Detection of potential target genes in silico?

Prediction of cis-regulatory elements for drug-activated transcription factors in the regulation of drug-metabolising enzymes and drug transporters

The expression of drug-metabolising enzymes is affected by many endogenous and exogenous factors, including sex, age, diet and exposure to xenobiotics and drugs. To understand fully how the organism metabolises a drug, these alterations in gene expression must be taken into account. The central process, the definition of likely regulatory elements in the genes coding for enzymes and transporters involved in drug disposition, can be vastly accelerated using existing and emerging bioinformatics methods to unravel the regulatory networks causing drug-mediated induction of genes. Here, various approaches to predict transcription factor interactions with regulatory DNA elements are reviewed.

New Approaches forin SilicoIdentification of Cytokine-Modified β Cell Gene Networks

Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine-induced apoptosis depends on the expression of pro- and anti-apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and "effector" gene networks regulated by interleukin-1beta and/or interferon-gamma in beta cells. This suggests that beta cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF-kappaB (previously shown to be pivotal for beta cell apoptosis) in 15 temporally related gene clusters, identified by time-course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer-based findings must be validated by use of positive and negative controls, and by "ChIP on chip" analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a "proof of principle" for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine-regulated beta cell gene networks leading to beta cell apoptosis in T1DM.

Genome-wide co-occurrence of promoter elements reveals a cis-regulatory cassette of rRNA transcription motifs in Saccharomyces cerevisiae

Combinatorial regulation is an important feature of eukaryotic transcription. However, only a limited number of studies have characterized this aspect on a whole-genome level. We have conducted a genome-wide computational survey to identify cis-regulatory motif pairs that co-occur in a significantly high number of promoters in the S. cerevisiae genome. A pair of novel motifs, mRRPE and PAC, co-occur most highly in the genome, primarily in the promoters of genes involved in rRNA transcription and processing. The two motifs show significant positional and orientational bias with mRRPE being closer to the ATG than PAC in most promoters. Two additional rRNA-related motifs, mRRSE3 and mRRSE10, also co-occur with mRRPE and PAC. mRRPE and PAC are the primary determinants of expression profiles while mRRSE3 and mRRSE10 modulate these patterns. We describe a new computational approach for studying the functional significance of the physical locations of promoter elements that combine analyses of genome sequence and microarray data. Applying this methodology to the regulatory cassette containing the four rRNA motifs demonstrates that the relative promoter locations of these elements have a profound effect on the expression patterns of the downstream genes. These findings provide a function for these novel motifs and insight into the mechanism by which they regulate gene expression. The methodology introduced here should prove particularly useful for analyzing transcriptional regulation in more complex genomes.

NUBIScan, an in silico approach for prediction of nuclear receptor response elements

Nuclear receptors (NRs) are transcription factors activated by a multitude of hormones, other endogenous substances, and exogenous molecules. These proteins modulate the regulation of target genes by contacting their promoter or enhancer sequences at specific recognition sites. The identification of these response elements is the first step toward detailed insight into the regulatory mechanisms affecting a gene. We have developed NUBIScan, a computer algorithm to predict DNA recognition sites for NRs in the regulatory regions of genes. The algorithm is based on weighted nucleotide distribution matrices and combines scores from both half-sites necessary for NR dimer binding. It provides more specific identification of functional sites than previous in silico approaches, as evidenced by scanning published regulatory regions of drug-inducible genes and comparing the obtained predictions with experimental results. In prospective analyses, NUBIScan consistently identified new functional NR binding sites in sets of large sequences, which had eluded previous analyses. This is exemplified by the detailed functional analysis of the flanking region of two genes. This approach therefore facilitates the selection of likely sites of gene regulation for subsequent experimental analysis.

Molecular and in silico characterization of a promoter module and C/EBP element that mediate LPS-induced RANTES/CCL5 expression in monocytic cells

The chemokine RANTES/CCL5 is a proinflammatory agent produced by a variety of tissues in response to specific stimuli. In human monocytes, RANTES/CCL5 transcription is up-regulated rapidly and transiently in response to LPS. We describe here two regions that help control LPS-driven transcription from the human RANTES/CCL5 promoter in monocytic cells. These sites were analyzed by using DNase I footprinting, transient transfection assays, site-directed mutagenesis, and EMSA. RANTES site E (R(E), -125/-99) constitutively binds C/EBP proteins in monocytic Mono Mac 6 cells. Mutation of region R(E) led to a significant (40%-50%) reduction in LPS-induced promoter reporter activity. Region R(AB) is composed of tandem kB-like elements R(A) and R(B) (-73/-34). These sites working in concert act as an LPS-responsive promoter module. R(A) constitutively binds Sp1, and Rel p50/p65 following LPS stimulation. Either factor can mediate transcriptional effects at R(A). Induced Rel p50/p50 binding to site R(B) is required for LPS regulation of RANTES/CCL5 transcription. A series of computer models based on the RANTES/CCL5 promoter were generated to represent the organization of these functional elements. The models could identify LPS-regulated promoters in human, other vertebrate, and viral sequences in various databases.

The Otx2 homeoprotein regulates expression from the gonadotropin-releasing hormone proximal promoter

The GnRH gene is expressed exclusively in a highly restricted population of approximately 800 neurons in the mediobasal hypothalamus in the mouse. The Otx2 homeoprotein has been shown to colocalize with GnRH in embryonic mouse brain. We have identified a highly conserved bicoid-related Otx target sequence within the proximal promoter region of the GnRH gene from several species. This element from the rat GnRH promoter binds baculovirus-expressed Otx2 protein and Otx2 protein in nuclear extracts of a hypothalamic GnRH-expressing neuronal cell line, GT1-7. Transient transfection assays indicate that the GnRH promoter Otx/bicoid site is required for specific expression of the GnRH gene in GT1-7 cells and that it can confer specificity to a neutral Rous sarcoma virus (RSV) promoter in GT1-7 cells but not in NIH3T3 cells. Overexpression of mouse Otx2 in GT1-7 cells induces expression of a GnRH promoter plasmid, an effect that is dependent upon the Otx binding site. Thus, the GnRH proximal promoter is regulated by the Otx2 homeoprotein. Finally, we have now demonstrated the presence of Otx2 protein in the GnRH neurons of the adult mouse hypothalamus. These data suggest that Otx2 is important in the development of the GnRH neuron and/or in the maintenance of GnRH expression in the adult mouse hypothalamus.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

Large-Scale Gene Expression Data Analysis: A New Challenge to Computational Biologists

The use of high-density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of each of the ∼6000 genes in the yeast genome. In this survey I review three recent experiments related to transcriptional regulation and discuss the great challenge for computational biologists trying to extract functional information from such large-scale gene expression data.