Identification and validation of colorectal neoplasia-specific methylation markers for accurate classification of disease.

Aberrant DNA methylation occurs early in oncogenesis, is stable, and can be assayed in tissues and body fluids. Therefore, genes with aberrant methylation can provide clues for understanding tumor pathways and are attractive candidates for detection of early neoplastic events. Identification of sequences that optimally discriminate cancer from other diseased and healthy tissues is needed to advance both approaches. Using well-characterized specimens, genome-wide methylation techniques were used to identify candidate markers specific for colorectal neoplasia. To further validate 30 of these candidates from genome-wide analysis and 13 literature-derived genes, including genes involved in cancer and others with unknown functions, a high-throughput methylation-specific oligonucleotide microarray was used. The arrays were probed with bisulfite-converted DNA from 89 colorectal adenocarcinomas, 55 colorectal polyps, 31 inflammatory bowel disease, 115 extracolonic cancers, and 67 healthy tissues. The 20 most discriminating markers were highly methylated in colorectal neoplasia (area under the receiver operating characteristic curve > 0.8; P < 0.0001). Normal epithelium and extracolonic cancers revealed significantly lower methylation. Real-time PCR assays developed for 11 markers were tested on an independent set of 149 samples from colorectal adenocarcinomas, other diseases, and healthy tissues. Microarray results could be reproduced for 10 of 11 marker assays, including eight of the most discriminating markers (area under the receiver operating characteristic curve > 0.72; P < 0.009). The markers with high specificity for colorectal cancer have potential as blood-based screening markers whereas markers that are specific for multiple cancers could potentially be used as prognostic indicators, as biomarkers for therapeutic response monitoring or other diagnostic applications, compelling further investigation into their use in clinical testing and overall roles in tumorigenesis.

Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates.

MOTIVATION: Methylation of cytosines in DNA plays an important role in the regulation of gene expression, and the analysis of methylation patterns is fundamental for the understanding of cell differentiation, aging processes, diseases and cancer development. Such analysis has been limited, because technologies for detailed and efficient high-throughput studies have not been available. We have developed a novel quantitative methylation analysis algorithm and workflow based on direct DNA sequencing of PCR products from bisulfite-treated DNA with high-throughput sequencing machines. This technology is a prerequisite for success of the Human Epigenome Project, the first large genome-wide sequencing study for DNA methylation in many different tissues. Methylation in tissue samples which are compositions of different cells is a quantitative information represented by cytosine/thymine proportions after bisulfite conversion of unmethylated cytosines to uracil and PCR. Calculation of quantitative methylation information from base proportions represented by different dye signals in four-dye sequencing trace files needs a specific algorithm handling imbalanced and overscaled signals, incomplete conversion, quality problems and basecaller artifacts. RESULTS: The algorithm we developed has several key properties: it analyzes trace files from PCR products of bisulfite-treated DNA sequenced directly on ABI machines; it yields quantitative methylation measurements for individual cytosine positions after alignment with genomic reference sequences, signal normalization and estimation of effectiveness of bisulfite treatment; it works in a fully automated pipeline including data quality monitoring; it is efficient and avoids the usual cost of multiple sequencing runs on subclones to estimate DNA methylation. The power of our new algorithm is demonstrated with data from two test systems based on mixtures with known base compositions and defined methylation. In addition, the applicability is proven by identifying CpGs that are differentially methylated in real tissue samples.

Effect of copper on growth of an aquatic macrophyte, Elodea canadensis.

Elodea canadensis has been proposed as a potential biomonitor due to its wide distribution and apparent ability to accumulate pollutants in aquatic ecosystems. We investigated the effects of copper sulfate on growth in E. canadensis to determine its effectiveness as a biomonitor of copper pollution in aquatic systems and whether growth is a suitable index of sub-lethal stress. Copper sulfate significantly slowed or stopped growth at all concentrations (low: 1 ppm, medium: 5 ppm, high: 10 ppm of copper sulfate) used. Final plant drymass was significantly lower in medium and high copper treatments compared with controls. E. canadensis appears to be very sensitive to copper levels, and may be useful as a biomonitor of copper levels in aquatic systems. However, its utility as a bioaccumulator may be limited, because we observed senescence of most leaves in all copper-treated plants following 4 weeks of treatment.

Statistical process control for large scale microarray experiments.

MOTIVATION: Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible. RESULTS: We introduce a novel method for microarray process control that estimates quality based solely on the distribution of the actual measurements without requiring repeated experiments. A robust version of principle component analysis detects single outlier microarrays and thereby enables the use of techniques from multivariate statistical process control. In particular, the T(2) control chart reliably tracks undesired changes in process relevant parameters. This can be used to improve the microarray process itself, limits necessary repetitions to only affected samples and therefore maintains quality in a cost effective way. We prove the power of the approach on 3 large sets of DNA methylation microarray data.

Rapid adaptation to internal states as a coding strategy in visual cortex?

Adaptation is a prominent feature of biological neuronal systems. A common interpretation of adaptation in terms of function is that it provides flexibility for a neuronal system to perform well under varying external conditions, for example by adjusting the input/output relation of a sensory system with reference to the ensemble of stimuli the organism currently perceives. This interpretation, however, only applies if the time-scale of adaptation is slower than the time-scale at which the environment changes. Experimentally it is observed, however, that adaptation can be very rapid. Spike-frequency adaptation of cortical neurons, for example, occurs on a time-scale of approximately 100 ms. Here we show that those rapid adaptation processes can also be understood within the framework of information theory. We start with the hypothesis that neuronal codes are designed to optimize the information a neuronal representation conveys about an input stimulus for any increasing time window beginning with stimulus onset, and we show that this implies a rapid adaptation of the neuronal code on the time-scale of stimulus presentation. Adaptation, however, does not occur because the state of the environment changes. Rather it is a reaction to changes of the organisms own internal state, e.g. the level of noise in the neuronal representation. We apply this approach to a model of an orientation hypercolumn in the primary visual cortex, and predict that inter-columnar interactions should adapt on the time-scale of a typical fixation period ( approximately 300 ms).

Tumour class prediction and discovery by microarray-based DNA methylation analysis.

Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers.