ascending aorta on histology, in an exploratory analysis, we also assessed the regional effect of statin treatment on CEU molecular imaging signal for microbubbles targeted to VCAM-1. In non-treated animals, signal for VCAM-1 targeted microbubbles was slightly, but not significantly lower in the distal ascending aorta compared to the base of the aorta. In both regions of interest, statin treatment resulted in a similar reduction in signal with significantly lower signals than in animals in non-treated animals .Current treatment of atherosclerosis is to a large extent directed at patients with established clinical disease. However, it can be anticipated that in the future existing and emerging therapeutic agents will be employed at earlier stages during the disease process with the goal of quelling the molecular pathways that lead to the buildup of plaques, including endothelial recruitment of inflammatory cells. Such strategies will also create the need for a method for early, noninvasive detection of treatment effects. The results from our study indicate that ultrasound molecular imaging can detect the impact of therapies aimed at reducing endothelial inflammation during early disease stages in a murine model of atherosclerosis when high frequency morphologic plaque imaging is unable to detect a treatment effect.de novo synthesis of the protein. Regulation of VCAM-1 gene transcription involves the activation of NF-kB as a response to inflammatory stimuli. Statin treatment has been shown to reduce the activation NF-kB in endothelial cells The mouse model of atherosclerosis that we studied is characterized by a progressive development of atherosclerotic plaques with the first minimal lesions detected around 10 weeks of age, when treatment was started in our study. At 20 weeks of age, the mouse model shows lesions that cover about 5% of the total aortic surface, but 25–30% of the proximal aorta The effect of statins in different mouse models of atherosclerosis has been variable It has previously been shown that molecular imaging with targeted MRI and SPECT probes can be used for assessing the effect of statins in animal models of advanced atherosclerosis VCAM on the aortic endothelial surface. Due to the small size of the murine model of atherosclerosis in our study, the expression of VCAM-1 was imaged in the aorta. With regards to translation into humans, the aorta will not be a suitable vessel for imaging, nor will it be likely that noninvasive ultrasound molecular imaging of the coronary arteries will be feasible in the near future. However, the carotid artery is an accepted surrogate vessel for the assessment of cardiovascular risk and will be easily accessible for ultrasound molecular imaging in humans. Given the differences in plaque burden in the sinuses of valsalva versus the ascending aorta, we performed a regional analysis of VCAM-1 targetd signal. This analysis was not compared to regional assessment of VCAM-1 expression, in particular Western blot, and is to be viewed as an exploratory analysis. Last, from our study we cannot draw conclusions on whether the assessment of treatment effects early during the pathogenesis of atherosclerosis would be of value in improving disease management.Several limitations of our study should be mentioned. We used high-dose statin treatment in our animal model, and did not evaluate whether ultrasound molecular imaging would be capable of detecting graded responses to several different doses of statin treatment. As we did not perform examinations at multiple timepoints, we cannot comment on whether changes in vascular inflammatory phenotype precede changes in histology. Further it has been shown that microbubble targeting efficiency not only depends on the expression of target molecules, but also on endothelial glycocalyx thickness In conclusion, we show that ultrasound molecular imaging can detect the effects of statin therapy on early inflammatory processes in atherosclerosis at a timepoint when high-resolution imaging is unable to ascertain differences in plaque development. Such a low-cost, easily accesible imaging strategy could in the future be useful in the future for assessing the effect of drug treatment in individual patients, but also for screening of the effect of novel drug classes other than statins in the preclinical field.The authors would like to thank Dr. Jonathan Lindner for his scientific input and critical review of this manuscript.


The resource aims at providing fast and easy access to relevant information, in particular to the complex network of interacting cell types and molecules, from the wealth of publications in the field through visualization interfaces. It provides structured information on more than 7000 experimentally validated interactions between molecules, bioprocesses and environmental factors. Information is manually derived by critical reading of the scientific literature from expert annotators. Hematopoiesis-relevant interactions are accompanied with context information such as model organisms and experimental methods for enabling assessment of reliability and relevance of experimental results. Usage of established vocabularies facilitates downstream bioinformatics applications and to convert the results into complex networks. Several predefined datasets (Selected topics) offer insights into stem cell behavior, the stem cell niche and signaling processes supporting hematopoietic stem cell maintenance. HSC-Explorer provides a versatile web-based resource for scientists entering the field of hematopoiesis enabling users to inspect the associated biological processes through interactive graphical presentation.HSC-Explorer ( All blood cells are ultimately generated from multipotent hematopoietic stem cells (HSCs) which are the only cell type capable of long-term (if not life-long) self-renewal . According to the classical view of hematopoiesis, HSCs generate multipotent and committed progenitors which produce terminally differentiated cells. Characterized by a massive production rate . Terminologies like Long-Term (LT) and Short-Term (ST) HSCs are functionally defined  and do not necessarily correlate with certain enrichment protocols. However, the use of such terms is not consistent throughout