Burkholderia cenocepacia, the second messenger cyclic diguanosine monophosphate (c-di-GMP) has previously been shown to positively regulate biofilm formation and the expression of cellulose and type-I fimbriae genes through binding to the transcriptional regulator Bcam1349. Here, we provide evidence that cellulose and type-I fimbriae are not involved in B. cenocepacia biofilm formation in flow chambers, and we identify a novel Bcam1349/c-di-GMP-regulated exopolysaccharide gene cluster which is essential for B. cenocepacia biofilm formation. Overproduction of Bcam1349 in trans promotes wrinkly colony morphology, pellicle, and biofilm formation in B. cenocepacia. A screen for transposon mutants unable to respond to the overproduction of Bcam1349 led to the identification of a 12-gene cluster, Bcam1330–Bcam1341, the products of which appear to be involved in the production of a putative biofilm matrix exopolysaccharide and to be essential for flow-chamber biofilm formation. We demonstrate that Bcam1349 binds to the promoter region of genes in the Bcam1330–Bcam1341 cluster and that this binding is enhanced by the presence of c-di-GMP. Furthermore, we demonstrate that overproduction of both c-di-GMP and Bcam1349 leads to increased transcription of these genes, indicating that c-di-GMP and Bcam1349 functions together in regulating exopolysaccharide production from the Bcam1330–Bcam1341 gene cluster. Our results suggest that the product encoded by the Bcam1330–Bcam1341 gene cluster is a major exopolysaccharide that provides structural stability to the biofilms formed by B. cenocepacia, and that its production is regulated by c-di-GMP through binding to and promotion of the activity of the transcriptional regulator Bcam1349.In  Burkholderia cepacia complex (Bcc) is a group of closely related Gram-negative bacteria containing at least 17 different species. Bcc bacteria have recently emerged as highly problematic opportunistic pathogens causing chronic infections in immunocompromised individuals and particularly in patients with cystic fibrosis (CF)  plays a central role in the transition between free-living motile and biofilm life styles in many bacteria, and in particular functions as a positive regulator in the synthesis of various biofilm matrix components, including exopolysaccharides  agar. Sixty micrograms tetracycline mL−1 (liquid medium), 100 μg tetracycline mL−1 (solid medium), 25 μg gentamicin-sulfate mL−1, and 50 μg kanamycin mL−1 were used for the B. cenocepacia strains where appropriate. Twenty-five micrograms tetracycline mL−1, 25 μg of gentamicin mL−1, 50 μg kanamycin mL−1, and 6 μg chloramphenicol mL−1 were used for the E. coli strains where appropriate.The bacterial strains and plasmids used in this study are listed in E. coli SM 10-λpir/pBT20) and the recipient strain (B. cenocepacia carrying a plasmid-borne bcam1349) were harvested from overnight culture on LB-agar plates containing appropriate antibiotics, and were resuspended in 1 mL 0.9% NaCl. Concentrations of the cells in the suspensions were adjusted to an OD600 of 40 and 20 for the donor and the recipient strain, respectively. Fifty microliters of each strain were mixed, and spot inoculated on prewarmed LB-agar plates. After 45 min of incubation at 37°C, the cells were scraped off and were resuspended in 1 mL of 0.9% NaCl. The cell suspension was plated on AB-agar medium (without NaCl) supplemented with 10 mmol/L Na-citrate as carbon source and 25 μg gentamicin sulfate mL−1. The plates were incubated at 37°C for 4 days. Thirteen independent rounds of transposon mutagenesis were performed as described above, and approximately 6000 colonies were screened for smooth colony morphology. To determine the location of the transposon insertions, arbitrary primed PCR was performed with genomic DNA isolated from the mutants , the resulting gene replacement fragments were first transferred into pDONR221 generating entry plasmids, and subsequently transferred into pEX18Ap-pheS-GW generating the gene replacement plasmids pEX18AP-pheS-GW-bcal1389 and pEX18AP-pheS-GW-bcal677.Gene replacement fragments containing a gentamicin (Gm) resistance cassette were generated by PCR overlap extension as described by Choi and Schweizer . PrimerspheS-GW used in this study, a 1744-bp GW cassette was excised from pEX18ApGW  p-chlorophenylalanine  via the counter-selectable pheS marker on the gene replacement plasmid  without NaCl, and subsequent incubation at 37°C for 2 days.Colony morphology of the Overnight cultures were 100 times diluted in 5 mL of LB medium, supplemented with appropriate antibiotics for maintenance of the plasmids, and were subsequently placed in culture tubes. Pellicles were assayed by visual inspection of the air–liquid interface of the static liquid culture after 2 days at room temperature. Complete coverage of the surface with an opaque layer of cells and matrix material was considered pellicle formation.B. cenocepacia strains with gfp, the strain containing the delivery plasmid pmini-Tn7-kan-gfp, the strains containing the helper plasmids pUX-BF13 and pRK600, and the recipient strain were grown in LB medium with appropriate antibiotics at 37°C overnight. Five hundred microlitres of the overnight-grown strains containing the delivery and helper plasmids, and 250 μL of the recipient strain were mixed, washed twice in 1 mL LB medium, and resuspended in 100 μL LB. The cell suspension was spot inoculated on prewarmed LB-agar plates and incubated 37°C overnight. The next day, the cells were scraped from the plates and resuspended in 1 mL 0.9% NaCl. Serial dilutions of the cell suspension were plated on selective AB-agar medium supplemented with 10 mmol/L Na-citrate as carbon source and 50 μg kanamycin mL−1, and incubated at 37°C for colony growth. The transformants were checked whether they were tagged with gfp by using an epifluorescence microscope equipped with filters to detect gfp-fluorescence.In order to tag the −1) and glucose (1 mmol/L) as carbon source. The flow-chamber biofilm system was assembled and prepared as described previously . Images were further processed for display by using Photoshop software . Quantification of biofilm biomass was performed by COMSTAT software . Microscopic observation and image acquisition of biofilms were performed with a Zeiss LSM 710 CLSM  equipped with an argon laser and detectors and filter sets for monitoring g for 10 min, and the aqueous phase was transferred to a new microcentrifuge tube. The LPS extract was separated on a 15% SDS-PAGE gel, and visualized using the Bio-Rad silver stain kit according to manufacturer's instructions .Lipopolysaccharide was extracted from bacteria as described previously , and it was DNase treated using the Turbo DNA-free kit  according to manufacturers' instructions. cDNA synthesis and quantitative RT-PCR analysis were carried out using the Qscript 1-Step Sybr green qRT-PCR kit  according to manufacturer's instructions. As a control, quantitative RT-PCR was similarly applied to analyze the expression of the gyrB gene. The relative expression levels of the target genes were calculated using the threshold cycle (2−ΔΔCt) method (Livak and Schmittgen The effect of increased cellular levels of Bcam1349 and c-di-GMP on expression of the genes of the Bcam1330–Bcam1341 cluster was monitored by quantitative RT-PCR. RNA was isolated from biofilm grown bcam1330 and bcam1331 genes according to manufacturer's instructions using the Abridged Anchor Primer (AAP) and Abridged Universal Amplification Primer (AUAP) in combination with the gene-specific primers. Total RNA was isolated from B. cenocepacia cultures grown until midexponential phase using the RNeasy Protect Bacteria Mini Kit (Qiagen).The Invitrogen 5′ RACE System (version 2.0) was used to determine the transcription start site (TSS) of the The DNA probes containing the putative promoter regions (500 basepairs) of the genes of interest were generated by PCR using 5′-end DIG synthetic oligonucleotides as primers. Isolation of the Bcam1349 protein, the binding conditions, and the detection procedures were as described previously  that the transcript levels of all the cellulose biosynthesis genes were significantly decreased in the bcam1349 mutant compared with the wild type. Furthermore, we showed that the Bcam1349 protein binds to bcs  promoter DNA in a manner that is promoted by c-di-GMP. Based on these results, we hypothesized that a decrease in cellulose and fimbriae expression in the bcam1349 mutant may be an underlying cause of the biofilm defect displayed by this mutant.In a previous study, we provided evidence that the transcriptional regulator Bcam1349 is a c-di-GMP responsive protein that regulates biofilm formation in bcal1389 and bcal1677 mutants by replacing the corresponding open reading frames (ORFs) with a gentamicin resistance cassette. We chose to delete these genes, because bcal1389 and bcal1677 are the first genes of the putative cellulose  and fimbriae  biosynthesis gene clusters, respectively. Analysis of the two gene clusters by using the DOOR (Database of prokaryotic operons) operon prediction tool . Treatment with medium containing 0.03% SDS for 1 h caused almost the same reduction of the biomass in the wild-type and mutant biofilms, indicating that the mutant biofilms were nearly as resistant to SDS treatment as the wild-type biofilm, and that cellulose and fimbriae do not play a major role in biofilm stability under the conditions tested  predicted to be involved in exopolysaccharide biosynthesis .Because expression of exopolysaccharide genes in some cases affects regulation of lipopolysaccharide (LPS) production or results in gene products that constitute a portion of LPS biosynthesis pathways, the defect in biofilm formation by the E. coli, P. aeruginosa, and V. cholerae , and if the product encoded by the Bcam1330–Bcam1341 gene cluster is the major contributor to the wrinkly colony morphology observed either when Bcam1349 or c-di-GMP is overproduced, then an increase in the level of c-di-GMP should not lead to the formation of wrinkled colony morphology in the Q strain , supportbcam1330, bcam1331, and bcam1332 mutants.As multiple different transposon insertions occurred in the Bcam1330–Bcam1341 gene cluster and they all resulted in similar phenotypes, we excluded the possibility that the observed phenotypes were due to secondary mutations. Thus, we did not