Circular chromosome conformation capture (4C) has provided important insights into three dimensional (3D) genome organization and its critical impact on the regulation of gene expression. We developed a new quantitative framework based on polymer physics for the analysis of paired-end sequencing 4C (PE-4Cseq) data. We applied this strategy to the study of chromatin interaction changes upon a 4.3 Mb DNA deletion in mouse region 4E2.A significant number of differentially interacting regions (DIRs) and chromatin compaction changes were detected in the deletion chromosome compared to a wild-type (WT) control. Selected DIRs were validated by 3D DNA FISH experiments, demonstrating the robustness of our pipeline. Interestingly, significant overlaps of DIRs with CTCF/Smc1 binding sites and differentially expressed genes were observed.Altogether, our PE-4Cseq analysis pipeline provides a comprehensive characterization of DNA deletion effects on chromatin structure and function.The online version of this article (doi:10.1186/s12864-015-2137-5) contains supplementary material, which is available to authorized users. Chromatin organization in eukaryotic cells is associated with patterns of transcriptional activity and genomic stability (reviewed in –5). WithAll 3C technologies use cross-linked chromatin to identify genomic interactions in the nuclear space, providing estimates of the frequency of contacts of specific regions with the rest of the genome (reviewed in ). Among However, chromatin fibers can be modeled as a polymer, i.e., “beads-on-a-string”, with nucleosomes as beads, and linker DNA as the string. Polymer entropy, or the random thermal exploration of all the spatial configurations of a polymer, can dictate the expectation of contact probabilities for a chromatin region. This probability of entropy-driven random contacts has been well-characterized in the polymer physics literature, and has been described as a power-law fall off of interactions between chromatin fragments with increasing genomic separation , 17. ThiDf), as well as marked chromatin decompaction detected downstream of the deletion position. DIRs were enriched in differentially expressed (DE) genes as detected by RNA-Seq, as well as CTCF and Smc1 binding sites, proteins that had been previously characterized as structural determinants of chromatin organization [We developed a novel quantitative framework for the analysis of multi-viewpoint PE-4Cseq data firmly grounded on polymer physics. Our pipeline corrects for PE-4Cseq data biases, normalizes data, and computes the contact probability between a viewpoint fragment and all other fragments in the chromosome, allowing the quantitative testing of differences in chromatin contacts and chromatin compaction. In particular, this method is especially suited for the analysis of complex chromosome modifications such as copy number variants (CNVs). We applied this new approach to study chromatin interactions within and around a 4.3 Mb engineered deletion in mouse region 4E2 , which inization –26.Collectively, our work demonstrates the successful development and application of a quantitative PE-4Cseq analysis pipeline, and illustrates for the first time the complex and diverse effects of a DNA deletion on both the local and global signatures of chromatin organization and gene expression.When chromatin is modeled as a polymer, PE-4Cseq experiments provide the probability of each viewpoint fragment contacting any other fragment within the same chromosome; we call these frequencies the intra-chromosomal contact probability profile (CPP) of the viewpoint , and the specific interactions that have a biological origin. CPP signal differences are key in drawing meaningful conclusions on distinct control and test experimental conditions.I, I + 1) are connected by Gaussian springs with spring constants kI,I + 1. These spring constants are a measure of the strength of tethering of one chromatin fragment to another mediated by the intervening chromatin fiber, and exhibit a power-law scaling for intermediate genomic separations (100 Kb-10 Mb). The CPP, represented by PIJ, has the scaling form sIJ is the genomic separation between viewpoint positions I and J, and νI is the local scaling exponent. The CPP typically falls off with increasing genomic separation of interacting fragments—entropic interactions between distal fragments are rare—therefore νI is always negative. Different polymer models of chromatin have different scaling. For example, random-walk (Gaussian) chromatin polymers exhibit a uniform scaling exponent ν = − 1) leading to the hypothesis of a non-equilibrium  model of chromatin [νI as a measure of viewpoint-specific chromatin compaction and its variability upon deletion occurrence, as presented below.We set the null expectation, of entirely entropic origin, for each viewpoint’s CPP using a generalized Gaussian “beads-on-a-string” polymer model of chromatin  viewpoint sequences, KI is the overall bias factor for the capture experiment of viewpoint I, and PIJ is the average CPP for viewpoints I and J. Similarly, for the experiment corresponding to viewpoint J, the normalized capture data is FJI = CICJKJPIJ; from these equations, note that only the experiment bias factor KJ is distinct. We then solved the linear set of equations (in logarithm space) to compute the bias factors C and K from FIJ and FJI. The unbiased estimate of the contact probability, PIJ, should be symmetric for both I and J viewpoints. We performed this bias correction using only nearest and next-nearest-neighbor viewpoint pairs, and observed that biases for all other viewpoint pairs are significantly reduced and PIJ is very close to being symmetric, whereas FIJ is clearly not  observable FIJ. Therefore, at least four viewpoints to perform bias correction analyses are needed.We removed biases without modeling them individually by their technical or biological sources. We first normalized the capture data for each experiment by the product of viewpoint and fragment lengths, and call this the biased contact probability (BCP) Fig. . This noI and fragment α is defined as FIα is the BCP, CI is the overall viewpoint I bias factor, and KI is the bias factor for the PE-4Cseq experiment of viewpoint I. Once the raw CPP per viewpoint was obtained, we thresholded and smoothed it using a Gaussian kernel of standard-deviation 20 Kb  for whole chromosome contact comparisons. The 20 Kb standard deviation is in the upper range of viewpoint sizes (~0.5-20Kb) in our experiments, and is the genomic resolution of the PE-4Cseq measurements.We used the bias factors to estimate the unbiased CPP per viewpoint and for all their contacting intra-chromosomal fragments (captures) Fig. . The CPPPdel − log PWT)/log PWT, between Df and + DBl6 (del) chromosomes relative to the WT CPPs for +129 and + Bl6 in two biological replicates . We performed this testing independently for the two biological replicates, and reported DRCPs only when they were present (within the window size) in both replicates. The regions of the chromosome with significant DRCP signals were catalogued as differentially interacting regions (DIRs).The smoothed CPP per viewpoint was used to compute the differential relative contact probability (DRCP) signals, defined as  [Df/ + DBl6 and their corresponding +129/+Bl6 WT controls  , therefoDf/+ DBl6 mice, as well as +129/+Bl6 controls. A total of 12 viewpoints spanning 4E2 were selected  were derived from F1 4360 see .Df when compared against +129   with a median size of 225.5 Kb and maximal size of 1.195 Mb  with a median size of 195 Kb and a maximal size of 454 Kb  .Each viewpoint’s contact probability falls off with increasing genomic separation, Ivaries for each viewpoint I in Df/+ DBl6 MEFs. Overall, considerable variation was observed between the scaling exponents for the 12 viewpoints along the + DBl6 chromosome, in agreement with observed qualitative variation in local compaction in Hi-C data heatmaps [νI values are observed for viewpoints 11 and 12 in the Df chromosome, which are less compact than expected upon deletion , important features of nuclear architecture and genomic regulation [DBl6 also displayed significant differences in chromatin compaction . 3D DNA FISH experiments provide direct measurements of physical distances separating any pair of genomic loci inside the nucleus, which can be used to estimate their contact frequencies , while the fourth region (BAC set 4) tested a specific interacting pair observed in our PE-4Cseq data not classified as a DIR but enriched in CTCF and Smc1 binding sites  probes overlapping DIR positions (‘query probes’), with a third BAC probe inside the deletion region (‘deletion probe’) Fig. . The del129 measurements  and deletion (Df/+ DBl6) chromosomes compared to the expectation for similar genomic separation  [CNVs are known to affect expression patterns of distal and neighboring genes –37. SomeRNA-seq) –40.Df/+ DBl6 and +129/+Bl6 MEFs RNA-seq experiments revealed 1345 differentially expressed (DE) genes between both genotypes . Enrichment analysis revealed 4E2 and 4E as the cytogenetic bands with the most significant DE clustering locations in Df/+ DBl6 MEFs . Interestingly, the 4D region, directly upstream of 4E, was the next most significant DE clustering location in the genome (p = 2.27e–08). 