Virulence signatures: microarray-based approaches to discovery and analysis

Rapid, accurate, and sensitive detection of biothreat agents requires a broad-spectrum assay capable of discriminating between closely related microbial or viral pathogens. Moreover, in cases where a biological agent release has been identified, forensic analysis demands detailed genetic signature data for accurate strain identification and attribution. To date, nucleic acid sequences have provided the most robust and phylogentically illuminating signature information. Nucleic acid signature sequences are not often linked to genomic or extrachromosomal determinants of virulence, a link that would further facilitate discrimination between pathogens and closely related species. Inextricably coupling genetic determinants of virulence with highly informative nucleic acid signatures would provide a robust means of identifying human, livestock, and agricultural pathogens. By means of example, we present here an overview of two general applications of microarray-based methods for: (1) the identification of candidate virulence factors; and (2) the analysis of genetic polymorphisms that are coupled to Bacillus anthracis virulence factors using an accurate, low cost solid-phase mini-sequencing assay. We show that microarray-based analysis of gene expression can identify potential virulence associated genes for use as candidate signature targets, and, further, that microarray-based single nucleotide polymorphism assays provide a robust platform for the detection and identification of signature sequences in a manner independent of the genetic background in which the signature is embedded. We discuss the strategy as a general approach or pipeline for the discovery of virulence-linked nucleic acid signatures for biothreat agents.     

DNA methylation analysis by pyrosequencing

Pyrosequencing is a sequencing-by-synthesis method that quantitatively monitors the real-time incorporation of nucleotides through the enzymatic conversion of released pyrophosphate into a proportional light signal. Quantitative measures are of special importance for DNA methylation analysis in various developmental and pathological situations. Analysis of DNA methylation patterns by pyrosequencing combines a simple reaction protocol with reproducible and accurate measures of the degree of methylation at several CpGs in close proximity with high quantitative resolution. After bisulfite treatment and PCR, the degree of each methylation at each CpG position in a sequence is determined from the ratio of T and C. The process of purification and sequencing can be repeated for the same template to analyze other CpGs in the same amplification product. Quantitative epigenotypes are obtained using this protocol in approximately 4 h for up to 96 DNA samples when bisulfite-treated DNA is already available as the starting material.     

DNA methylation analysis by MethyLight technology

MethyLight is a sensitive, fluorescence-based real-time PCR technique that is capable of quantitating DNA methylation at a particular locus by using DNA oligonucleotides that anneal differentially to bisulfite-converted DNA according to the methylation status in the original genomic DNA. The use of three oligonucleotides (forward and reverse primers, and interpositioned probe) in MethyLight, any one or more of which can be used for methylation discrimination, allows for a high degree of specificity, sensitivity, and flexibility in methylation detection.     

Profiling DNA methylation by melting analysis

The idea of modifying DNA with bisulfite has paved the way for a variety of polymerase chain reaction (PCR) methods for accurately mapping 5-methylcytosine at specific genes. Bisulfite selectively deaminates cytosine to uracil under conditions where 5-methylcytosine remains unreacted. Following conventional PCR amplification of bisulfite-treated DNA, original cytosines appear as thymine while 5-methylcytosines appear as cytosine. Because the relative thermostability of a DNA duplex increases with increasing content of G:C base pairs, PCR products originating from DNA templates with different contents of 5-methylcytosine differ in melting temperature, i.e., the temperature required to convert the double helix into random coils. We describe two methods that resolve differentially methylated DNA sequences on the basis of differences in melting temperature. The first method integrates PCR amplification of bisulfite-treated DNA and subsequent melting analysis by using a thermal cycler coupled with a fluorometer. By including in the reaction a PCR-compatible, fluorescent dye that specifically binds to double-stranded DNA, the melting properties of the PCR product can be examined directly in the PCR tube by continuous fluorescence monitoring during a temperature transition. The second method relies on resolution of alleles with different 5-methylcytosine contents by analysis of PCR products in a polyacrylamide gel containing a gradient of chemical denaturants. Optimal resolution of differences in melting temperature is achieved by a special design of PCR primers. Both methods allow resolution of "heterogeneous" methylation, i.e., the situation where the content and distribution of 5-methylcytosine in a target gene differ between different molecules in the same sample.     

Classifying antibodies using flow cytometry data: class prediction and class discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.     