carry out complementation analysis for the bcam1330, bcam1331, and bcam1332 in both the Bcam1349-overproducing wild type/pBcam1349 strain and the c-di-GMP-overproducing wild type/pYedQ strain. We found that overproduction of Bcam1349 from the pBcam1349 plasmid in the wild type resulted in a 30-fold increased level of the bcam1330 transcript, and an 11-fold increased level of the bcam1331 transcript compared with the vector control strain. The level of the bcam1332 transcript did not change significantly in the Bcam1349-overproducing strain compared with the vector control strain  of the  the ORF . Further1331 ORF .bcs promoter DNA to induce transcription  was performed using Digoxigenin (DIG) labeled upstream regions of the bcam1330 and bcam1331 genes. The probes were combined with 20 μmol/L of C-terminally GST-tagged Bcam1349 protein and assessed for binding using EMSA. As a positive and a negative control, EMSA was performed in parallel using the bcs promoter region that was previously shown to bind Bcam1349 and the promoter region of the otsA (bcam1573) gene that does not bind to Bcam 1349 as probes, respectively (data not shown). As shown in bcam1330 and bcam1331 genes and the Bcam1349 protein formed a stable DNA-protein complex that migrated at a slower rate than unbound probes, whereas no band shift was observed with the negative control lacking the protein. In contrast, the Bcam1349 protein could not form a DNA-protein complex with the otsA promoter, indicating specificity in DNA binding (data not shown).We have previously shown that Bcam1349 binds to Bcam1349  suggestsbcam1330 and bcam1331 genes is increased in the presence of elevated intracellular levels of c-di-GMP, we surmised that the addition of c-di-GMP to the DNA-protein reaction mixtures would influence the binding of Bcam1349 to bcam1330 and bcam1331 promoter DNA. In accordance, the addition of c-di-GMP at concentrations in the μmol/L range resulted in an increase in the ability of Bcam1349 to bind promoter DNA as measured by retarded migration of the DNA-protein complex through the gel  and fimbriae  mutants suggested that cellulose and fimbriae are not essential for B. cenocepacia biofilm formation and stability under the conditions we tested. The mutants formed flow-cell biofilms that were quite similar to the wild type flow-cell biofilms, and the mutant biofilms were nearly as resistant to SDS treatment as the wild type biofilm.We recently provided evidence that the c-di-GMP signaling system is involved in the regulation of biofilm formation in Bcam1349 mutant (Fazli et al. B. cenocepacia biofilm formation and stability under the conditions tested. This also suggested that Bcam1349 regulates the expression of other genes encoding products that serve as the major components of the biofilm matrix and are possibly more important for mature biofilm formation and stability than cellulose and fimbriae. In an effort to identify the target genes affected by Bcam1349, we screened for genes required for the formation of wrinkled colonies in the pBcam1349-containing wild type strain and identified a putative exopolysaccharide gene cluster, Bcam1330–Bcam1341, required for wrinkled colony morphology, pellicle, and biofilm formation in B. cenocepacia.Overproduction of the Bcam1349 protein in the wild type resulted in formation of wrinkled colonies on solid medium and thick pellicles at the air–liquid interface in static cultures, which are indicative of production of adhesive biofilm matrix components such as exopolysaccharides, adhesive proteins, and aggregative fimbriae (Rainey and Travisano bcam1340, for sugar precursor production. It is predicted to encode a mannose-1-phosphate guanylyltransferase catalyzing the formation of GDP-mannose precursor. Currently, we do not know the sugar composition of the putative exopolysaccharide encoded by the Bcam1330–Bcam1341 gene cluster; however, if other nucleotide sugar precursors than GDP-mannose are required, then the synthesis of these nucleotide sugar precursors is probably derived from central carbon metabolism and uses enzymes from other polysaccharide synthesis pathways, similar to the situation in P. aeruginosa where AlgC is required for precursor synthesis for alginate, Psl, rhamnolipid, and LPS biosynthesis (Goldberg et al. bcam1334, encodes a predicted hydrolase. This protein may have a similar function in exopolysaccharide biosynthesis as AlgL, PelA, and PslG from the alginate, Pel, and Psl polysaccharide biosynthesis machinery of P. aeruginosa, respectively, which are involved in quality control of the secreted polysaccharides (Franklin et al. E. coli outer membrane auxiliary lipoprotein Wza, which is responsible for the final stage of capsule polysaccharide export (Cuthbertson et al. The biosynthesis of exopolysaccharides can be divided into distinct steps, including formation of nucleotide sugar precursors, polymerization and transport across the inner membrane, and modification and transport from the periplasm to the extracellular medium (Franklin et al. Bcc bacteria have been shown to have the capacity to produce large amounts of the exopolysaccharide cepacian giving the bacteria a mucoid phenotype, and it is suggested that cepacian facilitates persistence of Bcc bacteria during lung infection in CF patients. Cunha et al. (Bcc strains have also been isolated from CF patients (Zlosnik et al. Bcc bacteria are able to form cepacian. In fact, the commonly studied clinical isolates B. cenocepacia J2315 and K56-2 produce little or no cepacian, yet they are still able to form biofilms in vitro (Saldias et al. Burkholderia cenocepacia J2315 is unable to produce cepacian due to a frameshift mutation in the glycosyltransferase encoding bceB gene of the cepacian biosynthesis gene cluster; however, its genome contains several other gene clusters associated with exopolysaccharide biosynthesis (Holden et al. P. aeruginosa produces at least three different exopolysaccharides that are involved at different stages of biofilm development (Ryder et al. P. aeruginosa. However, nonmucoid P. aeruginosa strains, which are the first to colonize CF patients, were found not to produce alginate as the major exopolysaccharide, and recent studies have demonstrated that alginate is in fact not required for initiation of biofilm formation by these nonmucoid strains (Wozniak et al. P. aeruginosa strains were found to produce the polysaccharides Pel and Psl, which have differing roles with respect to adherence and biofilm formation depending on the strain. Studies demonstrated that the Pel polysaccharide is not essential for bacterial adherence to surfaces but involved in further maturation of biofilms, while the Psl polysaccharide is involved in bacterial adherence to surfaces and in the maintenance of already established biofilms (Friedman and Kolter B. cenocepacia, and accordingly, the presence of multiple gene clusters for exopolysaccharide biosynthesis suggests that the bacterium may require and produce different polysaccharides at different stages of biofilm formation, probably as a response to diverse environmental stimuli.Most a et al.  demonstrB. cenocepacia both resulted in the same phenotypic alterations such as wrinkled colony morphology on solid medium, thick pellicles in static liquid cultures, and increased biofilm formation in flow-cells; and mutations in multiple genes of the Bcam1330–Bcam1341 exopolysaccharide biosynthesis gene cluster reverted these phenotypes. The Bcam1349 protein was shown to bind to the promoter region of the bcam1330 and bcam1331 genes, and this binding was enhanced in the presence of c-di-GMP. Furthermore, overproduction of c-di-GMP or Bcam1349 resulted in elevated transcript levels of the bcam1330 and bcam1331 genes. We previously demonstrated that Bcam1349 plays a role downstream in c-di-GMP signaling by acting as a c-di-GMP responsive transcriptional regulator (Fazli et al. bcam1330 and bcam1331 genes in the Bcam1330–Bcam1341 gene cluster and thereby activates exopolysaccharide production and promotes biofilm formation by the bacterium.Overproduction of c-di-GMP and Bcam1349 in Bcc bacteria are not normally carried as commensal organisms, suggesting that the infection is either acquired in the hospital setting or from the environment (Hutchinson et al. Bcc bacteria display the mucoid phenotype, whereas the original environmental strains usually are nonmucoid. Other exopolysaccharides than cepacian may be more important for survival of the bacteria in the environment and may be required during initial colonization of the CF lung prior to switch to a mucoid phenotype. In addition, some Bcc strains, for example, B. cenocepacia J2315, are nonmucoid and do not produce cepacian, yet they survive in the CF lung despite aggressive antimicrobial therapy and host immune response, indicating that they may use other exopolysaccharides than cepacian as components of the biofilm matrix during biofilm formation in vivo. Our results in this study suggest that the product encoded by the Bcam1330–Bcam1341 cluster is a major exopolysaccharide that provides structural stability to biofilms formed by B. cenocepacia.In contrast to other opportunistic pathogens,


Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests. a priori definition of any parameter — all parameters are ‘learned’ from the data. This and other methods tested were implemented in a freely available software package for future testing and application to other ChIP-seq datasets.Transcription factors (TFs) are the main regulators of gene transcription. Thus, knowing the genes that are targeted by a specific TF is of utmost importance for understanding developmental processes, cellular stress response, or disease etiology. Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) allows for measuring the genome-wide binding of TFs. Several computational methods have been used for inferring the genes that are targeted by TFs using this binding information, but a thorough evaluation of their performance has not been performed so far. Here we present an assessment of a range of TF-target-calling methods using 68 ChIP-seq datasets. It turns out that the first step of the scoring, the assignment of binding events to genes, is the most important for correctly calling target genes. Our evaluation revealed important performance differences between the target-calling methods, with some simplistic methods exhibiting a particularly poor performance compared to more elaborate scorings. One of the methods is particularly attractive, because it does not require the  Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) is a powerful technique for the genome-wide profiling of protein-DNA interactions, histone modifications, and nucleosome positions Published ChIP-seq studies often use ad-hoc approaches for calling target genes, such as defining as targets all genes with a peak within a certain distance from the transcription start site (TSS) www.cellularnetworks.org. This package greatly facilitates the parallel testing of the various methods on any given ChIP-seq dataset.In this study, we systematically compared different TF target prediction approaches. We focused on the genome-wide prediction of targets using ChIP-seq data alone, because it is often the case that only this data is available. ChIP-seq-based target scores can be later integrated with expression data or any other information that could enhance target identification, if such data is available We have structured the TF-target scoring based on ChIP-seq data into three main steps that are conducted on a gene-by-gene basis : (1) peaWe distinguish window-based and window-free peak-to-gene assignment methods. The simplest approach is to define a fixed window around each TSS and assign all peaks within this window to the corresponding gene  and downstream (3′) of the TSSs. Thereby it implicitly accounts for a potential bias for preferential upstream (or downstream) binding.In the simplest approach, which we refer to as Binary , each ge Linear, , which iak Chen,  [12]. WesestGene , which iBinary and Chen methods do not require any integration; Binary does not depend on the number of peaks and Chen assigns only one peak to each gene. All other methods assume an additive influence of multiple binding events on the gene expression. Therefore, the total TF-gene association score is computed as the sum of the individual scores of the peaks assigned to that gene.The last step consists of integrating the scores of all peaks that have been assigned to a given gene. The Binary and Linear methods we tested several window sizes and chose the optimal one for the comparison with other methods . TF-target calling was performed using two sets of ChIP-seq experiments: The first is the HemoChIP compendium consisting of 53 ChIP-seq studies performed in mouse hematopoietic cells We have evaluated six target assignment methods that are representative for the different classes of methods being used in the recent literature . For theAn obvious way to compare the performance of target scoring methods would be to use known target genes as benchmarks. However, since relatively few target genes have been validated in small scale studies, we have chosen to conduct a range of unbiased tests employing different genome-wide expression datasets and using functional consistency of the predicted targets as a criterion.First, we used perturbation expression profiling data, i.e. measurements of differential expression after inhibiting a specific TF either through RNA interference (RNAi) or genetic modifications. Functional targets of a TF are expected to respond stronger to the perturbation of a TF than non-targets. Even though perturbation experiments have indirect effects on genes that are not direct targets of the TF, a bigger overlap with perturbation expression data is indicative of better target scoring Binary and Chen performed worst suggesting that considering peak distances from the TSS (neglected by Binary) as well as proper peak-to-gene assignment are crucial aspects of target scoring. In addition, the Chen method suffers from the fact that the scoring is based on one peak only and thus is very sensitive to experimental variation in ChIP-seq data, such as miscalled peaks. The best performing method in this test was ClosestGene  of genes . This gisestGene . SimilarsestGene .ClosestGene performs worse than Linear, Ouyang and Cheng on the HemoChIP data  that a given TF would change its activity. As an effect true targets of the TF should exhibit differential expression between these two conditions. This latter analysis rests on more physiological conditions than engineered TF activity changes, but it has the drawback that several TFs may change their activity between the two cell states. Thus, observed expression differences cannot exclusively be attributed to the TF of interest. This analysis was performed similarly as for perturbation expression data . The reshIP data . SimilarhIP data .ClosestGene had the largest number of relevant GO terms in both cases (HemoChIP and ESChIP). Similar conclusions could be drawn when comparing other sets of target genes . The relatively weak performance of Binary and Chen correlates with the fact that they both ignore the number of peaks in the proximity of a gene. Similar conclusions could be drawn when comparing other sets of target genes  used in this method. Methods using smaller window sizes  or relying on fewer proximal peaks (Chen) show a smaller bias in favor of gene-rich regions. Importantly, Ouyang, which assigns all peaks to each gene (but with decreasing weight) shows a stronger bias than methods considering only peaks in the proximity of the TSS. Compared to the other methods, ClosestGene shows a bias in favor of gene-poor regions, which can be explained by the fact that in a gene-poor region more peaks are likely to be assigned to a single gene. These gene density biases are persistent across different sets of targets  increases the likelihood of finding a peak close to any gene in such region. In order to test the relevance of this issue for target gene identification we compared the gene densities around the highest scoring targets for the different scoring methods . The str targets .In this study we have presented the first systematic comparison of ChIP-seq based TF-target prediction methods using a wide range of ChIP-seq studies from different biological systems. We have developed robust evaluation tests that account for several biases and potential pitfalls. Structuring the target scoring process into three phases helped systematizing the methods and it revealed the peak-to-gene assignment as the most important step. Although all methods resulted in TF-target predictions that are better than random, we observed important differences between the methods.Binary and Chen clearly performed the worst. In contrast, all other methods accounting for the number and distance of peaks around a TSS consistently performed better across all our tests. All of these methods  performed similarly, but we observed a small advantage of ClosestGene especially in the expression data tests and GO enrichment. Although the performance gain was small it was consistent when changing thresholds (see ClosestGene approach presented in this study is a combination of three ideas that have not been used in this combination in the past: (1) assigning peaks to their closest gene, (2) scoring peaks based on the distribution of all peaks around the TSSs, and (3) considering (summing) all peaks assigned to a particular gene. Contrary to all other methods tested, ClosestGene does not require the definition of any parameters, giving it another advantage.A common principle underlying all of these methods is the scoring of putative TF-targets by correlating the positions of TF binding sites with the positions of TSSs. Even though genomic distance is not a perfect predictor for TF-target interactions  this approach yields statistically significant predictions. Individual targets would however have to be validated through functional assays. Out of six compared methods, olds see . This inAn obvious limitation of all methods tested herein is their inability to predict distal regulatory events. Matching long-range DNA interaction data obtained through methods such as 5C Cheng), methods assessed in this paper use the list of identified peaks as input. Since all target prediction methods were faced with the same set of peaks, the evaluation tests allow for a fair comparison of the predictions.Another factor influencing the quality of the TF target prediction from ChIP-seq data is the quality of peak calling itself. Different peak calling algorithms result in different numbers of identified peaks and/or different peak intensities, which in turn influences the performance of target identification methods. All, but one  or pileup height at peak summit as returned by MACS (ESChIP). Peaks with intensity above 200 were excluded from the analysis.www.ensembl.org) using the R package biomaRt For the perturbation analysis, 13 HemoChIP related and 8 ESChIP related expression datasets were collected . In the For the activity analysis of HemoChIP TFs, the normalized expression data were obtained from For the activity analysis of ESChIP TFs, we compared expression profiles for ES cells and differentiated (MEF) cells www.ensembl.org) using the R package biomaRt Genomic positions of the transcripts were retrieved from the Ensembl64 database .For the A gene was designated as a target with a score 1 if there was at least one peak within the window, and as a non-target with a score 0 otherwise. For this approach we chose a window , i.e. spanning the region from 5 kb upstream of the TSS to 5 kb downstream of the TSS .Linear the window was set to   score was implemented as described in http://archive.gersteinlab.org/proj/tftarget/. There are two variants of this method: (1) using a binding profile inferred directly form the wiggle file or (2) using a list of identified peaks as input. We used the first approach and following the original publication, we set the window size to  to calculate binding profile around the TSS.The target identification from profiles (TIP) method was implemented as described in The TF-gene association score was implemented as described in In the first step, each peak was assigned to its closest gene. When a particular gene was scored, only the peaks assigned to that gene, i.e. peaks closer to that gene than to any other, were considered. The peak scoring was derived from the observed peak-to-gene distance distribution.The peak-to-gene distance distribution was obtained by computing the distances between each TSS and all peaks within 1 Mb upstream and 1 Mb downstream of the TSS. Separate distributions for upstream and