34 % of DE genes in chromosome 4 are contained within Df DIRs, and 24 % of DE genes are contained within + DBl6 DIRs  . Similarly, Gene Ontology (GO) analysis into cellular function for Df/+ DBl6MEFs revealed 26 genes associated with “condensed nuclear chromosome” , where the deletion resides. Interestingly, despite the elimination of only the 129S5/SvEvBrd allele inside the deletion region, 198 DE genes located in diverse genomic regions along all mouse chromosomes were mis-regulated at both alleles, and their allelic fold changes were highly correlated , with the exception of the deleted genes located inside the deletion region, which exhibit a higher fold change for the 129S5/SvEvBrd allele only , or by positioning two different chromatin regions near to each other (deletions). Therefore, it is important to distinguish interactions that arise from a clear biological source, from those originated by polymer entropy. Since we explicitly constructed a null model of chromatin CPP, we were able to distinguish specific interactions not explained by altered genomic proximity upon DNA deletion.To test the utility of this new pipeline, we studied the changes in chromatin organization occurring upon the deletion of a 4.3 Mb segment of mouse chromosome region 4E2. The mouse 4E2 chromosome band is a particularly interesting region as it is orthologous to human 1p36, where heterozygous deletions are recurrent in cancer  and origDf/+ DBl6) MEFs, we uncovered extensive changes in local and long-range chromatin interactions occurring upon the 4.3 Mb deletion in 4E2. Up to 22 % of chromosome 4 sequence in Df, and ~10 % of sequence in + DBl6 are engaged in altered intra-chromosomal contacts (DIRs) as revealed through the analysis of 4 viewpoints bordering the deletion. ~9 Mb of sequence is shared between Df and + DBl6 DIRs, implying that the 4.3 Mb deletion was not affecting Df chromatin conformation alone, but also + DBl6 by trans mechanisms. Interestingly, both Df and + DBl6 DIRs are enriched for CTCF and Smc1 binding; these proteins have been previously identified as structural determinants of chromosome structure [Df DIRs may be a direct consequence of the alteration of preferred chromatin conformation in the 4E2 region, future studies are needed to determine how cis and trans effects in chromatin organization are derived. Possible causes may be due to changes in the transcriptome owing to the deletion of genes inside the deletion. It is noteworthy to mention that gene expression analysis through RNA-seq revealed an upregulation in CTCF and other chromosome condensation genes in Df/+ DBl6 MEFs compared to +129/+Bl6. The increased expression of these proteins in Df/+ DBl6 MEFs could have important consequences in chromosome architecture for this genotype, and directly participate in DIR formation in cis and trans. Future chromatin immunoprecipitation experiments would be able to test this hypothesis.Using allele-specific PE-4Cseq experiments on heterozygote deletion/WT , which could cause the intervening chromatin to extend upon the occurrence of the 4.3 Mb deletion. We verified a select few Df DIRs as well as a chromatin decompaction region through 3D DNA FISH. Notably, we found a strong agreement between the strengths and direction of change for both experimental modalities, thus validating our PE-4Cseq analysis pipeline and demonstrating the existence of chromatin reorganization upon the occurrence of the deletion in Df.A surprising reduction of local chromatin compaction was identified for viewpoints located near the telomeric end of the Df and + DBl6 DIRs. Interestingly, there is a high genome-wide correlation between allelic fold expression changes for the non-deleted alleles. While it is possible that chromatin interaction changes may be responsible for the altered expression of these genes, deriving associations between gene expression and chromatin conformation in a DNA deletion context is not easily attained.Altered gene expression, as measured by RNA-seq, was observed for a significant number of genes falling within or flanking Df/+ DBl6 mouse genotype can be a model of heterozygous Monosomy 1p36 deletions in human. Such deletions frequently occur de novo and tend to have different sizes and positions [Df/+ DBl6 DIRs. One such genes, endothelin-converting enzyme 1 (Ece1) [Df DIR with a 30–40 % decrease in contact probability, and its expression is decreased in Df/+ DBl6 MEFs. It is tempting to speculate that Ece1 could be a candidate gene for which positional long-range effects on deletion alter gene expression, leading to phenotypic consequences in Monosomy 1p36 patients. However, this hypothesis remains to be tested.Future studies of chromatin architecture in deletion/duplication model systems will be essential to expand our understanding of human disorders linked to the presence of CNVs or other genomic rearrangements such as balanced chromosome translocations. The ositions –45. Inteositions , which s1 (Ece1) , 47, 12 In summary, the development and validation of a new PE-4Cseq analysis framework based on polymer physics has revealed the extensive impact of a 4.3 Mb deletion in mouse chromosome 4 on local and global chromatin organization and gene expression. Future studies will assess deletions or duplications in other regions of the genome to determine if similar or different effects on gene expression and nuclear organization occur. Such studies will shed more light on the complex interplay between gene expression, chromosome structure and genomic rearrangements, and their impact on disease states.Df, a well characterized 4.3 Mb engineered deletion of the 4E band spanning D4Mit190-D4Mit51 derived from 129S5/SvEvBrd ES cells [Df/+ DBl6progeny. F1 + 129/+Bl6 crosses were used as controls. MEFs were derived from 13.5 day Df/+ DBl6and +129/+Bl6 embryos, and confluent passage 4 MEFs were used for all the experiments . The phenotypic characteristics of Df/+ DBl6 MEFs and mice are as previously described [In order to study chromatin organization of a DNA deletion in a heterozygous setting, we used an engineered mouse model for which we could identify the deletion chromosome from its WT homologue. For this reason, we used Df/+ DBl6 (MEF lines 129S5E71 and 129S5E98) and +129/+Bl6 (MEF lines 129S5E117 and 129S5E118) were prepared as described in [4C templates for xi} of all the fragments i, and is given by ki,i + 1 is the spring constant associated with the chromatin segment intervening the centers of fragments i and i + 1 neighboring along the chromatin fiber. In a purely Gaussian model, the ki,i + 1 ∼ 1/si, i + 1, where si, i + 1 is the length of the chromatin segment. We allow for a general and local scaling of the contact probability, Our generalized Gaussian model is defined by the joint distribution of the set of spatial positions { polymer . The modDpnII cutter fragment sizes have a median of ~3Kb, which is the resolution at which each PE-4Cseq experiment queries the BCP . However, at this resolution we only had “one-sided” BCP corresponding to one viewpoint with all fragments (FIj for viewpoint I and fragment j). In order to correct for biases in a general manner, the “two-sided” information of fragment-fragment contact is needed. We obtained this information in two steps: (1) We used the scaling form FIj against the logarithm of genomic distances sIj (of viewpoint I to fragment j) using a smoothing spline. A smoothing spline, as opposed to a linear fit, is used because the scaling form is not expected to apply at very large separations (>100 Mb), where contacts are expected to be very rare and the signal to noisy ratio very poor, and nuclear confinement effects may dominate. The scaling form also breaks down at short distances corresponding to the measurement resolution we discussed above [νI. This length scale is chosen from observation that the spline fit is typically linear from 10 Kb to 10 Mb for all viewpoints . This smoothing factor is in the upper range of viewpoint sizes (~0.5–20Kb) which determine the genomic resolution of the PE-4Cseq measurement. We compared the CPPs to report DRCP  at a signal strength (>5 %) defined by the percentage relative change for smoothed logarithm of CPP profiles of a and b being compared, (log Pa − log Pb)/log Pa. We then tested the statistical significance of each DRCP signal by comparing the raw data (CPPs) for a and b within a window of 80 Kb bracketing each DRCP location. Because the comparison is in difference of the mean of two samples we use a nonparametric test, the Mann-Witney U test. Only when the signal is statistically significant  in both biological replicates in the window, we report the region as a significant DIR. The PE-4Cseq data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibusand are accessible through GEO Series accession number GSE64360 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64360).To compute the CPP (Contact Probability Profile) we thresholded the capture reads for all fragments Df/+ DBl6 (129S5E56) and +129/+Bl6 (129S5E118) MEF slides were prepared using 60 % confluent cultures, and processed with a FISH protocol that preserves nuclear morphology [Pairs of differentially labeled (Alexa 594 and Alexa 488) BAC probes bordering the deletion start and end were used  cannot be distinguished, however, the homologous chromosomes in the deletion heterozygote genotype (Df and + DBl6) can be differentiated, due to one FISH probe being within the deletion region. We constructed analogous 4C comparisons, namely DRCP between Df and the average CP of +129 and + Bl6. We reported the differential in kIJ relative to the WT, (kIJdf − kIJWT)/kIJWT, for quantitative comparison between PE-4CSeq results and FISH validation. We could have also reported probability of contacts between the BACset loci using the distribution of physical probe distances as measured on the microscope. However, the interaction volume within which two fragments are measured as a contact in the 4C capture data is unknown . A direct comparison between FISH and 4C is not feasible without the knowledge of this interaction volume.We used our generalized Gaussian model, introduced in the previous section, to fit the measured distributions of spatial separations of probe pairs in the FISH experiments. In this context, the model dictates the functional form of the probability distribution of spatial distances between FISH probes ll pairs . A Gauss129/+Bl6: 129S5E88, 129S5E90, 129S5E95; Df/ + DBl6: 129S5E36, 129S5E56, 129S5E71, 129S5E98) using TRIzol reagent (Ambion), and polyA+ RNA was isolated . Stranded libraries were prepared using a protocol adapted for paired-end sequencing on the Illumina GA IIx platform [Brd transcriptomes. We used the Ensembl release 72 gene set for the C57BL6/J transcriptome (GRCm38/mm10) [Brd transcriptome was constructed by modifying the C57BL/6J transcriptome using SNPs and indels calls derived from [+129/+Bl6 samples (C57BL/6J x 129S5/SvEvBrd) and Df/+ DBl6 samples. To account for the allelic mapping biases that is a result of more reads mapping to the C57BL/6J transcriptome, we tested for changes in the proportion of reads mapping to each allele between treatment and WT groups, on a gene by gene basis, to determine whether similar degree of changes to expression levels occurred between alleles. Counts were normalized using DESeq and tests were done using the R function, prop.test, using median counts across replicates and p-values were adjusted for multiple testing in R using the fdr method . The software GREAT was used for functional term enrichment analysis with single gene associations [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64360).RNA from seven independent primary MEF lines was isolated (+platform . PEx100 38/mm10) . The 129ved from . Where mved from . Reads wved from , using aciations  as well ciations . Locatio