Tobacco-smoking-related differential DNA methylation: 27K discovery and replication

Tobacco smoking is responsible for substantial morbidity and mortality worldwide, in particular through cardiovascular, pulmonary, and malignant pathology. CpG methylation might plausibly play a role in a variety of smoking-related phenomena, as suggested by candidate gene promoter or global methylation studies. Arrays allowing hypothesis-free searches on a scale resembling genome-wide studies of SNPs have become available only very recently. Methylation extents in peripheral-blood DNA were assessed at 27,578 sites in more than 14,000 gene promoter regions in 177 current smokers, former smokers, and those who had never smoked, with the use of the Illumina HumanMethylation 27K BeadChip. This revealed a single locus, cg03636183, located in F2RL3, with genome-wide significance for lower methylation in smokers (p = 2.68 × 10(-31)). This was similarly significant in 316 independent replication samples analyzed by mass spectrometry and Sequenom EpiTyper (p = 6.33 × 10(-34)). Our results, which were based on a rigorous replication approach, show that the gene coding for a potential drug target of cardiovascular importance features altered methylation patterns in smokers. To date, this gene had not attracted attention in the literature on smoking.     

Phylogenetic and DNA methylation analysis reveal novel regions of variable methylation in the mouse IAP class of transposons

Background

Select retrotransposons in the long terminal repeat (LTR) class exhibit interindividual variation in DNA methylation that is altered by developmental environmental exposures. Yet, neither the full extent of variability at these “metastable epialleles,” nor the phylogenetic relationship underlying variable elements is well understood. The murine metastable epialleles, A^vy and Cabp^IAP, result from independent insertions of an intracisternal A particle (IAP) mobile element, and exhibit remarkably similar sequence identity (98.5%).

Results

Utilizing the C57BL/6 genome we identified 10802 IAP LTRs overall and a subset of 1388 in a family that includes A^vy and Cabp^IAP. Phylogenetic analysis revealed two duplication and divergence events subdividing this family into three clades. To characterize interindividual variation across clades, liver DNA from 17 isogenic mice was subjected to combined bisulfite and restriction analysis (CoBRA) for 21 separate LTR transposons (7 per clade). The lowest and highest mean methylation values were 59% and 88% respectively, while methylation levels at individual LTRs varied widely, ranging from 9% to 34%. The clade with the most conserved elements had significantly higher mean methylation across LTRs than either of the two diverged clades (p?=?0.040 and p?=?0.017). Within each mouse, average methylation across all LTRs was not significantly different (71%-74%, p?>?0.99).

Conclusions

Combined phylogenetic and DNA methylation analysis allows for the identification of novel regions of variable methylation. This approach increases the number of known metastable epialleles in the mouse, which can serve as biomarkers for environmental modifications to the epigenome.     

A DNA methylation microarray-based study identifies ERG as a gene commonly methylated in prostate cancer

DNA methylation of promoter regions is a common event in prostate cancer, one of the most common cancers in men worldwide. Because prior reports demonstrating that DNA methylation is important in prostate cancer studied a limited number of genes, we systematically quantified the DNA methylation status of 1505 CpG dinucleotides for 807 genes in 78 paraffin-embedded prostate cancer samples and three normal prostate samples. The ERG gene, commonly repressed in prostate cells in the absence of an oncogenic fusion to the TMPRSS2 gene, was one of the most commonly methylated genes, occurring in 74% of prostate cancer specimens. In an independent group of patient samples, we confirmed that ERG DNA methylation was common, occurring in 57% of specimens, and cancer-specific. The ERG promoter is marked by repressive chromatin marks mediated by polycomb proteins in both normal prostate cells and prostate cancer cells, which may explain ERG’s predisposition to DNA methylation and the fact that tumors with ERG DNA methylation were more methylated, in general. These results demonstrate that bead arrays offer a high-throughput method to discover novel genes with promoter DNA methylation such as ERG, whose measurement may improve our ability to more accurately detect prostate cancer.     

A DNA methylation microarray-based study identifies ERG as a gene commonly methylated in prostate cancer

DNA methylation of promoter regions is a common event in prostate cancer, one of the most common cancers in men worldwide. Because prior reports demonstrating that DNA methylation is important in prostate cancer studied a limited number of genes, we systematically quantified the DNA methylation status of 1505 CpG dinucleotides for 807 genes in 78 paraffin-embedded prostate cancer samples and three normal prostate samples. The ERG gene, commonly repressed in prostate cells in the absence of an oncogenic fusion to the TMPRSS2 gene, was one of the most commonly methylated genes, occurring in 74% of prostate cancer specimens. In an independent group of patient samples, we confirmed that ERG DNA methylation was common, occurring in 57% of specimens, and cancer-specific. The ERG promoter is marked by repressive chromatin marks mediated by polycomb proteins in both normal prostate cells and prostate cancer cells, which may explain ERG's predisposition to DNA methylation and the fact that tumors with ERG DNA methylation were more methylated, in general. These results demonstrate that bead arrays offer a high-throughput method to discover novel genes with promoter DNA methylation such as ERG, whose measurement may improve our ability to more accurately detect prostate cancer.     