downstream binding were generated and the distances were pooled across all chromosomes. This distribution approximates the distribution of the distances of all peaks to all genes. The restriction of the distance to 1 Mb was applied in order to minimize artifacts for genes located close to the telomeres and due to different lengths of the chromosomes.Subsequently, individual peaks were scored using the cumulative distance distributions by computing the fraction of peaks observed at the given distance from the TSS or closer (again separately for upstream and downstream binding). This fraction , the overlaps between the top 500 genes ranked according to the TF-gene score in the representative ChIP-seq dataset and the top 500 of genes ranked according to the TF-gene score in other ChIP-seq studies for the same TF were calculated. For op genes .For each TF, for which perturbation expression data were available, the overlap between the top 500 genes ranked according to the TF-gene association score and the top 500 genes ranked according to the fold change between perturbation and control conditions was calculated. In case of the activity analysis respective fold changes from different conditions were used. Bindings were handled in the same way as for the consistency analysis. The overlap between the target scorings and expression data not only depends on the performance of the scoring methods but also on the compatibility between the ChIP-seq data and the expression data (e.g. depending on the cell types measured). In order to correct for this effect the average overlap achieved by all methods for a particular ChIP-seq - expression data pair was subtracted. Hence, this measures how much better (or worse) than average each method performed on a given dataset.We have validated that our conclusions are robust with respect to the choice of this set of top genes  and S6.For each TF the GO enrichment was analyzed using the ‘biological process’ GO category. Genes were ranked according to the target score and the enrichment of top 500 genes with specific GO functions was quantified with Fisher's exact test using the ‘elim’ algorithm from the topGO R package op genes  or p-valop genes .Figure S1Performance of Binary for different window sizes. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S2Performance of Linear for different window sizes. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S3Specificity of TF-target prediction methods. High specificity of target predictions means that there should be higher congruence with perturbation expression data from the same factor than with perturbation data from different factors. Here, we compare measuring consistency based on the overlap of the top 500 genes  with measuring consistency based on the sum of rank products . Specificity is quantified as the difference between the scorings obtained for matching data (matching ChIP-seq and expression data) versus non-matching data, expressed as the –log10 A, C) refers to comparing matching and non-matching pairings from the same cellular system. ‘Other category’  refers to comparing matching pairings with pairing HemoChIP ChIP-seq data with ES cell expression data and vice versa. Larger differences are expected in the latter case. ‘All’ using all genes for the scoring; ‘5000’ and ‘2000’ using only the top 5000 (or 2000) most variable genes. The dashed line corresponds to p-value = 0.05.(PDF)Click here for additional data file.Figure S4Significance of the targets recovery within top 500 genes. Z-scores of the overlap between the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments, (C) between erythroid and myeloid cells, (D) between undifferentiated (ES) and differentiated (MEF) cells. The difference between this visualization and (PDF)Click here for additional data file.Figure S5Targets recovery within different sets of genes (perturbation). Overlap of the top  300,  500 or  1000 targets with the respective number of genes differentially expressed in  HemoChIP and  ESChIP TF perturbation experiments.(PDF)Click here for additional data file.Figure S6Targets recovery within different sets of genes (activity). Overlap of the top  300,  500 or  1000 targets with the respective number of genes differentially expressed  between erythroid and myeloid cells or  between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S7Functional homogeneity of targets for different sets of targets. Number of significantly enriched GO terms among top  300,  500 or  1000 targets specific for a given cellular system and specific for the opposite cellular system for HemoChIP  and ESChIP . The specific terms are hematopoiesis or embryonic development related GO terms for HemoChIP and ESChIP, respectively.(PDF)Click here for additional data file.Figure S8Functional homogeneity of the targets for different enrichment significance thresholds. Number of significantly enriched GO terms specific for a given cellular system and specific for the opposite cellular system for HemoChIP  and ESChIP . The significantly enriched GO terms were defined as the ones with p–value<0.05A,B) or p–value<0.0001C,D) of the Fisher's exact test.(PDF)Click here for additional data file.Figure S9Consistency of target gene predictions for different sets of targets. Independent ChIP-seq experiments are available for some of the factors measured in the hematopoietic system. Consistency of target predictions is quantified as the overlap between the top (A) 300, (B) 500 or (C) 1000 target genes. Results are summarized based on intersecting targets from pairs of ChIP-seq experiments measuring the same transcription factor (‘same TF’), using ChIP-seq experiments from different factors, but the same system  and using ChIP-seq experiments from a different system . Numbers on the right are rounded p-values, measuring the significance of the difference between the overlaps (t-test). P-values for the comparison ‘same TF’ versus ‘HemoChIP’ are generally less significant than ‘HemoChIP’ versus ‘ESChIP’, because the number of comparisons (observations) is smaller.(PDF)Click here for additional data file.Figure S10Characterization of binding events with different numbers of co-factors. Density distribution of binding sites around TSSs as a function of the number of factors binding (as shown in legend). Figure shows a fragment of the density distribution plot; the density below the lines sums to 1 for distances from −1 Mb to 1 Mb from the TSS.(PDF)Click here for additional data file.Figure S11Characterization of binding events with different intensities. Density distribution of peaks around TSSs as a function of normalized peak intensity  for (A) HemoChIP and (B) ESChIP. Figure shows fragments of density distribution plots; the below the density lines sums to 1 for distances from −1 Mb to 1 Mb from the TSS.(PDF)Click here for additional data file.Figure S12Combination of peak-scoring criteria. Peaks are scored based on their distance to the TSS (Distance), intensity (Intensity) or number of co-binding factors at the same site (Co-binding) or combinations thereof (as indicated). Peak-to-gene assignment and score integration is done using the ClosestGene scheme. Overlap of the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Overlap of the top 500 targets with the top 500 genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Figure S13Gene density in target regions for different sets of targets. Gene density (number of genes inside 1 Mb regions around the target gene's TSS) of the regions harboring the top 300 , 500  or 1000  genes across the studies in the HemoChIP  and ESChIP datasets .(PDF)Click here for additional data file.Figure S14Characterization of transcriptional regulators. Fraction of peaks binding at promoters, or co-occurring with P300 or CTCF binding. Overlap encompasses peaks falling in more than one of those categories. Regulators are grouped depending on the cellular system in which the corresponding ChIP-seq study has been conducted .(PDF)Click here for additional data file.Figure S15Performance of ClosestGene for different scorings. Z-scores of the overlap between the top 500 targets with the top 500 genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments, (C) between erythroid and myeloid cells, (D) between undifferentiated (ES) and differentiated (MEF) cells. ClosestGene corresponds to ClosestGene using TF-specific peak-to-gene distance distribution. ClosestGene (symmetric) corresponds to the variant where a distribution symmetric around a TSS . ClosestGene (unspecific) corresponds to the variant where a distribution specific for another TF is used for peak scoring. Linear 1Mb corresponds to the variant where peaks assigned to the TSS are scored using linearly decreasing weights.(PDF)Click here for additional data file.Figure S16Peak-to-gene distance distribution. Peak-to-gene distance distribution for (A) OCT4 and (B) P300 used for peak scoring.(PDF)Click here for additional data file.Figure S17Performance of the OCT4 target prediction using different distributions. Z-score representing the significance of the overlap between top 500 targets and top 500 genes differentially expressed after Oct4 knock-down (‘perturbation’) or between ES and undifferentiated (MEF) cells (‘activity’) when scoring OCT4 peaks using OCT4 (‘OCT4’) or P300 (‘P300’) distributions.(PDF)Click here for additional data file.Figure S18Overlap between targets and differentially expressed genes. Median overlap between top 300, 500 and 1000 targets with the respective number of genes differentially expressed in (A) HemoChIP and (B) ESChIP TF perturbation experiments. Median overlap between top 300, 500 and 1000 targets with the respective number of genes differentially expressed (C) between erythroid and myeloid cells or (D) between undifferentiated (ES) and differentiated (MEF) cells.(PDF)Click here for additional data file.Table S1Transcription factors and data included in the study.(PDF)Click here for additional data file.Text S1Supporting text. (1) TF characterization based on ChIP-seq studies. (2) Importance of using TF-specific peak-to-gene distance distributions. (3) Incorporation of peak height or binding of co-factors does not improve target prediction. (4) Significance and robustness of evaluation methods. (5) Ranking is biased by non-changing genes. (6) Q-value calculation for ClosestGene scores.(PDF)Click here for additional data file.