A major limitation of previous approaches is their inability to handle conditional interactions that emerge when genes are subject to different regulatory mechanisms. Although chromatin immunoprecipitation-based histone modification data are often used as proxies for chromatin accessibility, the association between these variables and expression often depends upon the presence of other epigenetic markers (e.g. DNA methylation or histone variants). These conditional interactions are poorly handled by previous predictive models and reduce the reliability of downstream biological inference.Predictive modelling of gene expression is a powerful framework for the We have previously demonstrated that integrating both transcription factor and histone modification data within a single predictive model is rendered ineffective by their statistical redundancy. In this study, we evaluate four proposed methods for quantifying gene-level DNA methylation levels and demonstrate that inclusion of these data in predictive modelling frameworks is also subject to this critical limitation in data integration. Based on the hypothesis that statistical redundancy in epigenetic data is caused by conditional regulatory interactions within a dynamic chromatin context, we construct a new gene expression model which is the first to improve prediction accuracy by unsupervised identification of latent regulatory classes. We show that DNA methylation and H2A.Z histone variant data can be interpreted in this way to identify and explore the signatures of silenced and bivalent promoters, substantially improving genome-wide predictions of mRNA transcript abundance and downstream biological inference across multiple cell lines.Previous models of gene expression have been applied successfully to several important problems in molecular biology, including the discovery of transcription factor roles, identification of regulatory elements responsible for differential expression patterns and comparative analysis of the transcriptome across distant species. Our analysis supports our hypothesis that statistical redundancy in epigenetic data is partially due to conditional relationships between these regulators and gene expression levels. This analysis provides insight into the heterogeneous roles of H3K4me3 and H3K27me3 in the presence of the H2A.Z histone variant (implicated in cancer progression) and how these signatures change during lineage commitment and carcinogenesis.The online version of this article (doi:10.1186/s13072-015-0013-9) contains supplementary material, which is available to authorized users. Understanding the precise spatiotemporal regulation of eukaryotic gene expression is a central challenge in molecular biology. Three features are common to the epigenetic interactions that underlie transcriptional regulatory programs : sequencCharacterising gene regulatory programs by studying individual protein–protein interactions would require a currently unavailable volume and resolution of proteomics data. Instead, predictive modelling frameworks –7 have bDespite the utility of predictive modelling as a framework for exploring fundamental molecular biology, a major limitation of current approaches is their inability to model the conditional associations that emerge between histone modifications and gene expression in the presence of other epigenetic markers . To highlight this shortcoming, we formulate and evaluate several methods of quantifying promoter-localised DNA methylation and demonstrate that its naïve integration into previous models is unable to improve prediction accuracy. These results are due to statistical redundancy between DNA methylation and histone modification data (previously studied in transcription factors ) despiteIn this study, we introduce a modelling framework that allows the integration of conditional regulatory data by unsupervised identification of latent regulatory classes. We demonstrate that this approach is effective at identifying gene silencing events (promoter methylation) and isolating the heterogeneous roles of H3K4me3 and H3K27me3 conditioned upon the H2A.Z histone variant , leading to substantial improvements in genome-wide accuracy of gene expression predictions. Specifically, our model integrates high-throughput sequencing data for DNA methylation, H3K4me3, H3K27me3, H3K9me3 and H2A.Z to predict mRNA transcript abundance levels (RNA-seq) across all ENCODE Tier1 cell lines . A simplR2 = 0.43 for H1-hESC and KM562, 0.47 for GM12878) are comparable to those of previous studies [Using a regression-based gene expression modelling framework , we eval studies , 14, 15,Figure de novo methyltransferase activity [R2PC2 > 0.4, adjusted R2PCx < 0.05 ∀ x ≠ 2), despite PC1 capturing the most variation in the histone score matrix, A, by definition. The orthogonality of PCA thus suggests that PC1 may capture a (linearly uncorrelated) functional signature of histone modification coordination unrelated to transcriptional regulation  genes than pluripotent H1-hESC, consistent with an increase in DNA methylation-associated gene silencing events during lineage commitment and carcinogenesis. DNA methylation is further implicated by the stronger regulatory signal for H3K9me3 in GM12878 and K562, which is associated with DNA activity . It is alication , 18 or rlication , 20).r = −0.25) and outright best for GM12878 and K562 , with all scores exhibiting stronger anti-correlation in the differentiated cell lines than hESC as expected from the previous discussion.It is widely accepted that promoter-localised CpG methylation prevents the initiation of eukaryotic gene transcription . By exteThe distribution of promoter methylation (MMFS) versus transcript abundance presented in Fig. As demonstrated in Fig. n × m histone score matrix, A, where n is the number of genes and m is the number of histone modifications. In this study, A contains histone scores for H3K4me3, H3K27me3, H3K9me3 and the H2A.Z histone variant, as described in the ‘Methods’ section. We constructed such models for all combinations of cell line and DNA methylation score and found that the resultant improvement in prediction accuracy was negligible in all cases (|adj. R2| ≤ 10−3).A naïve approach to integrating DNA methylation into a predictive gene expression model would amount to simply concatenating the vector of methylation scores as a new column of the Despite the anti-correlation shown between each methylation score and RNA transcript abundance, the naïve integration of this information into predictive models trained on histone modification data yields practically zero improvement in prediction accuracy (irrespective of score or cell line). Within the constraints of a linear model, DNA methylation and the four considered histone modifications are statistically redundant with respect to gene expression. This may be partially due to the well-established negative associations between DNA methylation and H3K4me3/H2A.Z –25 and p+ versus MMFS−) on the basis of a threshold determined through the unsupervised approach described in the methods. Intuitively, this approach should isolate genes subject to H3K9me3/DNA methylation-associated silencing from an otherwise-heterogeneous set.To explore the hypothesis that statistical redundancy between histone modification and DNA methylation data is caused by conditional relationships in methylated promoters, the MMFS score was selected to separate genes into two latent regulatory classes (MMFS− genes into two subclasses (H2A.Z+ and H2A.Z−) due to our earlier observations and other studies supporting its bivalent regulatory role [Unmethylated genes are still subject to a variety of transcriptional regulatory mechanisms, including H3K4me3-associated euchromatinisation (activation) and H3K27me3-associated facultative heterochromatinisation (repression) . H2A.Z wory role . We beliR2) relative to a standard predictive model constructed from the same data. The statistics across all ENCODE Tier1 cell lines are presented in Table In addition to the decision tree structure, Fig. + and H2A.Z−, and our ability to predict the expression of these genes improves substantially .By separating genes into regulatory classes based on the latent signature of methylated and bivalent promoters, it is evident from Table + genes are actually reduced in prediction accuracy, particularly in GM12878. We speculate that this is due to further latent subclasses of H2A.Z-associated regulation. H2A.Z flanks the TSS during transcriptional activation but is evicted during transcript elongation [We note that H2A.Zongation , 31, sugongation ) would iongation –35.+, H2A.Z+, H2A.Z−), allowing researchers to investigate regulatory activity specific to a particular chromatin context. Although this study has utilised prior knowledge to structure a decision tree specific to our regulatory context of interest , the approach can be extended to an arbitrary set of epigenetic markers by systematically evaluating all possible tree structures.Importantly, previous downstream analyses –12 can bPredictive gene expression modelling is an essential tool in computational biology, due to both the critical importance of understanding transcriptional regulation and the current inability to source sufficient proteomics to resolve data on a gene-by-gene basis. However, we believe that previous predictive models designed to explore regulatory activity have been confounded by conditional associations between epigenetic regulators and gene expression within a dynamic chromatin context. This study extends our previous investigation of statistical redundancy between transcription factors and histone modifications , which cOur results demonstrate that the naïve integration of DNA methylation data into a standard predictive model is unable to improve prediction accuracy despite strong anti-correlation between gene expression and our proposed gene-level methylation scores, strongly suggesting that more complex regulatory logic is involved. Instead, we present a new modelling framework that substantially improves genome-wide predictions of mRNA transcript abundance by using DNA methylation data to identify and separate genes into latent regulatory classes. These improvements were demonstrated across all ENCODE Tier1 cell lines , using aThe improved performance across multiple dissimilar cell lines supports our hypothesis that statistical redundancy in epigenetic data is caused by conditional associations between regulators and gene expression. In addition to leveraging DNA methylation data to identify the latent signature of epigenetically silenced genes, we identified the conditional, bivalent associations of H3K4me3 and H3K27me3 with the H2A.Z histone variant. Although predictive models at bivalent promoters remain less accurate than for non-bivalent genes, this study has provided a framework for identifying these genes and demonstrated that the occurrence and regulatory signature of promoter bivalency changes substantially across the lineage commitment spectra (from H1-hESC to GM12878) and carcinogenesis (K562). These findings are of particular importance in the context of recent studies linking H2A.Z over-expression to the progression of various cancers –35.in silico studies in two fundamental ways. Firstly, the accuracy of gene expression models is substantially improved by unsupervised separation of genes into latent regulatory classes. Secondly, researchers have the option of specifying classification variables from prior knowledge (the approach demonstrated in this study) to investigate gene regulatory logic specific to a particular chromatin context (e.g. the