the literature. This inconsistency together with the intrinsic heterogeneity of the HSC compartment Currently, hematopoiesis-specific databases such as Hematopoietic Fingerprints Here we present HSC-Explorer, a publicly available, manually curated, integrative database collecting literature-derived knowledge about the different hematopoietic stem cell subpopulations and their behavior in repopulation activity, self-renewal and quiescence and how these processes are regulated by intrinsic and extrinsic factors. The resource covers in particular the early steps of differentiation from the most primitive hematopoietic stem cells (HSC) to more differentiated multipotent progenitor cells (MPP) in adult mice. Multiple search options and an interactive graphical tool enable information retrieval of the manifold interrelations between factors and processes and their presentation as informative network structures.The complete content of the database is generated by biocurators who manually extract hematopoiesis-specific and experimentally verified information from the scientific literature. Annotation is performed according to the procedures applied in our CIDeR database The general information (A) refers to the broader context of the experimental findings. This includes the literature reference, the organism used for the experiments and information about the organism strain, gender and age, if specified in the publication. The information about mouse strains is especially important for the purification of murine HSCs since some frequently used stem cell markers, including Thy-1 and Sca-1, are not conserved among mouse strains All data from HSC-Explorer can be downloaded as flat files or in SBML format (Systems Biology Markup Language), a free and open interchange XML format for computer models of biological processes One crucial question in hematopoietic stem cell biology is the identification of markers that distinguish primitive stem cells from more differentiated progenitors with reduced self-renewal and repopulation activity In addition to their repopulation activity, hematopoietic stem cells are further characterized in HSC-Explorer by their proliferation state (quiescence) and their capacity to self-renew. The term “self-renewal” is only used in cases of reconstitution of secondary recipients.In The concept of the hematopoietic stem cell niche has been introduced in 1978 by Schofield Distinct HSC sub-populations are also characterized by their expression profiles. Several microarray studies have been performed to identify candidate genes involved in differentiation and self-renewal or quiescence Therefore we compared the expression profiles, including microarray data and qPCR data, of four different HSC sub-populations , HSC (Rho(lo)KSL), HSC , HSC (Thy1(lo)CD135-KSL) ) with long-term repopulation activity. As shown in the graph on the homepage (Selected topic) the gene sets are quite different and only two genes, Procr and Rbp1, have been identified in all four subpopulations. Procr is a well-known surface antigen already used for the purification of hematopoietic stem cells. The retinol-binding protein Rbp1, an important factor in retinoic acid synthesis, is involved in granulopoiesis +CD48-KSL [cKit+Sca1+Lin−]). Genes are described by their official Entrez Gene name A flexible web-based interface of HSC-Explorer allows both general and advanced searching of the database. By default all fields are searched. However, the user has the option to restrict the search to a specific category such as ‘Immunophenotype’, ‘Gene’, ‘Biological process’, ‘Tissue’, ‘Chemical compound’, ‘miRNA’, the PubMed identifier as well as the authors of articles. The category ‘Immunophenotype’ had to be introduced in this context as currently no single defined hematopoietic stem cell exists, instead, a variety of different HSC-sub-populations have been described in the literature so far. All these sub-types are characterized by a combination of markers used for their purification, the so-called immunophenotype  on the homepage displaying, for example, proteins known to influence the proliferation state (quiescence) or the self-renewal activity of a stem cell. Short summaries of signaling processes known to affect hematopoiesis, such as Cxcl12/Cxcr4 signaling, Ang1/Tie2 or Notch signaling are presented as well. A graph, showing an overview about niche-related data, including stem cell sub-populations, cellular components of the bone marrow niche, extrinsic or intrinsic factors and signaling processes necessary for establishing the niche is provided. A small outline thereof, displaying only the co-localization of HSCs to non-hematopoietic tissue is given as a selected topic as well.An objective of HSC-Explorer is to present complex experimental results relevant for murine hematopoiesis as an intuitive graphical output. The following examples illustrate the capability of HSC-Explorer to display complex data in a comprehensive and easy to follow manner.-KSL) or HSC (CD150+CD48-CD41-KSL). Cxcl12/Cxcr4 signaling not only plays a pivotal role in the regulation of HSC homing [discussed in -KSL) + stromal cell results in a down-regulation of Cxcl12 The chemokine Cxcl12 plays a key role during ontogeny of the hematopoietic system. A graphical visualization of the Cxcl12 interactions in HSC-Explorer clearly shows that Cxcl12 is mainly expressed in non-hematopoietic tissue like osteoblasts Fine control of gene expression by modulating the chromatin structure is a widely used mechanism in eukaryotes to regulate cell development. The multisubunit polycomb repressive complex PRC1 catalyzes histone modifications and has been implicated in the maintenance of hematopoietic stem cells. In conclusion, HSC-Explorer provides a publicly available integrative resource in the field of hematopoiesis. HSC-Explorer has been developed to present biological findings in form of a comprehensive and intuitive graphical network, which will enable scientists to explore hematopoiesis in a more systems-oriented approach.Figure S1Graphical presentation of  showing populations enriched for hematopoietic stem cells and their behavior in repopulation activity, self-renewal and quiescence.(TIF)Click here for additional data file.