Fourier transform mid-infrared (FT–IR) microspectroscopy is a label-free optical method based on the interaction between an incident light beam and matter. This vibrational spectroscopy permits to probe the biochemical composition of the analyzed sample and thus gives information about the structure of this sample. Associated with an imaging system, FT–IR microspectroscopy of human tissues can be used as a very sensitive, non-destructive and non-subjective tool for the detection and localization of tumoral areas independently of visual morphology. Thus, FT–IR microimaging has demonstrated potential to provide clinically relevant diagnostic information in oncology -3.The biochemical changes related to carcinogenesis between cancerous and surrounding tissue areas are subtle. As a consequence, IR hyperspectral images need to be processed by powerful digital signal processing and pattern recognition methods in order to highlight these changes ,5. To thIR spectral images were acquired on 8 biopsies of formalin-fixed paraffin-embedded human skin carcinomas, squamous cell carcinomas  and basal cell carcinomas . The samples were selected by the pathologists from the tumor bank of the Pathology Department of the University Hospital of Reims (France).2)  window for FT–IR imaging without any particular preparation, especially no chemical dewaxing. First adjacent slices (5-µm thick) to those used for FT–IR analysis were stained with hematoxylin and eosin (H&E) for conventional histology. From these slices, the cancer outlines defined by the pathologists were drawn on the photomicrographs.From samples, 10-micron thick slices were cut and mounted on a calcium fluoride , with a spatial resolution of 6.25 µm and a spectral resolution of 4 cmThe samples being analyzed without previous chemical dewaxing, the recorded FT–IR hyperspectral image were digitally corrected from paraffin spectral contribution thanks to an automated pre-processing method based on extended multiplicative signal correction (EMSC) . Only thIn order to highlight the different biological structures of the samples from the weak inter-spectra differences, the EMSC-based pre-processed IR spectra were analysed by an upgraded version of the FCM clustering-based algorithm proposed in . The innAfter the application of the upgraded version of the FCM-based algorithm, the reconstructed color-coded clustering images allow recovering different histological structures automatically, particularly to precisely localize tumoral areas and their normal counterparts. Due to the limited length of the article, here we present only one representative case of SCC.The images generated by the FCM-based algorithm are shown in Figure More than reproducing classical histology, our algorithm can give access to additional information about the assignment of the IR image pixels to the tissular structures. For each pixel, fuzzy clustering provides membership values, permitting to nuance their assignment. Such data are very valuable for the pixels located at the interface between tumoral tissue and its microenvironment. However, to ease the interpretation of transitional areas between tumor and marginal normal tissue, we developed an entropy based interconnectivity measure which is maximal when a pixel equally belongs to two clusters. Applied on the SCC IR image as shown on Figure These areas cannot be identified on hematoxylin-eosin staining or by conventional clustering of IR data.IR spectral microimaging associated with clustering techniques shows a great potential for the direct analysis of paraffin-embedded tissue sections of human skin cancers. These preliminary results show significant potential for probing tumor progression and for early determination of tumor aggressiveness in cutaneous cancers. Experiments are underway to define the molecular assignments of the spectral variations observed in these peritumoral areas. Furthermore, this approach could be applied to other human skin cancers such as melanoma.FT–IR: Fourier transform mid-infrared; FCM: fuzzy C-means; SCC: squamous cell carcinoma; BCC: basal cell carcinoma; H&E: hematoxylin and eosin; EMSC: extended multiplicative signal correctionThe authors declare that they have no competing interests.• DS realized the acquisitions of the data, developed the original FCM based algorithm, applied it on the IR images and drafted the extended abstract.• CG developed the upgraded FCM based algorithm and the entropy based interconnectivity measure, and drafted the extended abstract.• VV developed the original FCM based algorithm.• PJ, OP and MM designed and managed the project


A 39-year-old Iranian female patient who was a known case of primary cervical dystonia since 10 years ago presented to a private office for Dysport injection. The patients experienced severe dysphagia after inferior alveolar nerve block which was preceded by cervical botolinum toxin injection. A possible synergistic effect of botolinum toxin and lidocaine to produce severe dysphagia is presented in this case report. Idea for possible therapeutic usage of botolinum toxin (BTX) dates back to 18th and 19th century proposed first by Justinus Kerner (1786 - 1862). He called this toxin “sausage poison” (i.e. botulus means sausage in Latin)  direct invasion of carotid sheath was not presumed to be the case. Briefly, possible mechanisms for the explanation of dysphagia preceding IANB and BTX therapy are as the followings: 1-diffusion of either lidocaine or BTX to the adjacent carotid sheath and paralysis of within IX-XIth cranial nerves, 2- direct diffusion to the adjacent muscles and induction of paralysis at motor-end-plate or neuromuscular junction level with possible synergistic effect of lidocaine and BTX. Botolinum toxin may diffuse 30 - 45mm around the site of injection, although further spreading depends on anatomic features of the location, dosage and probably the type of botolinum toxin and its preparation . These diagnostic tools were not available at the hospital where the patient was admitted. She was uncooperative to perform a barium swallow study; hence no additional imaging study was carried out for her. Proposed synergistic effect of botolinum toxin and lidocaine needs future in-vitro and in-vivo approval investigations. There are evidences of beneficial features of lidocaine to alleviate pain and improve dystonia in patients with secondary dystonia due to parkinson disease or complex regional pain syndrome (CRPS) previously known as reflex sympathetic dystrophy syndrome (RSDS) , 7. Prob


Nutritional infertility is very common in societies where women fail to eat enough to match their energy expenditure and such females often present as clinical cases of anorexia nervosa. The cellular and molecular mechanisms that link energy balance and central regulation of reproduction are still not well understood. Peripheral hormones such as estradiol, testosterone and leptin, as well as neuropeptides like kisspeptin and neuropeptides Y (NPY) play a potential role in regulation of reproduction and energy balance with their primary target converging on the hypothalamic median eminence-arcuate region. The present study was aimed to explore the effects of negative energy state resulting from intermittent fasting dietary restriction (IF-DR) regimen on complete hypothalamo-hypophysial-gonadal axis in Wistar strain young female and male rats. Significant changes in body weight, blood glucose, estrous cyclicity and serum estradiol, testosterone and LH level indicated the negative role of IF-DR regimen on reproduction in these young animals. Further, it was elucidated whether serum level of metabolic hormone, leptin plays a mechanistic role in suppressing hypothalamo-hypophysial-gonadal (HPG) axis via energy regulators, kisspeptin and NPY in rats on IF-DR regimen. We also studied the effect of IF-DR regimen on structural remodeling of GnRH axon terminals in median eminence region of hypothalamus along with the glial cell marker, GFAP and neuronal plasticity marker, PSA-NCAM using immunostaining, Western blotting and RT-PCR. Together these data suggest that IF-DR regimen negatively influences reproduction in young animals due to its adverse effects on complete hypothalamus-hypophysial-gonadal axis and may explain underlying mechanism(s) to understand the clinical basis of nutritional infertility. Animals adjust their energy requirements when food is scarce or have a very high acquisition cost Circulating leptin plays a potential role in nutritional signaling to the brain and in the regulation of reproduction and energy balance with its primary target pathway in the hypothalamic arcuate nucleus Kisspeptin and NPY neurons are considered good candidates in linking pertuberations in energy balance with alterations in the activity of reproductive axis. NPY, a potent orexigenic neuropeptide involved in the neuroendocrine control of reproductive axis, may metabolically regulate KISS-1 expression in the hypothalamus in vivo also blocked the retraction of glial processes and decreased GnRH neuron remodeling to secrete GnRH in perivascular space Fluctuating physiological conditions reversibly alter the structural relationship among the neurons and glial cells in the hypothalamus and the functional pathways over which the information is transmitted to the neuroendocrine neurons The current study was aimed to understand the cellular and molecular mechanisms that may link energy status with reproduction in animals on IF-DR regimen for 12 weeks. 