role of pioneer transcription factors in euchromatin versus heterochromatin). Alternatively, all combinations of classification variables can be exhaustively evaluated to provide a fully generalisable and unsupervised analysis.It is well-established in previous literature that the utility of gene expression models extends beyond the ability to predict the expression levels of individual genes. The interpretability of these models across all biological problems is, however, limited by prediction accuracy at this level. As well as allowing us to investigate gene expression at methylated and bivalent promoters, we anticipate that this interpretation of conditional regulatory activity in predictive models will improve the explorative potential of future ENCODE Tier1 cell lines  were selected to explore methylated and bivalent promoters, as functional patterns of DNA methylation vary substantially during lineage commitment and carcinogenesis. All cell line gene expression (RNA-seq), histone modification (ChIP-seq) and DNA methylation (methyl RRBS) data were downloaded from ENCODE  and RPKMi, and histone modification, j, is calculated using the constrained sum-of-tags histone score [The association strength between a gene, ne score :1\documegk is the number of ChIP-seq reads  for j mapped to position k relative to the TSS of i. As ChIP-seq involves sequencing of DNA corresponding with the end of each nucleosome, the position for each read was shifted by ±73 bp  to centre on the modified nucleosome [where cleosome . Integracleosome , 10, 14 Sum of methylation fractions by site (SMFS): Sum of the CpG-level methylation scores within a region, similar to the constrained sum-of-tags score previously applied to the analysis of ChIP-seq data Mean methylation fraction by site (MMFS): Equivalent to the SMFS score divided by the number of assayed CpGs within the region, similar to the mean methylation level described by Shultz et al. Mean methylation fraction by region (MMFR): proportion of raw reads that were found to be methylated, similar to the weighted methylation level described by Schultz et al. d/d0), where d is the distance (bp) from the TSS and d0 = 5000, similar to the exponentially decaying affinity score previously applied to the analysis of ChIP-seq data [Sum of scaled methylation reads by region (SMRR): Equivalent to the MMFS score except each read is multiplied by − exp transformation, x) for x ≫ 0, allowing it to be regarded as practically equivalent to the log-transformation applied in previous gene expression modelling studies [x), sinh−1(x) is defined for x = 0, removing the need to meta-optimise small constant to add to x (leading to spurious inflation of reported prediction accuracy) and making it better-suited to integrating ChIP-seq and RPKM-normalised RNA-seq data.where  studies . Unlike R2 score, which in comparison to the standard R2 approach prevents spurious inflation of the statistic due to the introduction of additional explanatory variables [Prediction accuracy is assessed for each regression model using an adjusted ariables . SeparatA . Specifically, the histone score matrix, U is the matrix of component scores, Σ is the diagonal matrix of the singular values of A, and V is the matrix of loadings (weights by which the histone scores are multiplied to derive their respective component scores). In the context of modelling gene expression, the columns of the matrix UΣ are the principal components (PCs), and the rows correspond with eigengenes [where gengenes . The datgengenes .R2 evaluation metric [We provide a framework for improved modelling of conditional and synergistic interactions from matched transcriptomic and epigenomic data. As an illustrative example, gene-level H2A.Z scores  could be used to separate genes into two subsets: those associated with H2A.Z  and those that are not. Separate predictive models can then be constructed and evaluated for both subsets from the remaining regulatory elements (i.e. no longer using H2A.Z as a predictor), as illustrated in Fig. n metric .Fig. 5IlA, and the respective models are presumed to capture homogeneous regulatory logic. Regression models associated with non-leaf nodes are only used for the assignment of categorisation thresholds.This model is implemented in a binary decision tree, where each node represents a linear regression model over a subset of genes and each non-leaf node represents a categorisation step  or not). The genes associated with each leaf node partition the full set of genes from the histone score matrix, n × m matrix A of histone scores (n genes by m histone modifications), histone modification vector AxT  is selected to partition A into two regulatory classes, Ak × m − 1left and An − k × m − 1right, where k is the number of genes for which AxT ≤ γ. In a tree-based representation where Ak × m − 1left and An − k × m − 1right are the left and right children of A, respectively, there are two scenarios for the unsupervised assignment of the threshold γ:If exactly one child of the H2A.Z node is a leaf, the threshold is optimised to maximise the prediction accuracy of the respective model. This model is assumed to capture genes subject to homogeneous regulation.R2 calculated over the concatenated predictions for both regulatory classes).Otherwise, the threshold is optimised to maximise the prediction accuracy of both left and right child models  and balance. At each step, the selection of which variable is used to partition A into separate models can be either manually selected to explore specific regulatory contexts (the approach demonstrated in this study) or automated to exhaustively evaluate all possible tree structures . In the latter scenario, the greedy nature of the threshold selection procedure still does not guarantee a globally optimal set of values; improved prediction accuracy could be obtained by multivariate optimisation across the full set of non-leaf thresholds for an arbitrarily large tree, although we argue that this approach would a) lose the biological meaning (regulated-or-not) underlying the methodology presented, and b) be poorly suited to the recursive implementation preferable for tree-based algorithms.This approach can be repeated recursively for a binary decision tree of arbitrary height (constrained by the number of epigenetic variables, http://sourceforge.net/projects/budden2015treeome/.All scripts used in this study are implemented using open-source software and made available as a pre-configured bootable virtual environment . This en


A method for identifying an aptamer in a single round was developed using custom DNA microarrays containing computationally derived patterned libraries incorporating no information on the sequences of previously reported thrombin binding aptamers. The DNA library was specifically designed to increase the probability of binding by enhancing structural complexity in a sequence-space confined environment, much like generating lead compounds in a combinatorial drug screening library. The sequence demonstrating the highest fluorescence intensity upon target addition was confirmed to bind the target molecule thrombin with specificity by surface plasmon resonance, and a novel imino proton NMR/2D NOESY combination was used to screen the structure for G-quartet formation. We propose that the lack of G-quartet structure in microarray-derived aptamers may highlight differences in binding mechanisms between surface-immobilized and solution based strategies. This proof-of-principle study highlights the use of a computational driven methodology to create a DNA library rather than a SELEX based approach. This work is beneficial to the biosensor field where aptamers selected by solution based evolution have proven challenging to retain binding function when immobilized on a surface. Aptamers are oligonucleotide molecular recognition elements selected through a synthetic iterative evolutionary process termed SELEX  , 2. SincDespite the advantages of aptamers, several significant drawbacks are inherent to the standard SELEX process. One example is the timescale of aptamer selection, which typically requires an average of 12 cycles and a minimum of 2–6 months, not including initial optimization processes, validation of aptamer candidates, or structural analysis , 5. ThisSeveral groups have proposed using DNA microarrays to address the possible SELEX biases and expedite the aptamer identification process –11. DNA 6 sequences, in contrast to SELEX methods, which evolve from an initial library of ~1015 sequences. However, combinatorial drug-screening libraries successfully identify binders with only 103–106 different compounds in the starting library due to the diversity of the functional groups of the compounds -1x PBS  + 1 mM MgCl2; Rinse [R]-1/4 dilution of PBSM and nuclease free water.Buffers: Binding [PBSMTB]-1x PBS , Thrombin , BSA and HSA (Sigma), neuropeptide Y , Illustra NAP-25 desalting columns and Cy3 Mono-Reactive Dye Pack , NanoDrop (Thermo Scientific), nuclease free water (Gibco). Microarray equipment consisted of the following: custom 8 × 15 k DNA microarrays, 8 × 15 k gasket slides, ozone barrier slides, hybridization chambers, scanner cassettes, hybridization oven, and High-resolution Microarray Scanner  and slide rack and wash dishes (Shandon) and Kimtech polypropylene wipes (Kimberly-Clark). All DNA was purchased through IDT:2CO3 buffer (pH = 9.3) and incubated for 30 minutes. The product was purified with Texas Red protein labeling size exclusion column (Molecular Probes) and the dye-to-protein ratio (D/P) was calculated as 0.8 D/P using the manufacturer's instructions with UV/Vis detection.Thrombin was labeled with Cy3 using a Cy3 Mono-Reactive Dye Pack . Protein was diluted to 1 mg/mL in 1 mL 0.1 M NaT = 25°C, [Na+] = 100 mM, and [Mg2+] = 5 mM. These settings represent generalized conditions related to aptamer studies encompassing a variety of buffers and applications. Only sequences with a secondary structure that passed these selection criteria were candidates for the microarray selection experiments. Sequences were analyzed at random until 50,000 sequences were reported to fit the criteria. Five thousand out of 50,000 total sequences that fit the constraints were randomly incorporated onto the 8 × 15 k microarray chip with a 3′-T10 spacer, in duplicate or triplicate. Controls were synthesized with a minimum of 10 replicates also containing a 3′-T10 spacer.UNAFold software was used to screen DNA sequences from the patterned library generated using Perl scripts and to determine which sequences were folding according to a predefined set of criteria. Constraints were set to maximize the number of potential binders: (1) 1st base should be paired with the 50th base; (2) the total number of unpaired bases 10 < unpaired < 30; (3) there should be at least two 4-unpaired base stretches. The secondary structure of generated sequences was evaluated using the