Recent advances that have been made in our understanding of cancer biology and immunology show that infiltrated immune cells and cytokines in the tumor microenvironment may play different functions that appear tightly related to clinical outcomes. Strategies aimed at interfering with the cross-talk between microenvironment tumor cells and their cellular partners have been considered for the development of new immunotherapies. These novel therapies target different cell components of the tumor microenvironment and importantly, they may be coupled and boosted with classical treatments, such as radiotherapy. In this work, we try to summarize recent data on the microenvironment impact of radiation therapy, from pre-clinical research to the clinic, while taking into account that this new knowledge will probably translate into indication and objective of radiation therapy changes in the next future. In the last few years, the impact of the specific immune microenvironment in cancer has gained renewed interest, and is actually recognized as one of the major determinants of clinical evolution in a wide range of tumors. In this sense, cells like tumor-associated macrophages (TAM), regulatory T cells (Treg), and myeloid-derived-suppressor cells (MDSC), among others, are being proposed as new prognostic biomarkers that might be taken into account for diagnostic purposes, with the aim to complete classical information that is generally focused mainly on intrinsic characteristics of the tumor cell itself . Importantly, the effect of antineoplastic treatments on tumor cells may change the composition of microenvironment cells and their functional status, which introduces even more complexity (and importance) to this topic.Radiotherapy (RT) remains a cornerstone of oncological treatment for many types of tumors. Recently, it has been demonstrated that ionizing radiation may exert interesting effects over the tumor microenvironment, increasing the effectiveness of patients’ anti-tumor immune responses in the clinical setting even at distant sites . This faIn this review, we pursue to update and summarize the local and systemic immune effects of RT from the molecular level to the clinical scenario. Finally, some choices for immunotherapy combinatorial approaches based on RT and strategies for monitoring these sorts of response are suggested.The radiation-induced biological response exerts pro-inflammatory and immunomodulatory effects against tumor cells, and nowadays the modulation of the acquired immune response after irradiation is gaining increased interest .The radiobiological model considers that DNA damage after radiation induces different types of biological response, this has been classically described as the 5 Rs of radiobiology  . In thisIncreasing evidence has revealed that RT can change its recognition level, making the tumor vulnerable to the immune system. Furthermore, it has been described that the radiobiological response causes the activation of different T-cell lines, generating the “switch-on” of the adaptive immune response . These f(1)Calreticulin: Radiation causes the translocation of calreticulin (CRL) from the endoplastic reticulum to the cell surface, inducing the apoptotic cell antigen presentation to antigen presenting cells (APCs), in particular dendritic cells (DC), and stimulating specific anti-tumor T-cell responses , 10.(2)High-mobility group box 1 (HMGB1): Another immunogenic determinant of cell death is the pro-inflammatory factor HMGB1. HMGB1 is a nuclear protein that is released after necrotic cell death and from dying cells during late stage apoptosis. After cell death induced by RT, HMGB1 may be released to the stroma and act as a neo-antigen, which in turn acts as an immunogenic endogenous “danger signal,” initiating an inflammatory response through binding Toll-Like Receptor 4 (TLR4) on DC , 11.(3)+ T cells Upregulation of death receptors: RT may activate the extrinsic pathway of apoptosis, upregulating the expression of the FAS death receptor on tumor cells, which induces the activation of CTL via FAS ligand expressed on their surface .(5)Release of tumor antigens: Apoptotic and necrotic tumor cells after RT are a big source of tumor antigens, commonly tumor-associated antigens (TAAs), which can be efficiently taken up by DC that subsequently present them to CTL  (this to(6)Release of pro-inflammatory cytokines: Pro-inflammatory cytokines like IL-1β, TNF-α, or prostaglandin E2 are up-regulated in tumor cells after the cellular lesion post-irradiation, and they represent ultimately danger signals due to the tissue stress induced by RT .It seems that one of the main effects of RT to unleash an effective immune response is the induction of a strong “danger signal,” which is a concept postulated by Polly Matzinger in 1994, related to the stress signals generated by the damaged tissue . Dying tImmune-modulating effects of radiation are influenced by several factors. In this sense, the dose of radiation has been correlated with different responses. Low radiation doses seem to activate innate immune cells and fail to induce cell death. This situation develops a tumorigenic effect mediated by the cells of the immune microenvironment , 18. On In addition, the physical sequence of events after RT seems to be critical to mount a successful immune response. Pre-clinical models have shown that DC loaded with tumor antigens migrate toward the draining lymph nodes. This process leads to an activation of T cells that had not been previously exposed to specific tumor antigens, spreading the immune response against the tumor , 23. Act+ M1-macrophages in tumor microenvironment. These macrophages may release pro-inflammatory molecules such as TNF and facilitate cell infiltration by tumor-reactive effector T cells, improving local immune response against cancer  and Scopus (target). The abscopal effect provides new insights into the mechanisms of RT activity  showed the reduction of tumor growth outside the irradiation fields  agonist (CpG) during treatment with RT. Previously, authors detected the recognition and response of cytotoxic lymphocytes against B lymphoma cells in vitro . Furthern fields .+ after receiving palliative RT combined with immunotherapy (anti-CTLA-4/ipilimumab) has been recently described by Postow et al. in vitro an in vivo tumor regression (a)Cytotoxic T lymphocyte antigen-4 (CTLA-4): mAb anti-CTLA-4 ipilimumab has been approved by the FDA for the treatment of advanced melanoma, after demonstrating an increase in overall statistically significant survival in two randomized phase III trials in the first and second line setting , 62. Spe(b)+ T cells Programed death ligand 1 (PD-L1): Evidence in pre-clinical models suggests that a PD-L1 blockade is essential in some situations to fully uncover anti-tumor immunity that is induced by local RT in combination with co-stimulatory receptor engagement. Local RT combined with anti-OX40 and anti-PD-L1 has shown to mediate complete regression in orthotopic AT-3 mammary tumors. .(d)CD137: Agonist antibodies to CD137 and CD137-ligand co-stimulate T cells after TCR stimulation . Anti-CDSecond signal: co-stimulatory/co-inhibitory molecules: Immune synapse has been revealed as a promising therapeutic target and a set of monoclonal antibodies (mAb) targeting the molecules of this virtual space is under intensive clinical research .(a)CytoBesides CTLA-4, OX40, PD-1, and CD137, other co-stimulatory and co-inhibitory molecules like CD40 or glucocorticoid induced TNFR (GITR) represent other new emerging targets for immunotherapy .(3)Third signal: cytokines: To complete the process of a full effector T-cell response with clonal expansion and differentiation on memory T cells, a third signal provided by cytokines is needed . In thisAs previously described, synergism between RT and other immune therapies have a robust biological rationale that, related to the adaptive-cell response and generation of cytotoxic T lymphocytes, may be summarized in the following sequence of events.A tumor microenvironment is a challenging battlefield where many actors interact. From a clinical point of view, synergy between RT and immune therapies open a new breakthrough for clinical research. Specifically, targeting of immunostimulatory and inhibitory checkpoints with immunomodulatory mAbs can “awake” and promote the systemic effects induced by RT that ultimately may be maintained and boosted by cytokines.low) was ascertained after RT and these findings were timely correlated with the clinical response detected  the immune responses detected. Clinical development of anti-CTLA-4 mAb in the last few years has increased the interest in the search and validation of immune biomarkers. At this point, characterization of antigenic-specific immune responses has been performed for several cancer related antigens. Serological and T-cell responses to NY-ESO-1 and MAGEA3 have been detected and prove to be useful to monitor immune responses and clinical evolution in patients with melanoma treated with ipilimumab and with the combination of RT and ipilimumab , 58. Somdetected .Every clinical trial with the aim of studying the synergism between RT and immunotherapy in the future might introduce some of the biomarkers previously cited (or a combination of them) in order to detect accurately if the clinical outcomes eventually observed are related or not to the combination approach. This is a critical point to truly confirm the abscopal effect hypothesis, as noted in some case reports illustrated in Table The data available support the hypothesis of a mediated immune anti-tumor activity for RT. Our understanding of this effect at the molecular level has substantially increased in the last few years, and a couple of well-documented clinical cases have been reported recently, which serve as proof of principle of the abscopal effect in the clinical scenario. Therefore, combined strategies of radio-immunotherapy will eventually modulate immune response toward cancer cell destruction leading to meaningful clinical results. Nowadays, several clinical trials are ongoing exploring the immune consequences of RT, especially with immune checkpoints, and they will probably shed more light to this topic. Interestingly, validation of biomarkers like antibodies against NY-ESO-1 and MAGEA3 or measurement of TAM and MDSC is another very important task in order to facilitate the design of fine tune approaches related to these new immunotherapies and combinations in the coming future.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.