3–4 months old young adult rats of both the sexes were used for this study. After assessing the effect of IF-DR regimen on estrous cycle and serum level of estradiol and LH in female and testosterone and LH level in male animals, the study was further extended to explore the serum level of leptin hormone and hypothalamic expression of NPY and kisspeptin neuropeptides which are known to mediate nutritional signaling to regulate reproduction and energy balance at hypothalamic level. Previous studies from our lab and others have reported that GnRH axon terminals and associated glial cells in female rats undergo morphological remodeling to facilitate GnRH release near the perivascular space in median eminence during the proestrous phase of the reproductive cycle All animal work was approved by the Animal Ethics Committee of the Guru Nanak Dev University, Amritsar, Punjab, India.Wistar strain young adult male and virgin cycling female albino rats in the age group of 3–4 months and weighing 150–200 gm were used for these experiments. Animals were procured from Disease free small animal house, CCSHAU, Hisar and were housed 3 per cage under 12 h light and 12 h dark conditions. Animal care and procedures were followed in accordance with the guidelines of Animal Ethical Committee, Guru Nanak Dev University, Amritsar (permit number for this study: 2270/ZD). The estrous cycle was monitored by daily inspection of vaginal cytology in female rats and the paradigm of IF-DR involved periodic fasting, in which 3 months old wistar strain male and female albino rats were deprived of food for a full day, every other day, and fed ad libitum (AL) on the intervening day for 12 weeks. Food was provided and removed at 10.00 am every day. Previous studies have shown that rats and mice maintained on such an every-other-day feeding regimen consume about 30–40% less calories over time and live longer than AL fed animals After 12 weeks of IF-DR regimen, one group of animals (n = 6) alongwith control female rats (n = 6) and DR-R in pro-estrous phase were sacrificed via anesthetic overdose with sodium pentobarbital (100 mg/Kg). The animals were weighed before sacrificing, brain tissue was removed for immunohistochemistry and the blood sample was used for serum isolation. Ovaries were removed from IF-DR, AL fed and DR-R animals and weighed before putting them into formalin solution for fixation. For microscopial evaluation of ovaries, the mid-ovarian sections were cut for studying follicular morphology by Eosin and Haematoxylin staining.Blood sample was collected at the end of the fasting day after completion of 12 weeks of IF-DR regimen and blood sample from age matched AL fed animals  was collected after the overnight fasting of these animals and allowed to clot for 30 min. The samples were then centrifuged for 15 min at 5000 rpm. Serum was separated and immediately assayed for leptin, LH, Estradiol and Testosterone ELISA.The serum leptin and LH concentration from both female and male rat (n = 6 each in duplicate) groups was determined using rat Leptin ELISA kit  and rat LH ELISA kit  according to the manufacturer’s instructions using Multiscan Ascent Microplate Reader (Thermo Electron Corporation). The concentration of serum leptin (ng/ml) and LH (mlU/ml) was then calculated using kit provided standards and Sigma Stat software.Estradiol and testosterone in female and male rats, respectively was assayed in IF-DR and control groups (n = 6 each in duplicate) using ELISA kits from Cayman Chemical Company, Ann Arbor, MI according to the manufacturer’s instructions. All the readings were calculated for the preparation of data prior to the graphical analysis. Standard curve was plotted using kit provided standards and, estradiol and testosterone level are presented in pg/ml.Immunostaining of NPY and kisspeptin in (median eminence-arcuate) ME- ARC region of hypothalamus from AL fed and IF-DR young male and female rats (n = 6 each) was performed by DAB method described earlier Immunofluorescence labelling of GnRH, PSA-NCAM and GFAP in ME-ARC region of hypothalamus from AL fed and IF-DR young male and female rats (n = 6 each) was done according to the protocol described earlier Images were acquired using Nikon A1R Confocal Microscope and the images were analyzed using Image Pro-plus TM software version 4.5.1 from the Media cybernetics. The intensity of immunoreactivity was quantified in randomly selected fields in each section using the count/size command. Five consecutive sections each from 5 to 6 animals in all groups were used for data analysis. An area of interest (AOI) was selected and placed within region on each section and density measurement was made for staining intensity. All the slides were coded for their group assignment and code was not broken till the intensity was measured. An examiner blind to the group assignment of each animal did density measurement. Results are expressed as the average of staining intensity and presented as the mean ±S.E.M. Differences between means were determined using student t-test, and p<0.05 was considered statistical significant.The co-localization of GnRH and PSA-NCAM was done using the co-localization command of the Image Pro-Plus software. This command measures the co-localization coefficient of two or more molecules (tagged with different fluorescent dyes) in precisely the same space.Western blotting was done according to the protocol described earlier –PCR) analysis according to the protocol as described earlier 6 random hexamers (Fermentas). 2 µl of cDNA was amplified in a 50 µl PCR reaction mixture containing two units Taq polymerase, 5 µl 10X PCR buffer, 3.0 µl of 25 mM MgCl2 (Sigma), 1 µl of 10 mM dNTP mix (Fermentas), and 20 picomoles of respective primers as listed in Tissue from ME-ARC region of hypothalamus (n = 5) was pooled from AL fed and IF-DR young male and female rats to isolate RNA using TRI reagent (Sigma) according to manufacturer’s instructions. The expression of kisspeptin, NPY, GnRH, PST and GFAP mRNA was quantified by semi-quantitative reverse transcriptase–polymerase chain reaction  and testosterone  levels were assayed from IF-DR and control rats. Serum LH level was significantly reduced after 12 weeks of IF-DR regimen in both female and male rats as compared to AL fed controls . FurtherSerum was collected from IF-DR, AL fed, proestrous and diestrous animals and leptin level was measured by ELISA. IF-DR regimen resulted in altered pattern of serum leptin level in both male and female rats. The results indicate that IF-DR significantly decrease serum leptin level in female rats as compared to control group. Moreover, IF-DR regimen also caused mild reduction in serum leptin level in IF-DR as compared to AL fed male rats . FurtherRats on IF-DR regimen were sacrificed on the day of fasting for both male and female rats, whereas, AL fed rats were sacrificed on pro-estrous day afternoon. Further hypothalamic NPY expression was studied in dietary restricted diestrous and AL fed proestrous alongwith virgin female rats in diestrous and proestrous phase of estrous cycle. Immunohistochemical study of NPY in arcuate region of hypothalamus indicated that IF-DR regimen resulted in up regulation of NPY expression in hypothalamus which was found to be higher in IF-DR female  and maleImmunohistochemical localization showed decrease in the kiss-ir in female rats maintained on IF-DR regimen  than conDietary restricted animals showed decline in GnRH expression as well as marked morphological changes in GnRH axon terminals in the ME region. The astro-glial cells were seen to lose their star shaped structure which may be due to retraction of their processes surrounding GnRH axon terminals. Co-expression of GnRH and GFAP in AL fed and IF-DR rats is shown in The results of immunofluorescent staining were further confirmed by quantitative analysis of immunoblots and semiquantitative RT-PCR. GFAP and α-tubulin labelling in the representative samples in IF-DR and AL fed female and male rats from median eminence region of hypothalamus is shown in Earlier we have reported that PSA-NCAM-ir is associated primarily with the periphery of the GnRH axon terminals and regulated by excitatory and inhibitory inputs Immunofluorescent staining data was further supported by quantitative analysis by immunobloting and semiquantitative RT-PCR results. PSA-NCAM and α-tubulin labelling in the representative samples from median eminence region of hypothalamus in IF-DR and AL fed female  and maleFeeding regimen of intermittent fasting-dietary restriction of three months duration reduced body weight and blood glucose level in both young (3–4 months) male and female Wistar strain rats and resulted