Mining knowledge for the methylation status of CpG islands using alternating decision trees

Network-based prediction and knowledge mining of disease genes

Background

In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident.

Methods

We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship.

Results

Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network could be used to identify likely disease associations.

Conclusions

We analyzed the human protein interaction network and its relationship to disease and found that both the number of interactions with other proteins and the disease relationship of neighboring proteins helped to determine whether a protein had a relationship to disease. Our classifier predicted many proteins with no annotated disease association to be disease-related, which indicated that these proteins have network characteristics that are similar to disease-related proteins and may therefore have disease associations not previously identified. By performing a post-processing step after the prediction, we were able to identify evidence in literature supporting this possibility. This method could provide a useful filter for experimentalists searching for new candidate protein targets for drug repositioning and could also be extended to include other network and data types in order to refine these predictions.

Characterizing the Retinoblastoma 1 locus: putative elements for Rb1 regulation by in silico analysis

Limited understanding of the Rb1 locus hinders genetic and epigenetic analyses of Retinoblastoma, a childhood cancer of the nervous systems. In this study, we used in silico tools to investigate and review putative genetic and epigenetic elements of the Rb1 gene. We report transcription start sites, CpG islands, and regulatory moieties that are likely to influence transcriptional states of this gene. These might contribute genetic and epigenetic information modulating tissue-specific transcripts and expression levels of Rb1. The elements we identified include tandem repeats that reside within or next to CpG islands near Rb1's transcriptional start site, and that are likely to be polymorphic among individuals. Our analyses highlight the complexity of this gene and suggest opportunities and limitations for future studies of retinoblastoma, genetic counseling, and the accurate identification of patients at greater risk of developing the malignancy.

Identification and characterization of putative methylation targets in the MAOA locus using bioinformatic approaches

Monoamine oxidase A (MAO A) is an enzyme that catalyzes the oxidation of neurotransmitter amines. A functional polymorphism in the human MAOA gene (high- and low-MAOA) has been associated with distinct behavioral phenotypes. To investigate directly the biological mechanism whereby this polymorphism influences brain function, we recently measured the activity of the MAO A enzyme in healthy volunteers. When found no relationship between the individual's brain MAO A level and the MAOA genotype, we postulated that there are additional regulatory mechanisms that control the MAOA expression. Given that DNA methylation is linked to the regulation of gene expression, we hypothesized that epigenetic mechanisms factor into the MAOA expression. Our underplaying assumption was that the differences in an individual's genotype play a key role in the epigenetic potential of the MAOA locus and, consequently, determine the individual's level of MAO A activity in the brain. As a first step towards experimental validation of the hypothesis, we performed a comprehensive bioinformatic analysis aiming to interrogate genomic features and attributes of the MAOA locus that might modulate its epigenetic sensitivity. Major findings of our analysis are the following: (1) the extended MAOA regulatory region contains two CpG islands (CGIs), one of which overlaps with the canonical MAOA promoter and the other is located further upstream; both CGIs exhibit sensitivity to differential methylation. (2) The uVNTR's effect on the MAOA's transcriptional activity might have epigenetic nature: this polymorphic region resides within the MAOA's CGI and itself contains CpGs, thus, the number of repeating increments effectively changes the number of methylatable cytosines in the MAOA promoter. An array of in silico analyses (the nucleosome positioning, the physical properties of the local DNA, the clustering of transcription-factor binding sites) together with experimental data on histone modifications and Pol 2 sites and data from the RefSeq mRNA library suggest that the MAOA gene might have an alternative promoter. Based on our findings, we propose a regulatory mechanism for the human MAOA according to which the MAOA expression in vivo is executed by the generation of tissue-specific transcripts initiated from the alternative promoters (both CGI-associated) where transcriptional activation of a particular promoter is under epigenetic control.

Molecular genetic analysis of Down syndrome

Down syndrome (DS) is caused by trisomy of all or part of human chromosome 21 (HSA21) and is the most common genetic cause of significant intellectual disability. In addition to intellectual disability, many other health problems, such as congenital heart disease, Alzheimer's disease, leukemia, hypotonia, motor disorders, and various physical anomalies occur at an elevated frequency in people with DS. On the other hand, people with DS seem to be at a decreased risk of certain cancers and perhaps of atherosclerosis. There is wide variability in the phenotypes associated with DS. Although ultimately the phenotypes of DS must be due to trisomy of HSA21, the genetic mechanisms by which the phenotypes arise are not understood. The recent recognition that there are many genetically active elements that do not encode proteins makes the situation more complex. Additional complexity may exist due to possible epigenetic changes that may act differently in DS. Numerous mouse models with features reminiscent of those seen in individuals with DS have been produced and studied in some depth, and these have added considerable insight into possible genetic mechanisms behind some of the phenotypes. These mouse models allow experimental approaches, including attempts at therapy, that are not possible in humans. Progress in understanding the genetic mechanisms by which trisomy of HSA21 leads to DS is the subject of this review.