We longitudinally assessed level and status changes of anti-glycan antibodies over time in Saccharomyces cerevisiae (gASCA) antibodies by ELISA. All patients had at least two and up to eleven serum samples taken during the disease course.859 serum samples derived from a cohort of 253 inflammatory bowel disease (IBD) patients ) were tested for the presence of anti-laminarin (Anti-L), anti-chitin (Anti-C), anti-chitobioside (ACCA), anti-laminaribioside (ALCA), anti-mannobioside (AMCA) and anti-Median follow-up time for CD was 17.4 months (Interquartile range (IQR) 8.0, 31.6 months) and for UC 10.9 months . In a subgroup of CD subjects marked changes in the overall immune response (quartile sum score) and levels of individual markers were observed over time. The marker status (positive versus negative) remained widely stable. Neither clinical phenotype nor NOD2 genotype was associated with the observed fluctuations. In a longitudinal analysis neither changes in disease activity nor CD behavior led to alterations in the levels of the glycan markers. The ability of the panel to discriminate CD from UC or its association with CD phenotypes remained stable during follow-up. In the serum of UC patients neither significant level nor status changes were observed.While the levels of anti-glycan antibodies fluctuate in a subgroup of CD patients the antibody status is widely stable over time. The diagnosis of inflammatory bowel disease (IBD) and the differentiation between ulcerative colitis (UC) and Crohn's disease (CD) is currently based on the combination of clinical, laboratory, radiological, endoscopic and histopathologic criteria Saccharomyces cerevisiae (ASCA), anti-Pseudomonas-associated sequence I2 (anti-I2), outer membrane porin C (OmpC) of Escherichia coli antibodies and antibodies against the bacterial flagellin cBir1 (Anti-cBir1) have been extensively investigated for diagnosis and disease stratification Saccharomyces cerevisiae antibodies (gASCA), anti-mannobioside carbohydrate antibodies (AMCA), anti-laminaribioside carbohydrate antibodies (ALCA), anti-chitobioside carbohydrate antibodies (ACCA), anti-laminarin carbohydrate antibody (Anti-L) and anti-chitin carbohydrate antibody (Anti-C) has been reported in several independent cohorts to show a high discriminatory capacity for CD versus UC and association with and prediction of complicated CD behavior Serological markers linked to CD, such as anti-Despite a large number of cross sectional studies and a growing number of prospective studies in patient cohorts examining the utility of anti-glycan antibodies and other serum markers for diagnosis, disease stratification and prediction In contrast to this lack of information, knowledge in this field is crucial not only for interpreting existing studies but also for the design of future prospective trials that can ultimately lead to routine use of these biomarkers in clinical practice. The aim of this study was to fill these information gaps by I) defining the extent of level changes of anti-glycan antibodies in individual patients over time, II) interrogating potential associations of clinical factors and genotypes with marker fluctuations, III) performing a longitudinal analysis following marker levels correlated with distinct clinical events over time and IV) investigating, if the accuracy of the marker panel to differentiate UC from CD and association with complicated CD behavior changes over time.We performed a longitudinal cohort study among adult IBD patients. All IBD in- and outpatients seen at our tertiary referral center between 2000 and 2006 were considered for participation in the study. The diagnosis of CD and UC was made based on clinical, radiographic, endoscopic and histopathologic criteria according to Stange et al. Inclusion criterion for this study was the presence of more than one serum sample per individual IBD patient during the disease course. The samples were collected at arbitrary visits to our hospital. Clinical data including age at diagnosis, body mass index (BMI), gender, date of sample procurement, date and type of first complication and surgery, medication and disease location were obtained or updated, respectively, for each single visit and time point of sample procurement separately by the treating physician of the IBD unit. Collected data were transferred and stored in a secure coded anonymized database for analysis. In July 2007, all patient charts and the database were reviewed and updated for the data points mentioned above without knowledge of the antibody values. The treating IBD physician determined CD activity based on the criteria included in the Crohn's disease activity index (CDAI) at the time of the patient visit and patients were grouped in active (CDAI>150) and non-active disease (CDAI<150), without assigning a specific CDAI point value. This patient cohort represents a subgroup of the previously reported cross-sectional study Signed informed consent was obtained from all participants. The ethics committee of the University of Regensburg approved the study.After procurement of the blood samples the serum was separated by centrifugation and kept frozen at −80°C until use. All sera were analyzed at the same time for the levels of gASCA IgG, ALCA IgG, ACCA IgA, AMCA IgG, Anti-L IgA and Anti-C IgA in a blinded manner as previously described For the level of immune response of the serum-antibodies and for analysis of association with certain phenotypes quartile scores for each serologic marker were calculated, as previously described CRP was recorded as positive  or negative .Standardized TaqMan® SNP Genotyping Assays  were used to determine the genotype of the three main CD-associated NOD2 variants SNP 8, 12 and 13 . All processed data were written to and administered by a previously described database-driven laboratory information management system (LIMS) The IBD physician assessed the IBD patients for disease phenotype at arbitrary visits during the disease course. Patient demographics at time of first sample procurement can be seen in Descriptive statistics were computed for all variables. These include means, standard deviations and percentiles for continuous factors and frequencies for categorical variables. Lin's concordance correlation coefficients were estimated to assess correlation between first and maximal change samples. Associations of level and status changes with clinical phenotypes were evaluated using logistic regression models and adjustments for total follow-up time were done. To study changes in antibody levels, linear mixed models were built; time since diagnosis, use of immunosuppressants, active disease, occurrence of complication, and IBD-related surgery were included in the models. An unstructured covariance matrix was used to model within-subject correlation due to repeated measures. The sensitivity, specificity, positive and negative predictive values were estimated to assess whether the validity of each marker in diagnosing CD changed with repeated samples; subjects with at least 4 samples were included in this analysis. In addition, this same group of patients was used to study the association between antibody levels, positivity, number of positive markers and quartile group with Crohn's disease complications; Wilcoxon rank sum tests and Pearson's chi-square tests were used, as appropriate. A p<0.05 was