in 19% and 34% loss in body weight in female and male rats, respectively. On the other hand, IF-DR regimen adversely affected the reproductive function as evident from disrupted estrous cycle and reduced ovarian weight observed in IF-DR female rats as compared to AL fed control animals, which showed normal estrous cyclicity. Estrous cycle was disturbed after about two weeks of initiation of alternate day feeding in female rats. The data further revealed that IF-DR regimen markedly enhanced serum estradiol in female rats and reduced testosterone level in male rats, whereas, serum LH concentration was found to drastically decrease in animals of both the sexes on IF-DR regimen. The current data suggests that IF-DR regimen adversely affects reproduction in young adult rats by disrupting estrous cycle in female rats as well as altering serum concentration of estradiol, testosterone and LH in both male and female animals. These results are supported by recent studies reporting significant reduction in body weight of ewes on food restriction and irregular and completely disrupted estrous cycle which were observed in 20% to 40% caloric restricted rats Further, we observed sex-based difference in animals responding to IF-DR regimen as the reproductive alterations were more pronounced in IF-DR females as compared to their male counterparts. The heterogeneous hormonal response has also been observed in the patients with restrictive anorexia nervosa The energy status of an animal, depending on its dietary regimen/intake is known to regulate diverse physiological functions as well as alter estradiol and testosterone levels in males and females The present study further elucidates alteration in the levels of metabolic fuel detectors/regulators such as leptin and hypothalamic ME-ARC region expression of NPY and kisspeptin. We observed significant reduction in serum leptin level in IF-DR rats of both the sexes. The decrease observed in the blood glucose level in rats on IF-DR regimen  may link these fuel detector’s function to inhibit the expression of GnRH and serum LH concentration. Welt et al NPY fibres make synapse with the dendrites and cell bodies of GnRH neurons in preoptic area and GnRH neuron terminals in ME region of hypothalamus The present study further investigated the effect of IF-DR regimen on the morphological remodeling of GnRH neuron terminals in the ME region of hypothalamus as our lab has earlier reported that GnRH axon terminals near perivascular space are known to undergo remodeling in order to facilitate release of neurohormone in hypophysial portal circulation In conclusion, the data shown in


Deformable image registration (DIR) is an attractive method for automatic propagation of regions of interest (ROIs) in adaptive lung radiotherapy. This study investigates DIR for automatic contour propagation in adaptive Non Small Cell Lung Carcinoma patients.Pre and mid-treatment fan beam 4D-kVCT scans were taken for 17 NSCLC patients. Gross tumour volumes (GTV), nodal-GTVs, lungs, esophagus and spinal cord were delineated on all kVCT scans. ROIs were propagated from pre- to mid-treatment images using three DIR algorithms. DIR-propagated ROIs were compared with physician-drawn ROIs on the mid-treatment scan using the Dice score and the mean slicewise Hausdorff distance to agreement (MSHD). A physician scored the DIR-propagated ROIs based on clinical utility.Good agreement between the DIR-propagated and physician drawn ROIs was observed for the lungs and spinal cord. Agreement was not as good for the nodal-GTVs and esophagus, due to poor soft-tissue contrast surrounding these structures. 96% of OARs and 85% of target volumes were scored as requiring no or minor adjustments.DIR has been shown to be a clinically useful method for automatic contour propagation in adaptive radiotherapy however thorough assessment of propagated ROIs by the treating physician is recommended. Dose response data has shown that escalating the tumour dose can improve the probability of local tumour control -4 in radART requires re-contouring of target and organ at risk (OAR) regions of interest (ROIs) for evaluation and re-planning. Currently, re-contouring of images during a radiotherapy course can be very time-intensive. Automatic propagation of target and OAR contours between two image sets is thus an attractive approach to reducing adaptive radiotherapy resource requirements . DIR hasth day after the start of radiation therapy, the details of which can be found in Van Elmpt et al. [Pre-treatment and repeat 4D respiratory correlated PET/CT scans together with a 3D intra-venous contrast-enhanced CT scan were obtained for 17 NSCLC patients. The study was approved by the Institutional Review Board. The repeat 4DCT scan was obtained during the second week of treatment, typically around the 8t et al. . The voxThree DIR algorithms were used – Demons (Fast Symmetric) , SalientThe SFBR algorithm is an automated version of landmark-based registration in which sharply prominent and distinctive features are automatically located in each image. The features are found using an interest point detector algorithm and can be detected at any point in the patient. Typically 1000–2000 features are obtained. Once the features have been located, they are assigned a location determined by the position of their centroid as well as a scale. The salient feature locations are then used as anchor points to interpolate a non-rigid transformation using the Thin Plate Splines (TPS) method.The Morphons algorithm uses quadrature phase differences to estimate deformations between two images ,24. QuadThe Demons and Morphons algorithms result in a deformation vector field (DVF) at the image resolution, in the frame of reference of the second (during treatment) image. DIR was performed by deforming the pre-treatment scan to the mid-treatment scan. Propagation of the ROIs is then performed by looking up the value of the DVF in each voxel in second image and tracing it back to the pre-treatment image and obtaining the value of the binary mask of each ROI at that location. ROIs were propagated using the same technique for both the Demons and Morphons DVF. The SFBR algorithm results in a set of TPS equations that can be evaluated at any point in the image. For propagation, the source ROIs are converted to meshes and the TPS is evaluated at each vertex location in the meshes to determine the location of each mesh in the second image. No smoothing of the propagated ROIs was performed for any algorithm.p = 0.05 was used as the threshold for statistical significance.The DIR-propagated ROIs were compared with the physician-drawn ROIs on the mid-treatment scan using the Dice score and the mean slicewise Hausdorff distance (MSHD). The Dice score for two ROIs A and B was defined as 2|A∩B|/(|A|+|B|). The MSHD is the average over all slices of the largest value of the smallest distance to agreement between two ROIs on each slice . The difIn addition to the quantitative metric scores, the ROIs were evaluated qualitatively by an expert physician to determine the clinical utility of the algorithms for ROI propagation. A score of 1 was given to ROIs that were clinically acceptable without modification, 2 was given to ROIs that were clinically useful but required minor modification on several slices, and 3 was given to ROIs that were not clinically useful, where it would be more efficient to start the contouring from scratch.Table p = 0.0030 & p = 0.0001 respectively). Figure p = 0.009 and p = 0.005 respectively for left lung, p = 0.025 and p = 0.027 respectively for right lung). For all other ROIs, there was no statistically significant difference between the algorithms. The average COM differences , 4 min 16 s (SFBR) and approximately 60 min (Morphons). Although at the time of use, none of the algorithms were optimised for speed, Demons and SFBR both have elements that are multi-threaded and were performed on a 16-core Sun Fire x4450 system. DIR with the Morphons algorithm in contrast was performed using 8 dual-core Intel Xeon X5550 2.66 GHz processors but the implementation was not (yet) multi-threaded. Use of 16 available cores and assuming a fully multi-threaded implementation of the algorithm with no serial component in the code remaining could theoretically could reduce calculation times 16-fold and would result in a calculation time of the same order of magnitude as that for Demons and SFBR.Although the physician scores represent the opinion of one expert physician, this is still a useful test of the clinical utility of automatically propagated ROIs in adaptive radiotherapy. The weak correlation between the physician scores and the Dice scores and MSHDs, suggest that the use of Dice scores and MSHDs alone are not sufficient for complete evaluation of automatically propagated ROIs in the lung/thorax region. The fact that 30% of structures required editing implies that the treating physician must assess all automatically propagated ROIs to ensure all propagated ROIs are sufficiently accurate for adaptive assessment and re-planning. The comparison