DNA甲基化分析方法

DNA甲基化是表观遗传学的重要研究内容之一.甲基化分析的方法多且研究难度大,各种方法都有其一定的优势和不足.本文综述了基因组DNA甲基化和特定DNA片段甲基化状态分析方法新进展,为研究者提供参考.     

DNA methylation: bisulphite modification and analysis

DNA methylation is an important epigenetic modification of DNA in mammalian genomes. DNA methylation patterns are established early in development, modulated during tissue-specific differentiation and disrupted in many disease states, including cancer. To understand further the biological functions of these changes, accurate and reproducible methods are required to fully analyze the DNA methylation sequence. Here, we describe the 'gold-standard' bisulphite conversion protocol that can be used to re-sequence DNA from mammalian cells in order to determine and quantify the methylation state of a gene or genomic region at single-nucleotide resolution. The process of bisulphite treatment exploits the different sensitivities of cytosine and 5-methylcytosine (5-MeC) to deamination by bisulphite under acidic conditions--in which cytosine undergoes conversion to uracil, whereas 5-MeC remains unreactive. Bisulphite conversion of DNA, in either single tubes or in a 96-well format, can be performed in a minimum of 8 h and a maximum of 18 h, depending on the amount and quality of starting DNA.     

DNA methylation analysis by digital bisulfite genomic sequencing and digital MethyLight

Alterations in cytosine-5 DNA methylation are frequently observed in most types of human cancer. Although assays utilizing PCR amplification of bisulfite-converted DNA are widely employed to analyze these DNA methylation alterations, they are generally limited in throughput capacity, detection sensitivity, and or resolution. Digital PCR, in which a DNA sample is analyzed in distributive fashion over multiple reaction chambers, allows for enumeration of discrete template DNA molecules, as well as sequestration of non-specific primer annealing templates into negative chambers, thereby increasing the signal-to-noise ratio in positive chambers. Here, we have applied digital PCR technology to bisulfite-converted DNA for single-molecule high-resolution DNA methylation analysis and for increased sensitivity DNA methylation detection. We developed digital bisulfite genomic DNA sequencing to efficiently determine single-basepair DNA methylation patterns on single-molecule DNA templates without an interim cloning step. We also developed digital MethyLight, which surpasses traditional MethyLight in detection sensitivity and quantitative accuracy for low quantities of DNA. Using digital MethyLight, we identified single-molecule, cancer-specific DNA hypermethylation events in the CpG islands of RUNX3, CLDN5 and FOXE1 present in plasma samples from breast cancer patients.     

Novel DNA methylation marker discovery by assumption‐free genome‐wide association analysis of cognitive function in twins

Privileged by rapid increase in available epigenomic data, epigenome‐wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption‐free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e‐04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region‐based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.     

Genome-wide analysis of DNA methylation patterns

Cytosine methylation is the most common covalent modification of DNA in eukaryotes. DNA methylation has an important role in many aspects of biology, including development and disease. Methylation can be detected using bisulfite conversion, methylation-sensitive restriction enzymes, methyl-binding proteins and anti-methylcytosine antibodies. Combining these techniques with DNA microarrays and high-throughput sequencing has made the mapping of DNA methylation feasible on a genome-wide scale. Here we discuss recent developments and future directions for identifying and mapping methylation, in an effort to help colleagues to identify the approaches that best serve their research interests.     

MSRE-PCR for analysis of gene-specific DNA methylation

Abnormal DNA methylation is observed in certain promoters of neoplastic cells, although the likelihood of methylation for each individual promoter varies. Simultaneous analysis of many promoters in the same sample can allow use of statistical methods for identification of neoplasia. Here we describe an assay for such analysis, based on digestion of genomic DNA with methylation-sensitive restriction enzyme and multiplexed PCR with gene-specific primers (MSRE-PCR). MSRE-PCR includes extensive digestion of genomic DNA (uncut fragments cannot be identified by PCR), can be applied to dilute samples (<1 pg/μl), requires limited amount of starting material (42 pg or genomic equivalent of seven cells) and can identify methylation in a heterogeneous mix containing <2% of cells with methylated fragments. When applied to 53 promoters of breast cancer cell lines MCF-7, MDA-MB-231 and T47D, MSRE-PCR correctly identified the methylation status of genes analyzed by other techniques. For selected genes results of MSRE-PCR were confirmed by methylation-specific PCR and bisulfite sequencing. The assay can be configured for any number of desired targets in any user-defined set of genes.     

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.