considered statistically significant, unless otherwise stated. SAS version 9.2 software  and R version 2.10.1  were used for all analyses.20% of the sera were collected within six months, 30% within one year of diagnosis. The frequency of testing for the individual patients can be seen in As not all samples were taken at time of diagnosis and follow-up times varied we first tested if these two criteria have an influence on our analysis. The magnitude of level changes in our IBD cohort was not dependent on the time from diagnosis to first sample procurement. In addition, there was no significant time-level association between subjects with a short or long follow-up period, indicating that the observed level changes are independent of the observation time (data not shown). This suggests that our cohort can be used for an analysis of fluctuations over time, despite not all samples being taken at the time of IBD diagnosis or with a fixed follow-up time.Figure S1 showing the levels of the individual markers in CD patients). To describe the magnitude of level changes in CD patients we first divided the QSS into four equal sections, assigned the CD subjects to a respective section and identified patients with changes of their QSS section over time  from the per-patient mean) for all patients and samples. When taking into account the average Z-scores per patient, which includes all observed marker changes, we found while the absolute changes of the marker levels are more pronounced in CD versus UC the relative changes are comparable, which is true for the QSS and the levels of all single markers (Table S1).We next categorized the maximal extent of level changes in individual CD patients over time we identified the samples with the maximal change in QSS (increase or decrease) compared to the first available sample per individual CD patient during follow-up. In other words we identified the two samples per individual patient with the maximal changes in serologic immune response to the tested glycan epitopes. The comparison of the first sample and the sample with the maximal QSS changes during follow-up can be seen in maximal level changes for each individual antibody . This further corroborated the finding of marked fluctuations in the overall immune response in a subgroup of CD patients. In CD patients the maximal Z-score per patient was 0.91 (SD 0.38) (versus UC 0.76 (SD 0.36)) again indicating that the relative level changes are comparable between CD and UC.To classify the average and maximal fluctuations. The relative level changes are comparable in CD versus UC, with stronger absolute changes in CD.Taken together we show that on average across the whole CD cohort individual marker levels are rather stable, with however certain patients exhibiting stronger A different way to assess stability in serologic markers is to use a dichotomic categorization - positive or negative for a respective antibody or if investigating a panel the number of positive markers. We therefore tested how often the levels of the single markers cross the respective cut-off values over time, changing their status from positive to negative or vice versa. In CD subjects in a median of 3.3% of the visits  0, maximum (max.) 50) any of the marker status changed compared to the previous consultation .maximal changes in immune response during follow-up we next characterized the longitudinal CD patient samples for stability in marker status (positive versus negative for a respective antibody). We found a strikingly stable antibody status for both the total number of positive markers over time . Only between 0% and 8.7% of the UC patients changed the antibody status of an individual marker during follow-up.As partly stated above and in contrast to the serum of CD patients the absolute changes in the overall immune response and in the antibody status over time in UC patients were minute . We corrected for follow-up time and used a level of significance of p<0.01 to account for multiple testing. No phenotype or NOD2 genotype was associated with a higher magnitude of average fluctuations in the marker levels. The maximal difference in Z-score between the groups was 0.06 SD and cannot be considered clinically relevant.We first tested the maximal level fluctuations we divided our CD patients in two different groups based on the magnitude of maximal level changes in individual patients over time: “Non-level changers”  of the QSS in the initial sample) and “Any level changers” (increase or decrease >0.5 SD in QSS at any time during follow-up). These two groups were analyzed for association with certain clinical disease phenotypes and NOD2 genotypes (Table S4). To control for multiple testing we used a significance level of p<0.01 and the results were adjusted for follow-up time. We did not find any association between the magnitude of maximal level fluctuation in our cohort and any of the pheno- or NOD2 genotypes. We performed this analysis with considering only CD-patients with a minimal observation period of 12 months. Also here no association of pheno- or genotypes with fluctuations was found (data not shown).To identify factors associated with Table S5. We also assessed the groups for association with certain clinical disease phenotypes and NOD2 genotypes (Table S6). We accounted for multiple testing by using a significance level of p<0.01 and adjusted for follow-up time. No association of pheno- or genoptypes with fluctuations was found, when considering the whole cohort but also when separately analyzing only CD patients with a minimum follow-up of 12 months (data not shown).The antibody levels in the first sample did not predict the degree of fluctuation of the markers at later time points in the disease course as revealed by a logistic regression analysis (data not shown). In other words the same degree of maximal level changes occurred in subjects during follow-up that had high, middle or low levels in the first sample. We additionally separated our cohort into two groups according to changes in marker status: “Non-status changers” (patients that did not change the number of positive markers during follow-up) and “Status changers” . The distribution of subjects can be found in average or maximal changes in marker levels and status over time could be found.In summary, despite considering a broad range of clinical phenotypes and NOD2 genotypes no associations with per se, it is well likely that changes in CD behavior or treatment can lead to changes in the levels of the antibody markers over time. We therefore longitudinally analyzed our CD cohort for potential level changes inflicted by clinical situations, namely the first occurrence of complicated CD-behavior, the first occurrence of CD-related surgery, the use of immunosuppressive medication or the onset of active disease. For this purpose repeated measures analysis was done using an unstructured covariance matrix to model within-subject correlation. The occurrence of first time complicated CD-behavior, first time CD-related surgery, active disease or the start of immunosuppressive medication did not influence the overall immune response or the levels of the individual markers. Pre-event antibody levels were not significantly different to post-event levels taking into consideration the above parameters (Table S7 for the single markers). When looking at the