UNAFold package with the following set of parameters: μL Cy3-thrombin (100 nM) in PBSMTB was loaded onto a gasket slide then incubated with the array for 2 hrs at 20°C in a hybridization chamber/hybridization oven. Slides were quickly disassembled in water and washed for 3 min in a PBSM buffer with the slide rack and stir plate then transferred to a 50 mL conical tube with 1/4 PBSM buffer/water for 1 min using a shaker plate. Slides in the slide rack were then dipped in a 50 mL conical tube of nuclease-free water to remove any remaining salt and washed for 1 min on a shaker plate. The microarray slide was slowly withdrawn from the water to promote a drier surface; the back of the slide was wiped with ethanol and then placed in a 50 mL conical tube with a polypropylene wipe at the bottom and centrifuged at 4150 rpm for 3 min. The microarray was loaded into a scanner cassette and covered with an ozone barrier slide before scanning. The arrays were scanned using Agilent Scan Control software. Images (TIFF) were generated using 20-bit imaging at 5 μm (8 × 15 k arrays). Data was extracted using Agilent Feature Extraction software version 10.7.3.1. Mean fluorescence intensity and standard deviation of replicates were determined using code written in Perl. Statistical significance was calculated using a two-tailed t-test at 95% confidence interval for potential binders identified through the microarray compared to control sequences. Statistical significance was used as a metric for differentiating real binding events from experimental artifacts.Blocking with PBSMTB was performed on the DNA microarray loaded into the gasket for 1 h at room temperature. The slides were quickly edge-tapped to remove excess buffer. Seventy μL/min. Neutravidin  was dissolved in HyClone water then diluted 1/10 in 10 mM sodium acetate buffer (pH = 4.5) and added at 30 μL/min for 210 sec. The surface was blocked with 1 M ethanolamine for 600 sec at 5 μL/min. 3′-biotin-T10 4A018 aptamer was heated to 95°C then introduced at 5 μM in 5 mM MgSO4 for 180 sec at 30 μL/min. Samples were diluted to 27.3 μM in HEPES buffer from Biacore  and dialyzed into HEPES buffer overnight. The samples were serially diluted to appropriate concentrations then introduced onto the chip at 30 μL/min for 30 sec. Data analysis was performed with BIAevaluation software for three experiments of three replicates for each compound with the background subtracted data (reference channel 1) and plotted in GraphPad Prism 5 using a one site total binding model. Each experiment included freshly prepared reagents introduced onto the same chip in triplicate.SPR Biacore studies were carried out on a CM7  chip with neutravidin custom immobilized on the surface. The surface was activated with a mixture of 1-ethyl-3-[3-dimethylaminopropyl]carbodiimide (0.2 M) and N-hydroxysuccinimide (0.05 M) for 420 sec at 10 2O : D2O ratios of 90 : 10 and 0 : 100 at aptamer concentrations of 1 mM for 4A018 and 1.3 mM for the TBA 15 mer. The imino proton spectra were acquired with the W5 water suppression pulse sequence and 2D NOESY spectra were acquired with 1024 points in the direct dimension and 256 points in the indirect dimension with a sweep width of 10 ppm and a mixing time of 0.2 s.Proton NMR of aptamer solutions was performed on a 400 MHz Bruker Avance NMR spectrometer. DNA was dissolved in PBSTMB buffer with Hhttp://dx.doi.org/10.1155/2015/137489. The pattern PT2 was applied to the microarray for assessing thrombin binding. Several potential thrombin aptamer candidates were identified from the microarray work. The top 15 candidates were ranked by fluorescence intensity . A close-up of the fluorescence intensity values of the top 5 sequences compared to SA is also shown , and human serum albumin (HSA), as well as a compound with an isoelectric point (pI) close to that of thrombin, neuropeptide Y (NPY), were assayed for binding in addition to thrombin.To confirm the microarray results due to the low number of technical replicates on the array, the top ranked sequence, 4A018, was assessed for target binding by SPR. Two separate flow channels were immobilized with biotinylated 4A018 with a 3′-TKd) of 4.04 ± 0.31 μM in channel 3 and 3.96 ± 0.29 μM from channel 4 , while BSA, HSA, and NPY demonstrated negligible binding and did not fit the curve . These SPR results show that 4A018 has affinity for thrombin protein rather than the fluorescent tag used in the microarray experiments and that the interaction is specific to thrombin despite addition of proteins of similar molecular weight and pI. Furthermore, it demonstrates that the use of proper controls and replicates during a microarray experiment enhances the ability to differentiate a real binding event from spurious interactions in a massively parallel format.Thrombin was the only molecule that significantly interacted with 4A018 in either of the flow channels . Flow chKd of the microarray-identified sequence may also be improved under fully optimized binding conditions or by applying genetic algorithms to the lead compound over multiple microarray rounds [One reason the affinity of the microarray selected aptamer may be lower than that of the solution-based aptamers (nanomolar dissociation constants) is because solution-based SELEX is an evolutionary process designed to select the “best” binders by employing multiple rounds of increasingly high stringency conditions. Furthermore, a much smaller starting population was utilized in the microarray experiment, and constraints were imposed that may preclude identification of a sequence with a higher affinity. In addition, the SELEX generated aptamers were fully optimized by truncating primers and nonessential bases and identifying a consensus sequence. Further optimization of patterns and stringency conditions as well as mutational/truncation analysis could lead to aptamers with enhanced target affinity. The reported y rounds –11. RefiIn contrast with promoting the sequence as an ideal aptamer, this SPR binding study instead validated the overall hypothesis that a patterned library aids in identifying an aptamer despite the relatively low sequence space covered in a microarray compared to SELEX. The enhanced stringency of the microarray conditions generated a sequence with high target specificity, and a binding sequence was identified by applying the very first pattern designed with no consideration to reported thrombin binders. The binding demonstrated in the SPR experiments shows that an aptamer identified while immobilized on a microarray surface will retain binding when transitioned to a new biosensing platform requiring aptamer surface linkage. Designing a microarray with low replicates of different patterns may serve as a screening mechanism to determine optimal patterns or homologous stretches necessary for target binding. Rather than providing the highest affinity sequence, this proof-of-principle study highlights the use of a computational driven methodology to create a DNA library rather than a SELEX based approach.Previous reports have shown that the well-known TFBS and THBS thrombin aptamers fold into G-quartet structures , 22. Due syn conformations about some guanine glycosidic linkages that can be identified by NMR [G-quartets have a number of unique features, including in-plane pairing of four guanine bases, slow imino proton exchange, high thermal stability, andd by NMR . While pd by NMR , we showThe imino proton NMR spectra is very sensitive to aptamer folding and the hydrogen bonding patterns resulting from the formation of G-quartets, AT and GC base pairs, loops, and mismatched base pairs . The imiA comparison of the imino proton spectra of 4A018 at 278  and 298  syn conformation of the glycosidic angle in some of the guanine bases [ syn conformation can be detected by 2D Nuclear Overhauser Effect spectroscopy (NOESY) [ anti to the syn conformation shortens the distance between the GH8 and GH1′ protons from 3.7 to 2.1 Å. Additionally, software designed to predict oligonucleotide secondary structure is typically unreliable in ability to report the formation of higher order structures (including G-quartets) which can be confirmed by 2D NOESY.Another feature of G-quartets identifiable in the NMR spectra is thene bases . The syn (NOESY)  since th syn conformation that give rise to four strong GH8-GH1′ cross peaks enclosed in the circle in  syn conformation. This supports the hypothesis that 4A018 does not adopt a G-quartet structure, and it backs the imino proton NMR results suggesting the accuracy of the predicted structure .The 2D NOESY spectra for TFBS  and 4A01The microarray evolution leading to a thrombin aptamer by Platt similarly did not adopt a G-quartet structure . While tKd, reducing the reliance of sensor performance on affinity [ complimentary to SELEX in the sense that different types of binders could be produced depending on the desired application. Future focus areas include investigating the performance of different patterns including in-depth motif analysis of both binders and nonbinders, as well as the effects of a combined microarray/SELEX scheme.This work illustrates the potential of DNA microarray technology for aptamer identification and highlights patterned libraries designed without prior binding sequence consideration as a viable solution to the limitations on microarray oligonucleotide surface density. This method emphasizes a rational computational driven methodology to DNA library creation rather than a SELEX approach. While the results of these initial proof-of-concept studies may not currently improve upon SELEX in terms of aptamer affinity, the microarrays rapidly provide a starting point to perform additional experiments to generate higher affinity aptamers based on the identified sequences. Binding candidates can be identified and ranked in less than one week utilizing microarray experiments, presenting methodology that is more amenable to potential high throughput applications than traditional SELEX. One area of immediate impact for this methodology is directed at the biosensor field by rendering it feasible to identify a functional aptamer directly immobilized on a solid support to mitigate the variability or elimination of affinity often observed in aptamers that are selected in solution for applications that involve a surface linkage. Furthermore, aptamer based electrochemical or gold nanoparticle biosensing technologies amplify signal and detect analytes at levels orders of magnitude lower than the affinity , 30. TheThe Supplementary Material provides additional information on data and methods supporting the Results and Discussion. Initial library pattern design and analysis, sequences and secondary structures of oligonucleotides from Figure 1, analysis of sequence motifs, random oligonucleotide fluorescence intensity measurements and sequences, and surface plasmon resonance sensorgrams are all included in Supplementary Material.