of the automatically propagated with the physician drawn ROIs must be done in the context of inter-observer variation. In this study all ROIs were delineated by an expert physician using PET/CT image data, which significantly reduces inter-observer variation, thus it is expected that the physician drawn ROIs used for comparison in this study would not vary significantly from those obtained from multiple observers ,28.Three DIR algorithms were used to automatically propagate both normal tissue and target volumes in repeat lung kVCT scans. Reasonably good agreement with physician drawn contours was observed for normal tissues. DIR-propagated nodal-GTV and esophagus structures were not as accurate, most probably due to less soft tissue contrast for these structures. This system is nevertheless a major step forwards for fast and accurate delineation for adaptive radiotherapy. Further work should include a robust analysis of efficiency gains when using automatic propagation of contours in adaptive radiotherapy.DIR: Deformable image registration; GTV: Gross tumour volume; PTV: Planning target volume; PET: Positron emission tomography; CT: Computed tomography; ROI: Region of interest; MSHD: Mean slicewise hausdorff distance; SFBR: Salient feature based registration; OAR: Organ at risk; kVCT: Kilovoltage computed tomography; MVCT: Megavoltage computed tomography; RTPS: Radiotherapy treatment planning system.Author NH performed this work whilst employed at the University of Wisconsin-Madison as a post-doctoral scientist funded through a grant awarded to WAT at UW from Philips Radiation Oncology Systems.Author WAT received grant funding from Philips Radiation Oncology Systems.NH & WAT receive royalties from a patent covering one aspect of deformable image registration awarded to NH and WAT (not included in this manuscript) which is managed by Wisconsin Alumni Research Foundation (WARF).Author KAB is an employee of Philips Radiation Oncology Systems.All authors contributed to manuscript preparation. NH & WvE performed the registrations and quantitative analysis of the registration. KB performed programming of the registration algorithms. WAT assisted in study design and interpretation. DdR performed qualitative analysis of the registration results. All authors read and approved the final manuscript.


By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor . The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively;  The patients with similar prognosis often respond differently to the same treatment. One explanation for this is that disease with similar phenotypes may have different genetic causes, a phenomena of genetic heterogeneity. Such genetic heterogeneity presents a significant challenge for modern clinical practice and biomedical research on common human diseases. MicroRNAs are a class of small noncoding RNAs that regulate translation of protein coding mRNAs by translational inhibition or cleavage of the mRNA transcripts. In humans, miRNAs are approximately 22-nucleotide-long single-stranded RNAset al. MicroRNA(MiRNA) expression profiles of tumour samples have recently been shown to provide phenotypic signatures for specific types of cancer In this study, we aimed to identify a subset of miRNAs that could dissect breast cancer patients with different survival outcomes. We employed a coupled two-way clustering algorithm (CTWC) http://www.ncbi.nlm.nih.gov/geo/). The raw dataset consisted of the expression profiles of 309 miRNAs in 137 samples in which 119 passed quality control . The original breast cancer study used a single sample predictor (SSP) to assign individual samples to one of five breast tumour subtypes: Luminal A, Luminal B, Basal-like, HER2+ and Normal breast-like A miRNA expression data (GSE7842) was download from the Gene Expression Omnibus (GEO) are obtained. A detailed description of the SPC and CTWC algorithm can be found in We obtained many high-correlation sample partitions and miRNA subsets by CTWC. To evaluate the power of dissecting tumour subtypes, we developed a functional consistency score that evaluates the biological significance of a miRNA subset. The steps for computing a functional consistency score of miRNA subsets (1) Correlation coefficient (CC). For each .(2) Computing the chromosome cluster. Based on the chromosome location of each miRNA in human via MiRGen For each pair(3) Functional consistency. For eachA higher p<0.05) of the effects if the miRNAs is included in the identified miRNA subset(s) on the patient' survival months. Wald Chi-square test was used to determine the significance of each predictor's hazard toward the survival time.To verify the clinical significance of the identified hidden breast cancer subtypes, we plotted their survival curves by Kaplan–Meier product-limit method, and assessed the differences between the survival curves of breast cancer patients belonging to different subtypes by log-rank test. The multivariate Cox proportion-hazards model was used to predict the overall survival time, and to determine the significance  of the top 5 subsets For 5 microRNA subsets with higher CC, we mapped all combinations of microRNAs pairs in each microRNA subset into 51 different chromosome clusters based on MiRGen, and then computed a chromosome consistency score for each miRNA subset. We found that score of et alAmong the 13 miRNAs in et al. using SPC We applied p = 0.008), respectively.To verify the clinical significance of the identified hidden breast cancer subtypes, we plotted the survival curves by Kaplan-Meier product-limit method, and assessed the differences between the survival curves of breast cancer patients with different subtypes by a log-rank test . The 5 yp value of 0.05) for the multivariate Cox proportional-hazards regression model. We found one predictor (miRNAs) in p = 0.038).In order to develop a compact model for clinical use, we further identified miRNAs that contributed mostly to the high prediction power. Multivariate Cox proportional-hazards model was used to analyze miRNAs in NAs) in  . miR-146http://www.ebi.ac.uk/arrayexpress/experiments/E-UCON-1/) was downloaded and 5228 pre-proceeded mRNAs We used the Target-Function-Expression Module of miRGen to predict the targets of each microRNA in Increasing evidence have suggested that miRNAs are involved in cancer development through regulating distinct biological processes, including cellular growth and proliferation, cellular movement and migration, extra cellular matrix degradation. Though the expression level of miRNAs is generally low in cancer, their unique profiles may have significant clinical outcomes, especially for the phenotypes of cancer. Recently, many studies have shown that the abnormal expression of miRNAs is correlated with human breast cancer. In this study we showed that the two-way clustering algorithm could result in an improved prognostic accuracy over the breast cancer patients' survival profiles.Computational discoveries of the hidden subtypes for a complex disease have to be verified by some means, e.g., a functional assay using bioinformatics approaches or a clinical validation using epidemiological approaches such as survival analysis. In unsupervised clustering analysis, however, identifying the best subset for dissecting clinically heterogeneous disease can be a challenging task as no cross-validation can be done internally. The underlying assumption for a clustering algorithm is that microRNA with similar expression patterns and adjacent chromosome location are more likely to have a similar biological function(s). However, a clustering algorithm itself does not provide proof of the best grouping of microRNA in terms of biological functions. Thus, the biological interpretation of the disease clustering results relies heavily on the expert knowledge which often may be subjective et al. Many methods use all the microRNAs on chips or a large number of microRNAs to predict patient survival. Since the vast majority of the microRNAs in a given dataset are irrelevant to the survival of the studied patients, this may reduce the prediction accuracy of the model because of the added noise. Hence, McLachlan Application of the proposed approach to breast cancer datasets led to identification of microRNA subsets, and further multivariate Cox proportional-hazards modeling defined microRNA-146b as one significant predictor for the survival of the breast cancer patients in the dataset. The individual miRNAs have only limited impact on their targets and multiple miRNAs are needed to drastically reduce transcription levels of target. We also found the targets of serval microRNAs in the optimal miRNA subset involoved similar pathways. Overall, our results demonstrated that the proposed approach is highly promising for peeling off the hidden genetic heterogeneity based on modern omics data, and may lead to an improved diagnosis and treatment of cancers.Table S1The correlation coefficient of among miRNAs in .(DOCX)Click here for additional data file.Table S2The anti-association miRNA-mRNA of among miR-146b.(DOCX)Click here for additional data file.