use of corticosteroids separately we found a trend towards a decrease in the QSS (data not shown). Interestingly, overall the QSS mildly increased over time from diagnosis (0.13 points each year), which was due to a level increase in the IgA marker ACCA with a trend to increased levels for Anti-L and Anti-C . The expression of the glycan markers, the clinical phenotype and the overall immune response in this subpopulation was comparable to the total CD cohort (n = 207), indicating that no selection bias was present.versus UC (nd sample), and 93.3% (3rd and 4th sample) during follow-up. 85% of the CD patients already underwent surgery once at time of first sample, which increased during follow-up to 90% for the other three time points. Also here an overall stable association pattern between the markers and clinical phenotypes was found. Independent of the time of sample procurement over the follow-up period the overall immune response, as depicted by the sum of quartiles or the number of positive markers remained to be associated with ileal involvement, complicated CD behavior or CD-related surgery .We assessed changes in the ability of the markers to differentiate CD ersus UC Table 4.There is a growing interest in the use of anti-glycan antibodies as markers for differentiation and stratification of CD. Previous reports, including ours average or maximal changes in marker levels. In a longitudinal analysis with individual patients over time neither changes in disease activity, CD behavior or surgery, nor the intake of immunosuppressive medication led to changes in the QSS. The ability of the panel to discriminate CD versus UC or its association with CD location, behavior or surgery remained stable during the follow-up time. In UC neither significant absolute level nor status changes were observed.We detected marked changes in the overall immune response (QSS) and levels of individual markers in a subgroup of CD subjects over time. The marker status (positive versus negative) remained widely stable. None of our tested clinical phenotypes or NOD2 genotypes was associated with stronger cross-sectional single point studies are controversial: In 32% of apparently healthy recruits of the Israeli Defence Forces, who were later diagnosed as having CD, levels of ASCA increased closer to the date of diagnosis Incomplete and limited information is available on fluctuations in level and status of serum antibodies directed against microbial components and associated with CD over time. Most data derived from longitudinal studies in individual patients over time is even scarcer and meaningful conclusions are difficult to make due to limited follow-up times or low patient numbers. Dotan et al. followed the serum levels of the glycan-markers anti-alpha-Rha, anti-alpha-GlcNAc and anti-cellotriose antibodies in seven healthy volunteers during the course of 13 weeks and found widely stable levels over time The information derived from versus placebo for prophylaxis of postoperative relapse (n = 38) remained stable over time Disease activity or relapse does not appear to influence marker levels in longitudinal studies Our study describes the dynamic change of anti-glycan antibodies in the to date largest cohort of CD patients with multiple samples collected during the disease course and scrutinizes detailed clinical information for each time point of sample procurement. Comparable studies but of smaller size have so far only been reported for ASCA We established that during the disease course significant fluctuations in marker levels occur in a subgroup of CD patients. The marker status, however, remained strikingly stable. Considering all markers a change in status occurred in only 3.3% of the visits per antibody. In an attempt to identify specific clinical phenotypes or genotypes that are associated with stronger level or status changes we included a wide range of phenotypes into our statistical analysis. Interestingly none of the clinical phenotypes was associated with level or status changes in antibodies over time. The immune response tended to decrease with intake of corticosteroids. This is in concordance with a previous report relative changes of the marker levels  with caution for disease stratification, due to strong fluctuations in a subgroup of CD subjects.Figure S1Changes in the level of single markers over time. Profile plots for changes in the levels of single markers in individual Crohn's disease (CD) patients over time. The broken line represents the cut-off value for each individual marker. The red lines indicate patients starting above the cut-off value and the green lines indicate subjects starting below the cut-off values. Depicted is a random set of 50 subjects per graph. gASCA: anti-Saccharomyces cerevisiae antibodies, ACCA: anti-chitobioside carbohydrate IgA antibodies, ALCA: anti-laminaribioside carbohydrate IgG antibodies, AMCA: anti-mannobioside carbohydrate IgG antibodies, Anti-L: anti-laminarin carbohydrate antibody, Anti-C: anti-chitin carbohydrate antibody.(TIF)Click here for additional data file.Figure S2Maximal changes in levels of single markers in Crohn's disease. Scatter plot comparing the level of the first sample and the sample with the maximal changes in quartile sum score during follow-up for each individual marker in Crohn's disease (CD) patients. One dot represents one patient. gASCA: anti-Saccharomyces cerevisiae antibodies, ACCA: anti-chitobioside carbohydrate IgA antibodies, ALCA: anti-laminaribioside carbohydrate IgG antibodies, AMCA: anti-mannobioside carbohydrate IgG antibodies, Anti-L: anti-laminarin carbohydrate antibody, Anti-C: anti-chitin carbohydrate antibody.(TIF)Click here for additional data file.Figure S3Maximal changes in levels of single markers in Ulcerative colitis. Scatter plot comparing the level of the first sample and the sample with the maximal changes in quartile sum score during follow-up for each individual marker in Ulcerative colitis (UC) subjects. One dot represents one patient. gASCA: anti-Saccharomyces cerevisiae antibodies, ACCA: anti-chitobioside carbohydrate IgA antibodies, ALCA: anti-laminaribioside carbohydrate IgG antibodies, AMCA: anti-mannobioside carbohydrate IgG antibodies, Anti-L: anti-laminarin carbohydrate antibody, Anti-C: anti-chitin carbohydrate antibody.(TIF)Click here for additional data file.Table S1Average of absolute standard deviations around the mean (Z-score).(TIFF)Click here for additional data file.Table S2Average of absolute standard deviations around the mean (Z-score) and association with disease pheno- and NOD2 genotypes.(TIFF)Click here for additional data file.Table S3Correlation coefficients of the Z-score with disease parameters.(TIFF)Click here for additional data file.Table S4Association of clinical phenotypes and genotypes with maximal level changes.(TIFF)Click here for additional data file.Table S5Number and distribution of CD subjects with antibody status changes.(TIFF)Click here for additional data file.Table S6Assosiation of clinical phenotypes and genotypes with status changes.(TIFF)Click here for additional data file.Table S7Longitudinal analysis of level changes inflicted by clinical situations.(TIFF)Click here for additional data file.Table S8Validity of markers for association with disease phenotypes over time in individual patients.(TIFF)Click here for additional data file.


Recurrent breast cancer occurring after the initial treatment is associated with poor outcome. A bimodal relapse pattern after surgery for primary tumor has been described with peaks of early and late recurrence occurring at about 2 and 5 years, respectively. Although several clinical and pathological features have been used to discriminate between low- and high-risk patients, the identification of molecular biomarkers with prognostic value remains an unmet need in the current management of breast cancer. Using microarray-based technology, we have performed a microRNA expression analysis in 71 primary breast tumors from patients that either remained disease-free at 5 years post-surgery (group A) or developed early (group B) or late (group C) recurrence. Unsupervised hierarchical clustering of microRNA expression data segregated tumors in two groups, mainly corresponding to patients with early recurrence and those with no recurrence. Microarray data analysis and RT-qPCR validation led to the identification of a set of 5 microRNAs (the 5-miRNA signature) differentially expressed between these two groups: miR-149, miR-10a, miR-20b, miR-30a-3p and miR-342-5p. All five microRNAs were down-regulated in tumors from patients with early recurrence. We show here that the 5-miRNA signature defines a high-risk group of patients with shorter relapse-free survival and has predictive value to discriminate non-relapsing versus early-relapsing patients . Network analysis based on miRNA-target interactions curated by public databases suggests that down-regulation of the 5-miRNA signature in the subset of early-relapsing tumors would result in an overall increased proliferative and angiogenic capacity. In summary, we have identified a set of recurrence-related microRNAs with potential prognostic value to identify patients who will likely develop metastasis early after primary breast surgery. Breast cancer comprises a group of heterogeneous diseases that can be classified based on both clinical and molecular features The risk of relapse after tumor resection is not constant over time. A detailed examination of large series of long-term follow-up studies over the last two decades reveals a bimodal hazard function with two peaks of early and late recurrence occurring at 1.5 and 5 years, respectively, followed by a nearly flat plateau in which the risk of relapse tends to zero The identification of distinctive molecular portraits by microarray-based gene expression profiling has led to a breast tumor classification into five different subtypes: luminal A, luminal B, HER2 overexpressing (HER2+), basal-like and normal-like www.mirbase.org) MicroRNAs (miRNAs) are small, single-stranded RNAs with an important role in the regulation of gene expression As happens to mRNA expression, the transcriptional profile of miRNAs can vary among different tissues and stages of development. Alterations of miRNA patterns and sequences are common in several diseases, including cancer In addition, miRNA expression profiles can provide molecular information of clinical relevance in cancer The 75 patients included in this study underwent primary breast cancer surgery at the Hospital Universitario Virgen de la Victoria , between years 1998 and 2005. All patients provided written informed consent for inclusion in the study, which was approved by the hospital's Institutional Review Board (IRB). Patients were uniformly treated and followed according to the protocols established by the Clinical Oncology Department, based on scientific evidence and international recommendations. All clinical investigation were conducted according to the principles expressed in the Declaration of Helsinki.None of the patients received neoadjuvant therapy. Clinicopathological and follow-up information were obtained for each patient by chart review.Archived formalin-fixed and paraffin-embedded (FFPE) tumors (n = 75) were retrieved, a pathologist selected the most representative areas and tissue microarrays were constructed with triplicate cores (0.6 mm in diameter). Tumors were classified according to the intrinsic subtypes by immunohistochemical staining. Specific antibodies against estrogen receptor , progesterone receptor , Ki-67 (clone SP6) epidermal growth factor receptor 1 , vascular endothelial growth factor  and cytokeratin 5/6  were all purchased from Master Diagnostica (Spain). HER2 immunostaining was performed with the HercepTest™ . Interpretation of immunohistochemical data was performed as previously described FFPE tumor areas enriched with >90% breast cancer cells were selected and cells were manually microdissected from 3x10 μm sections. Total RNA was extracted using the RecoverAll Total Nucleic Acid Isolation kit . RNA was converted to cDNA and hybridized to Affymetrix miRNA Chip array 2.0 . Chip hybridization and scanning were performed at the Functional Genomics Core facility  following the recommendations of the manufacturer.limma, a moderated t-statistic based on an empirical Bayes approach RankProd, a simple non-parametric statistical method based on ranks of fold changes vs A (B/A), BC vs A (BC/A), and B vs AC (B/AC). Group C alone could not be compared with A or B since microarray data from group C did not provide statistically significant differentially expressed miRNAs. The MIAME compliant microarray data has been deposited in the ArrayExpress public repository , with accession number E-MTAB-1989.All statistical analyses were performed using the open-source R programming environment (v2.14.2) together with the open-source Bioconductor v2.10 libraries t method. The small RNAs RNU6b and miR-16 were used as reference for normalization.Ten nanograms of total RNA from each tumor sample were used to obtain cDNA by reverse transcription with specific miRNA primers and reagents from the TaqMan MicroRNA Reverse Transcription Kit . PCR products were amplified from cDNA samples with the TaqMan MicroRNA Assays using the TaqMan Universal PCR Master Mix. All the assays were performed in triplicate according to the manufacturer's instructions. Relative miRNA expression was calculated using the ΔΔCsurvival package in R was used to compute survival