Chiral segregation of enantiomers or chiral conformers of achiral molecules during self-assembly in well-ordered crystalline superstructures has fascinated chemists since Pasteur. Here we report spontaneous mirror-symmetry breaking in cubic phases formed by achiral multichain-terminated diphenyl-2,2′-bithiophenes. It was found that stochastic symmetry breaking is a general phenomenon observed in bicontinuous cubic liquid crystal phases of achiral rod-like compounds. In all compounds studied the ${{\it Im}\bar 3m}$ Design and investigation of chiral self-assembled superstructures represents a fascinating field of contemporary research which provides numerous potential applications. Beside enantiomeric mixtures of molecules, segregation of chiral supermolecular aggregates,,2,3 and Herein we report that stochastic symmetry breaking is a general phenomenon typically observed in the bicontinuous cubic phases of achiral rod-like and polycatenar (multichain-terminated) liquid crystalline (LC) compounds. We show  symmetry,21 where1, see Table 1 melt between 110 and 139 °C and form optically isotropic and highly viscous mesophases which, based on X-ray diffraction (XRD) evidence (see below), are cubic LC phases, and which on further rising temperature transform to isotropic liquids. In some cases, an additional liquid-liquid transition (LLT) is observed and the liquid phase occurring between the cubic and the “ordinary” isotropic liquid is labelled here IsoLT, the liquid phase at higher temperature as IsoHT. IsoLT phases have previously been found as intermediate phases at Cub-Iso transitions (labelled Iso1)[1[*]).[LT is denoted here as IsoLT[*]. While IsoLT[*] appears in 1 d both on heating and cooling, exotherm is broad and is better described as a heat capacity anomaly rather than a first-order transition  and have1)[1[*]). This chi cooling, in 1 b aacub=11 nm for the ${Ia\bar 3d}$acub=15–18 nm for the ${{\it Im}\bar 3m}$Cubic space groups were determined by powder small-angle XRD and grazing-incidence diffraction as shown in the SI . Two cubic phases were observed, the double-network “gyroid”, with ${Ia\bar 3d}$y Figure , and they Figure . The lat[*]/${{\it Im}\bar 3m}$Symmetry breaking is observed in some of the cubic phases by polarized microscopy if the analyzer is rotated by a small angle out of the 90 °C crossed position with respect to the polarizer. In this way the polarizing microscope can be used as a polarimeter with the additional advantage of spatial resolution. Remarkably, all ${Ia\bar 3d}$d Figure . Rotatinbar 3m}$.1 g from the Cub[*]/${{\it Im}\bar 3m}$HT phase (130–140 °C), and then cooling back to the Cub[*]/${{\it Im}\bar 3m}$HT liquid and reappearing in the Cub[*]/${{\it Im}\bar 3m}$Microbeam circular dichroism (CD) spectroscopy confirmed the presence of chiral domains in the ${{\it Im}\bar 3m}$While both, optical activity and CD, are strongly affected by helical order, CD is highly sensitive to molecular conformation. It is suggested that the chiral chromophore mainly responsible for the CD is the slightly twisted 5,5′-diphenyl-2,2′-bithiophene unit Figure  which haHT liquid to the Cub[*]/${{\it Im}\bar 3m}$1 f and 1 g, Figure LT[*] phase . In this case investigation between uncrossed polarizers give no evidence of chirality. What is more, where the ${Ia\bar 3d}$LT[*] phase (compounds 1 b and 1 d), the chirality is completely erased  and 6 (BABH-n) are members of the two best investigated homologous series of cubic phase forming compounds  whereas the ${{\it Im}\bar 3m}$2–4, 5 b, c and 6 c). For the 3′-nitro-4′-alkoxybiphenyl carboxylic acids 5 there is an additional enantiotropic (i.e. reversible) IsoHT–IsoLT transition; however in this case the IsoLT phase is for all investigated compounds achiral. Nevertheless, chirality evolves for 5 c (n=20) at the transition to the Cub[*]/${{\it Im}\bar 3m}$5 b (n=18) an achiral ${Ia\bar 3d}$T≈180 °C .[[*]/${{\it Im}\bar 3m}$HT, via IsoLT phases which can be either 2) chiral (IsoLT[*]) or 3) achiral (IsoLT), or 4) from the achiral Cub/${Ia\bar 3d}$Similar Iso S20c,d). Again, cxception, while thcognized. ChiralitFor the understanding of the development of chirality in the cubic phase it must be recalled that the molecules have liquid like local order, with no hint of any Bragg-like X-ray reflection in the wide-angle range. Therefore, the classical approach of achiral symmetry breaking, where the formation of a more or less ordered crystalline phase energetically favors one chiral conformer over another,,3,7 cannIn soft-crystal columns of short hexacatenar hydrazine rods or broadacub≈3.9 nm between the junctions, which accommodates about 9 molecular triplets spaced at 0.45 nm. As the twist between the ${Ia\bar 3d}$ϕ=±70.5°, there is an average rotation of ±8° between adjacent molecules. For details see SI. As is well known, the two networks in the ${Ia\bar 3d}$However, in the bicontinuous cubic phases we also must consider the network junctions. At the 3-fold junctions the three twisting “ribbons” that may be used to represent the network segments, must merge into one another smoothly. In an optimal junction the aromatic rods arrive parallel to each other and with a synchronized twist where the helices lock-in together as they approach each other—for the ${Ia\bar 3d}$1 e–1 g) generally have larger terminal groups than ${Ia\bar 3d}$1 a–1 d). They are thus expected to cause greater steric crowding. We concentrate here only on the middle (yellow) network, as it is the simplest of the three and, like in the ${Ia\bar 3d}$Turning to the ${{\it Im}\bar 3m}$networks,,21 a facA similar calculation can be performed on the other two networks; however because of their complexity we shall not discuss them here. The important point to make, however, is that the chirality of the middle network will undoubtedly affect the chirality of the inner and outer networks. Because of their crystallographic equivalence, the helical sense of the inner and outer (red and blue) network will be the same. Whether it will be the same or opposite of that of the middle network, we cannot tell with certainty. However, what is certain is that, in the general case, there will always be at least a residual chirality in the cubic ${{\it Im}\bar 3m}$5 and 6 have shown[6).[The above model implies that each of the cubic structures is the solution best suited to accommodate a certain range of required molecular twist angles. It is not suggested that the ${{\it Im}\bar 3m}$ave shown that as ounds 6).LT[*]-Cub transitions indicates that the germs of the achiral ${Ia\bar 3d}$[*]/${{\it Im}\bar 3m}$A closer look at the IsoIn summary, we have discovered that, in spite of appearing in achiral compounds, the ${{\it Im}\bar 3m}$ achiral. This pretury ago.,32 But t


Amicoumacin A is an antibiotic that was recently shown to target bacterial ribosomes. It affects translocation and provides an additional contact interface between the ribosomal RNA and mRNA. The binding site of amicoumacin A is formed by universally conserved nucleotides of rRNA. In this work, we showed that amicoumacin A inhibits translation in yeast and mammalian systems by affecting translation elongation. We determined the structure of the amicoumacin A complex with yeast ribosomes at a resolution of 3.1  Å. Toxicity measurement demonstrated that human cancer cell lines are more susceptible to the inhibition by this compound as compared to non-cancerous ones. This might be used as a starting point to develop amicoumacin A derivatives with clinical value. Development of small molecule translation inhibitors is needed for progress in antibacterial as well as anticancer therapy1343Thermus thermophilus ribosome as well as biochemical and genetic analysis of bacterial translation inhibition has been reported. It appeared that amicoumacin A binds a conserved site between the E-site mRNA codon and 16S rRNA. The antibiotic contacts only the RNA backbone and nucleobases of rRNA. A number of antibiotics such as pactamycin7In a recent studyThe crystal structure of bacterial ribosome in complex with amicoumacin revealed that antibiotic interacts with universally conserved nucleotides of the small subunit rRNA3in vitro translation and mRNA transfection approaches as well as a toe-printing technique to show that amicoumacin A inhibits translation in yeast and mammalian systems by affecting translation elongation. We also compared human cancerous and non-cancerous cell lines for their susceptibility for protein synthesis inhibition by the antibiotic. The structure of the amicoumacin A complex with yeast ribosomes was determined by X-ray crystallography at resolution up to 3.1 Å. While the overall binding site of amicoumacin A in eukaryotic ribosomes appeared to be the same as in bacterial ones, certain differences in the elements of the binding site may provide a framework for designing selective inhibitors on the basis of the amicoumacin A scaffold.Although the major principles of protein biosynthesis are uniform in all domains of life, the bacterial and eukaryotic translational machineries substantially differ in some particular components, including