Functional Profiling of Human MeCP2 by Automated Data Comparison Analysis and Computerized Expression Pathway Modeling

OBJECTIVES

Methyl-CpG binding protein 2 (MeCP2) is a ubiquitous epigenetic factor that represses gene expression by modifying chromatin. Mutations in the MeCP2 gene cause Rett syndrome, a progressive neurodevelopmental disorder. Recent studies also have shown that MeCP2 plays a role in carcinogenesis. Specifically, functional ablation of MeCP2 suppresses cell growth and leads to the proliferation of cancer cells. However, MeCP2's function in adult tissues remains poorly understood. We utilized a weight matrix-based comparison software to identify transcription factor binding site (TFBS) of MeCP2-regulated genes, which were recognized by cDNA microarray analysis.

METHODS

MeCP2 expression was silenced using annealed siRNA in HEK293 cells, and then a cDNA microarray analysis was performed. Functional analysis was carried out, and transcriptional levels in target genes regulated by MeCP2 were investigated. TFBS analysis was done within genes selected by the cDNA microarray analysis, using a weight matrix-based program and the TRANSFAC 6.0 database.

RESULTS

Among the differentially expressed genes with a change in expression greater than two-fold, 189 genes were up-regulated and 91 genes were down-regulated. Genes related to apoptosis and cell proliferation (JUN, FOSL2, CYR61, SKIL, ATF3, BMABI, BMPR2, RERE, and FALZ) were highly up-regulated. Genes with anti-apoptotic and anti-proliferative functions (HNRPA0, HIS1, and FOXC1) were down-regulated. Using TFBS analysis within putative promoters of novel candidate target genes of MeCP2, disease-related transcription factors were identified.

CONCLUSIONS

The present results provide insights into the new target genes regulated by MeCP2 under epigenetic control. This information will be valuable for further studies aimed at clarifying the pathogenesis of Rett syndrome and neoplastic diseases.

Sequence information gain based motif analysis

BACKGROUND

The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions.

RESULTS

This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70% of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions.

CONCLUSIONS

Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://b2slab.upc.edu.

Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

Single nucleotide polymorphisms (SNPs) make up the most common form of mutations in human cytochrome P450 enzymes family, and have the potential to bring with different drug responses or specific diseases in individual patients. Here, based on machine learning technology, we aim to explore an effective set of sequence-based features for improving prediction of SNPs by using support vector machine algorithms. The features are derived from the target residues and flanking protein sequences, such as amino acid types, sequences composition, physicochemical properties, position-specific scoring matrix, phylogenetic entropy and the number of possible codons of target residues. In order to deal with the imbalance data with a majority of non-SNPs and a minority of SNPs, a preprocessing strategy based on fuzzy set theory was applied to the datasets. Our final model achieves the performance of 93.8% in sensitivity, 88.8% in specificity, 91.3% in accuracy and 0.971 of AUC value, which is significantly higher than the previous DNA sequence-based or protein sequence-based methods. Furthermore, our study also suggested the roles of individual features for prediction of SNPs. The most important features consist of the amino acid type, the number of available codons, position-specific scoring matrix and phylogenetic entropy. The improved model will be a promising tool for SNP predictions, and assist in the research of genome mutation and personalized prescriptions.

LASAGNA-Search: an integrated web tool for transcription factor binding site search and visualization

The release of ChIP-seq data from the ENCyclopedia Of DNA Elements (ENCODE) and Model Organism ENCyclopedia Of DNA Elements (modENCODE) projects has significantly increased the amount of transcription factor (TF) binding affinity information available to researchers. However, scientists still routinely use TF binding site (TFBS) search tools to scan unannotated sequences for TFBSs, particularly when searching for lesser-known TFs or TFs in organisms for which ChIP-seq data are unavailable. The sequence analysis often involves multiple steps such as TF model collection, promoter sequence retrieval, and visualization; thus, several different tools are required. We have developed a novel integrated web tool named LASAGNA-Search that allows users to perform TFBS searches without leaving the web site. LASAGNA-Search uses the LASAGNA (Length-Aware Site Alignment Guided by Nucleotide Association) algorithm for TFBS alignment. Important features of LASAGNA-Search include (i) acceptance of unaligned variable-length TFBSs, (ii) a collection of 1726 TF models, (iii) automatic promoter sequence retrieval, (iv) visualization in the UCSC Genome Browser, and (v) gene regulatory network inference and visualization based on binding specificities. LASAGNA-Search is freely available at http://biogrid.engr.uconn.edu/lasagna_search/.

MEET: motif elements estimation toolkit

MEET is an R package that integrates a set of algorithms for the detection of transcription factor binding sites (TFBS). The MEET R package includes five motif searching algorithms: MEME/MAST(Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) and MATCH. In addition MEET allows the user to work with different alignment algorithms: MUSCLE (Multiple Sequence Comparison by Log-Expectation), ClustalW and MEME. The package can work in two modes, training and detection. The training mode allows the user to choose the best parameters of a detector. Once the parameters are chosen, the detection mode allows to analyze a genome looking for binding sites. Both modes can combine the different alignment and detection methods, offering multiple possibilities. Combining the alignments and the detection algorithms makes possible the comparison between detection models at the same level, without having to care about the differences produced during the alignment process. The MEET R package can be downloaded from http://sisbio.recerca.upc.edu/R/MEET_1.0. tar.gz.