estimates and perform multivariate regression analysis The Three are the steps involved in the estimation of expression profiles to predict the outcome of future observations: model selection, prediction assessment and feature selection http://CRAN.R-project.org/package=Daim) packages were used in R to conduct these analyses. Feature selection refers to decide which miRNAs to include in the prediction, and it is a crucial step in developing a class predictor. Including too many features could reduce the model accuracy and may lead to a data overfitting A generalization of the 632+ bootstrap estimation of the misclassification error to estimate the TPR, FPR and ROC curves http://mirtarbase.mbc.nctu.edu.tw/) http://mirecords.biolead.org/) Validated targets for each miRNA were obtained from mirTarBase ; and group C, patients who developed late recurrence (50–60 months post-surgery). Microarrays were first analyzed for quality control and normalized for miRNA expression (see methods). Four samples with a poor signal quality were excluded from the study, leaving a cohort of 71 breast tumors for further analysis. Unsupervised hierarchical clustering of the microarray data showed that the transcription profiles of miRNAs discriminate the prognosis groups in two distinct clusters . ClusterIn order to select the statistically significant and differentially expressed miRNAs from Regarding the intrinsinc subtypes, we found lower levels of miR-149, miR-30a-3p and miR-342-5p in ER- tumors  with early relapse (≤24 months) to identify the best prognostic factors. The best model according to the AIC criterion included the tumor size and expression of the 5-miRNA signature (data not shown). Only the 5-miRNA signature  resulted statistically significant in the Cox model for the high risk group . The 5-miRNA expression data were also used to develop a predictor model through bootstrapping over a Naive Bayes classifier  were generated and visualized in Cytoscape. Each regulatory interaction in the network consist of two nodes, a regulatory component (miRNA) and a target biomolecule (mRNA) connected through one directed edge. in vivo by the 5-miRNA signature in early-relapsing tumors.To extend our set of five miRNAs with regulatory information, we next took advantage of the existing public databases curating predicted and validated miRNA-target interactions (MTIs). In particular, validated targets were obtained from the miTarBase and miRecords repositories see . First, To gain further insight into the molecular basis of the 5-miRNA signature prognostic value, we investigated the biological pathways associated with the 30 experimentally verified targets from In an effort to validate such hypothesis, we collected retrospective immunohistochemical data for expression of angiogenesis (VEGF) and proliferation (Ki67) markers in the set of primary tumors , when avPredicting early relapse represents a major challenge in the clinical practice, because an early failure very often corresponds to a more aggressive disease with fewer therapeutic options and poorer outcome. Indeed, up to half of all the relapses reside within the early peak of recurrence described by Demicheli and colleagues The model of bimodal recurrence relies on the concept of tumor dormancy and metastatic homeostasis being perturbed by tumor removal Several miRNAs with prognostic value for metastatic breast cancer have been proposed Our study neither support nor refute an iatrogenic effect derived from surgery but rather demonstrates that breast tumors with different risk or relapse can be differentiated at the miRNA expression level. However, if we consider such an iatrogenic effect, our data could be reconciled with a bimodal recurrence distribution as follows: the increased proliferation potential of the tumors in group B (early recurrence) could lead to the formation of micrometastasis as opposed to the tumors with lower proliferative capacity (group A and some in group C), which would seed mainly as single-cell micrometastasis. Both metastatic foci would remain avascular and therefore dormant, until a surgery-driven angiogenic switch restarts tumor progression. Then, micrometastasis would grow at different rates based on their distinct proliferative potential . Differences in the angiogenic and invasive capacities of the tumors could also contribute to the temporal distribution of recurrence but they still need to be firmly stablished in future studies.MiRNA deregulation is involved in breast cancer and there is a growing interest in the identification of miRNA signatures with biomarker potential in the clinical setting. Here, we have provided evidence that miRNA profiling can discriminate patients with different risk of relapse. In particular, we have identified a 5-miRNA signature  with prognostic value  that is downregulated in primary breast tumors from patients who develop early recurrence. In addition, we have identified a set of 30 mRNAs predicted to be up-regulated by the 5-miRNA signature in early-relapsing tumors. Notably, the set included mRNAs coding for proteins mainly involved in angiogenesis and proliferation . We have demonstrated that early-relapsing tumors have a significant higher proliferative potential, as measured by Ki67 immunostaining. Further efforts are needed to address the role of these biomarkers in the process of recurrence, but they could contribute to develop novel treatment strategies and to better understand the specific functions of miRNAs in cancer progression and metastasis.Although our results may require further external validation in a larger cohort, we propose our set of 5 miRNAs as an independent prognostic-associated signature for early recurrence in breast cancer.Figure S1Overview of predicted biological functions affected by expression of the 5-miRNA signature in early-metastasizing tumors. The set of 30 predicted targets of the 5-miRNA signature (ignature  was inte(EPS)Click here for additional data file.Table S1Most significant deregulated miRNAs in breast tumors of different intrinsic subtypes. Only those miRNAs with a FC>2 and p<0.05 were included in the list.(DOC)Click here for additional data file.