ribosome constituent elements111214Mammalian mRNAs are known to utilize a wide spectrum of translation initiation pathways14Renilla luciferase (Rluc) or firefly luciferase (Fluc). The former contained the 5′-untranslated region (5′UTR) of the human β-actin mRNA and were m7G-capped. Thus, its translation should reflect a behaviour of a regular cellular mRNA. In contrast, each of the Fluc encoding transcripts harboured one of three well-characterized viral IRESs in their 5′UTR. We used IRES elements that differ in translation initiation mechanisms and have different initiation factor (eIF) requirements. Encephalomyocarditis (EMCV) IRES requires the same set of eIFs as the Actin-Rluc, except for the cap-binding protein eIF4E; it places the 40S ribosome subunit into its internal region, so there is only a limited scanning event across a few nucleotide-long initiation window in this casevia eIF2-independent pathwaysi at all, since it utilizes the most exotic initiation mechanism everWe prepared polyadenylated transcripts that encoded either We transfected the reporter mRNA constructs into cultured HEK293T cells and measured the luciferase activity after 2 hours of expression. Increasing concentrations of Amicoumacin A were added to the culture medium just before the transfection. The short incubation period allowed us to minimize secondary effects that could appear if prolonged incubations with the drug were used. Translation of both scanning- and IRES-dependent mRNAs were inhibited by amicoumacin A in a micromolar concentration range . Almost in vitro translation experiment in the mammalian cell-free system that closely recapitulates in vivo conditionsThe difference we observed could be explained by either an additional inhibitory action of the antibiotic on the scanning process or by some indirect effects of the drug. It is long time known that some protein synthesis inhibitors cause ribotoxic stress response in mammalian cells, including eIF2 phosphorylation and partial inactivation20To dissect this issue unambiguously, we monitored ribosome progression along the capped β-globin mRNA by toe-printing in rabbit reticulocyte lysateAnother eukaryotic system that is widely used for analysis of translation inhibitors is budding yeast. We monitored yeast culture growth in the presence of increasing amicoumacin A concentrations. However, we did not observe substantial growth inhibition up to the highest antibiotic concentration we used . We hypoin vitro experiment in yeast cell-free system (19cI-Fluc) or a 5′ UTR from the natural yeast GCN4 gene (GCN4-Fluc) were used. Both mRNAs initiate translation by canonical scanning mechanism, although the latter one bears four uORFs in their leader and is a subject of a eIF2-P-mediated translational control19cI-Fluc. This result indicates that cell integrity protects yeast ribosomes from amicoumacin A inhibition in vivo, while the ribosomes themselves are highly susceptible to inhibition.To exclude any effect not related to translation, we performed an e system . Two lucSaccharomyces cerevisiae in complex with translation inhibitorsobs − Fcalc) was calculated using the model of vacant 80S yeast ribosome (PDB entry 4V88). The amicoumacin A molecule was located in the E-site of small ribosomal subunit after manual inspection of the peaks of positive electron density . The structure of 80S-amicoumacin A complex was refined with Phenix.refineThe ability of amicoumacin A to inhibit yeast ribosomes prompted us to use this system for structural study. For this, we applied the procedure to determine the structures of 80S ribosome from  density . The atoAmicoumacin A interacts mostly with rRNA residues in helixes 23 and 24 . It is iThermus thermophilus in complex with amicoumacin A reported previouslyWe compared the structure of amicoumacin A bound to the yeast ribosome to the structure of the bacterial ribosome from Thermus thermophilus 70S ribosome, while in the yeast 80S ribosome the terminal leucine of uS11 forms a hydrophobic interaction with the isobutyl group of amicoumacin A. The mutations in the C-terminus of uS11 are known to confer resistance to cryptopleurine and emetine2624in vitro. Interestingly, cryptopleurine and emetine are active only in eukaryotes, while pactamycin is a universal inhibitor.The differences in the binding site of amicoumacin A in bacterial and yeast ribosomes50) roughly matched that of inhibition of reporter mRNA translation . Since this loop has a special importance in the process of start codon selection during translation initiationA conserved loop of the ribosomal protein uS7 is located 3.9 Å from the isocoumarin part of amicoumacin A in the bacterial ribosome and is more than 10 Å away in the yeast 80S structure . Large dAnother intriguing possibility is using the amicoumacin scaffold for designing mRNA-specific translation inhibitors. For example, the HCV IRES domain II binds in close proximity35In summary, we presented evidence for amicoumacin A activity toward the eukaryotic ribosome in both mammalian and yeast systems, we revealed structural details of its interaction with the yeast 80S ribosome, and we showed a relative selectivity of the drug toward human cancer cell lines. Our study could be used for rational drug design aiming to improve amicoumacin A therapeutic potential.Amicoumacin A isolation was described earlierin vitro transcription, pbG was linearized at the EcoRI site. For synthesis of the polyadenylated mRNAs encoding the firefly and Renilla luciferases, a 50T-tailed PCR product was used as a template, as described previously2238The plasmids pbG coded for the rabbit β-globin mRNA2, as indicated. The mixture was incubated for 5 min at 30 °C, then 1 μl of mRNA solution (0.5 pmol/μl) was added followed by incubation for an additional 10 min. After that, 10 μl of RT Mix (including [32P]-labeled primer TCACCACCAACTTCTTCCAC) was added, according toRibosomal complex assembly was performed in the nuclease treated RRL , as described previously2 box and to avoid cooling the plate. Two hours after the transfection, cells were harvested, and luciferase activities were analyzed with the Dual Luciferase Assay kit (Promega). All the transfections were repeated at least three times in different cell passages.HEK293T cells were cultured and transferred into 24-well plates 12–16 h before transfection, as describedMATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) yeast strain was grown in a YPD medium  to exponential phase and inoculated into liquid YPD at OD600 of 0.05. Growth rates (OD600) were measured every 5 min in a 24-well plate in the Infinite 200 PRO microplate reader  at 30 °C with continuous shaking.The BY4741 , 2 u of RiboLock RNase inhibitor (Thermo Scientific), 0.5 mM D-luciferin, 0.25 pmol mRNA, and 1 μl of amicoumacin A solution, as indicated. Translation reactions in the yeast system were performed in a total volume of 15 μl, containing 7.5 μl of the extract, translation buffer 2, 12 mM creatine phosphate, 1 mM ATP, 0.4 mM GTP, 126 mM KOAc, and 50 μM of each amino acid), 3 u of RiboLock RNase inhibitor, 50 μg/ml creatine phosphokinase, 1 mM D-luciferin, 0.12 pmol mRNA, and 1 μl of amicoumacin A solution, as indicated. Translation mixtures were incubated in a white 384-well plate  and covered by a PCR plate seal at 30 °C  or 25 °C (for the yeast one) in the TECAN reader with continuous measurement of the luciferase activity (integration time 3 s). Light intensities at 25 min were taken as luciferase activity values.Translation experiments in the mammalian system were performed in a total volume of 10 μl, which contained 5 μl of the S30 extract, translation buffer S. cerevisiae were purified and crystallized, as previously described80S ribosomes from the yeast http://grade.globalphasing.org) using SMILES strings from the PubChem databasehttp://pymol.org/). Ribosomal proteins were named throughout the manuscript according to the newly established nomenclaturehttp://pdb.org/pdb/home/home.do) under accession code 5I4L.The structure was solved by rigid body refinement of the deposited 80S ribosome structure (PDB 4V88) using Phenix.refine2 for 18 h before treatment. Then we added 15 μl of the drug  and incubated the cells for additional 72 h. Amicoumacin A in final concentrations of 50 nM–100 μM (eight dilutions), in triplicate, was applied. 2 nM–6 μM doxorubicin was used as a positive control. At the end of the incubation we added MTT into the media (up to 0.5 mg/ml), incubated the cells for 2 h, followed by removing the media and addition of 100 μl DMSO. The amount of MTT reduced by cells to its blue formazan derivative was measured spectrophotometrically at 565 nM using a plate reader and normalized to the values for cells treated with the media/DMSO only. IC50 was calculated with “GraphPad Prism 5” software .Cytotoxicity was assessed using the MTT 2,5-diphenyl tetrazolium bromide) assay based on the method described by Ferrari and co-workersHow to cite this article: Prokhorova, I. V. et al. Amicoumacin A induces cancer cell death by targeting the eukaryotic ribosome. Sci. Rep. 6, 27720; doi: 10.1038/srep27720 (2016).


Litoria Aurea. It is a surface-acting membrane disrupting peptide that permeabilizes bacterial membranes via the carpet mechanism; the molecular details of this process are mostly unknown. Here the mechanism of action of Aurein 1.2 was investigated with an emphasis on the role of membrane charge and C-terminal amidation of the peptide. Using quartz crystal microbalance (QCM) fingerprinting it was found that the membrane charge correlates with membrane affinity of the peptide, however the binding and the membrane disrupting processes are not charge driven; increased membrane charge reduces the membrane disrupting activity. Coarse grain simulations revealed that phenylalanine residues act as membrane anchors. Accordingly Aurein 1.2 has the ability to bind to any membrane. Furthermore, bundling precludes membrane disruption in case of wild type peptides, while non C-terminal amidated peptides form random aggregates leading to detachment from the membrane. Hence C-terminal amidation is crucial for Aurein 1.2 action. Our results suggest that Aurein 1.2 acts via aggregation driven membrane penetration. The concomitant change in the tension of the outer leaflet imposes a spontaneous curvature on the membrane, leading to disintegration.Aurein 1.2 is a 13 residue antimicrobial peptide secreted by the Australian tree frog  AMPs are usually assigned into two main categories based on their mode of membrane disruption: surface acting peptides and transmembrane pore formers. The distinction is largely empirical with a weak correlation to the size of the peptides. AMPs greater than 20 amino acid residues are long enough to span a lipid membrane in an α-helical conformation11The specificity of AMPs to pathogens is frequently explained with their positive charge192324Structural factors also play a role in the activity of antimicrobial peptides2931332) secreted by the Australian green and golden bell frog Litoria aurea and by the related Australian southern bell frog Litoria reinforms3812Aurein 1.2 is a 13-amino acid peptide  with supported phospholipid bilayers of DMPC, DMPC/DMPG (4:1), DMPC/Cholesterol (9:1), and DMPC/DMPG (3:2) compositions, respectively, were recorded using QCM. In a typical experiment  after obf. Increasing the concentration further to the 7 and 10 μM resulted in a similar but more pronounced trend with a frequency increase to values above the initial f. At 15 μM this trend is followed by a steep frequency drop. The initial decrease in f that is observed at all concentrations is consistent with peptide binding to the membrane surface. Above 5 μM concentration, the initial negative trend is followed by a small increase in frequency that is followed by a second, slow negative trend for all concentrations, exhibiting a “wave” pattern that accelerates over time at 15 μM.f. Increasing the concentration further to 10 μM enhances the second process. Upon increasing concentration to 15 μM a third process, a substantial negative frequency change, is observed, similar to the pattern seen for neat DMPC with 15 μM Aurein 1.2.Addition of 10% cholesterol to the DMPC membrane  changed f after the initial binding. At 10 μM concentration a third trend also becomes visible: an additional negative change to produce a “wave” pattern. This result is consistent with earlier QCM work on gel phase membranes29Addition of 20% DMPG to the DMPC membrane  resultedCationic peptides have shown a stronger binding affinity to anionic lipids815To gain better insight into the effect of C-terminal amidation on the mechanism of action of Aurein 1.2 on membranes, Aurein 1.2-COOH was also studied . In all f). Such fingerprints of a mechanism are used primarily to identify stages or sub-processes that exhibit different viscoelastic character. The trendline  reveals the formation of a thin viscous layer, that is, mass uptake; conversely a trendline towards  usually indicates mass loss29qualitatively assigned if informed by other experimental methods, computational results or by assessing possible mechanistic pathways.The differences in the membrane interactions of Aurein 1.2 and Aurein1.2-COOH can be analyzed through changes in the membrane viscoelasticity. Hence the mechanism of action of these peptides was further explored by using the mechanistic “fingerprints” formed when plotting the QCM dissipation change against the frequency change  at 15 μM whereas for both DMPG containing mixtures it is at 10 μM. However, in case of 40% DMPG content the trend line is weakly , the opposite of the trend observed for all other mixtures (where it is a nearly pure negative frequency change).The near zero dissipation change that accompanies the large negative frequency changes suggests that a tightly coupled rigid layer is forming on the surface. Apparent mass gain without any change in dissipation is an unexpected feature since previous studies suggest membrane dissolution which would result in continuing  mass removal to frequency change values above zero that would indicate the removal of the membrane2943Single-peptide simulations show that both amidated and non-amidated Aurein 1.2 spontaneously bind to a DMPC lipid membrane. A movie of a representative simulation is available as via hydrophobic interactions (mostly mediated by isoleucine), resulting in a bundle. The aggregated structures remain anchored to the membrane through phenylalanines as described above . This implies that cholesterol hinders membrane binding and shifts the peptide activity towards higher concentrations, consistent with experimental observations. Charged lipids had the opposite effect. 20% DMPG lead to strong binding with the same geometry as in case of neat DMPC; bundling of the peptides was also observed on the surface . ImportaThese observations provide microscopic insight into the initial stages of the interaction between Aurein 1.2 and the membrane. Full membrane disruption has not been observed in any case, as it is beyond the time scales accessible with these simulations.It was found that Aurein 1.2 binds to both zwitterionic and negatively charged membranes. However, more peptide bind to the charged lipid and increasing anionic lipid content increases the amount of membrane bound peptide. Initial interaction of cationic AMPs with anionic membrane is generally considered to be purely electrostatic, forming the basis of selectivity to bacteria38The interaction of Aurein 1.2 with DPMC/Cholesterol shows weaker activity than with neat DMPC. Since mammalian cell membranes contain cholesterol whereas bacteria do not, this difference reveals a secondary motif for specificity and selectivity. It has been shown that cholesterol attenuates the insertion of AMPs into membranesThe comparison of ΔD vs Δf plots for neat DMPC exposed to wild type Aurein 1.2 and Aurein 1.2 -COOH is shown in et al.13943Rozek Our simulation results have confirmed the membrane anchor role of phenylalanine residues at 3 and 13 positions that place the amphiphilic helix at the boundary between the headgroup zone and the membrane core. Aurein 1.2 is significantly more active than Aurein 1.1 that lacks Phe13 residue, suggesting that the Phe3 and Phe13 play an important role in the membrane disrupting activityOf the carpet action of Aurein 1.2, it is known that the peptide binds to both zwitterionic and anionic membranes43The existence of the threshold suggests that the peptide does not act as a detergentHence it is feasible to assume the following mechanism of action: i) Linear peptide binds to the membrane, charge is involved in targeting ; ii) for2PO4) and Potassium hydrogen phosphate (K2HPO4) were purchased from Fluka at ACS grade. Sodium chloride (NaCl) was purchased from Merck. Propan-2-ol, hydrogen peroxide (H2O2), and ammonia solution  all were purchased from Sigma-Aldrich . 3-mercaptopropionic acid (MPA)  was purchased from Fluka, Bio- Chimia (Switzerland).Chloroform  was purchased from Sigma-Aldrich . Methanol was purchased from Sigma Aldrich  and ethanol was purchased from Sigma Aldrich . Aurein 1.2 and Aurein 1.2 –COOH, 95% purity, were purchased from GLBiochem (China).Potassium dihydrogen phosphate .1,2-dimyristoyl-sn-glycero-3-phosphocholine(DMPC), sodium salt of 1,2-dimyristoyl-sn-glycero-3-phosphoglycerol (DMPG) and cholesterol were purchased from Avanti Polar Lipids . Lipids were dissolved in chloroform; in case of DMPG 3%methanol was added to improve solubility. Desired ratios of lipids: neat DMPC, DMPC/DMPG (4:1), (3:2), and DMPC/cholesterol (9:1) were measured into test tubes. The solvent was evaporated under a gentle stream of N2 gas, after that placed into a 3:1:1 mixture of ultrapure water 18.2 MΩ , hydrogen peroxide (30% solution) and ammonium hydroxide (28–30%solution) for 20 min at 70 °C. Then chips were rinsed with ultrapure water and dried. A clean gold surface was treated with 2% (w/w) MPA in Propan-2-ol overnight to form a self-assembled monolayer as a support for the biomimetic membrane29.Quartz Crystal Microbalance “with Dissipation Monitoring” (QCM) measurements were performed with a Q-SENSE E4 system . The sensor crystal used were 5 MHz, AT-cut, polished chips with evaporated gold sensor surface. The change of resonance frequency (Δf) and energy dissipation (ΔD) upon mass deposition were measured simultaneously at four different overtones of the fundamental frequency . The (Δf) and (ΔD) values of the seventh overtone were presented. Sensor chips were rinsed with ethanol and dried under gentle stream of N−6 dissipation change was confirmedPeptides were injected into the QCM chamber once the formation of the single bilayer: 13–15 Hz frequency and 2.5–3.0 × 102 obey the Sauerbrey conditions; membranes deposited to MPA modified surface do not as the membrane is partially suspendedet al. would cause further negative frequency change; in the same article changes in the  direction were also interpreted as membrane removalΔf-ΔD plot can be used to provide an interaction fingerprint that is successfully used for the study of AMP-membrane interactions29qualitatively assigned if informed by other experimental methods, computational results or by assessing possible mechanistic pathways. Importantly, Δf-ΔD fingerprints cannot be directly interpreted for the mechanism of interaction unless independent information is available about the interacting system. A full description of Δf-ΔD plot interpretation can be found in Refs. In trendlines where ΔD is inversely proportional to –Δf,  and , the change predominantly occurs in viscoelastic properties, that is, distribution, density or mixing of the constituents of the deposit. These viscoelastic processes can be Molecular dynamics simulations were carried out using the GROMACS package (vers. 4.6)All simulated systems consisted in a 10 × 10 × 8 nm simulation box that included a lipid bilayer and one or more peptides. In all cases the peptides started in the solvent and were allowed to move freely throughout the system. Therefore, neither binding nor aggregation was forced by the original setup.The role of C-terminal amidation in binding was explored using one peptide (either amidated or non amidated) per simulation box and a lipid membrane composed of neat DMPC. The effect of the lipid composition in aggregation and binding was assessed in a different set of simulations where six peptides were placed in the simulation box. Both amidated and non amidated peptides were tested against a neat DMPC bilayer to provide further evidence about the role of amidation in the full process. For the other membranes  and DMPC:DMPG (3:2)) only wild type peptides were simulated.Peptide, membrane and solvent were coupled separately to a thermal bath at 300 K Berendsen thermostat; a Parrinello-Rahman semi isotropic barostat at 1 bar was also included. The standard MARTINI scheme was followed to complete the system setupHow to cite this article: Shahmiri, M. et al. Controls and constrains of the membrane disrupting action of Aurein 1.2. Sci. Rep. 5, 16378; doi: 10.1038/srep16378 (2015).