Visualizing the competitive recognition of TATA-boxes in vertebrate promoters

The Naming of Names: Guidelines for Gene Nomenclature in Marchantia

While Marchantia polymorpha has been utilized as a model system to investigate fundamental biological questions for over almost two centuries, there is renewed interest in M. polymorpha as a model genetic organism in the genomics era. Here we outline community guidelines for M. polymorpha gene and transgene nomenclature, and we anticipate that these guidelines will promote consistency and reduce both redundancy and confusion in the scientific literature.

A high-throughput percentage-of-binding strategy to measure binding energies in DNA-protein interactions: application to genome-scale site discovery

Quantifying the binding energy in DNA–protein interactions is of critical importance to understand transcriptional regulation. Based on a simple computational model, this study describes a high-throughput percentage-of-binding strategy to measure the binding energy in DNA–protein interactions between the Shewanella oneidensis ArcA two-component transcription factor protein and a systematic set of mutants in an ArcA-P (phosphorylated ArcA) binding site. The binding energies corresponding to each of the 4 nt at each position in the 15-bp binding site were used to construct a position-specific energy matrix (PEM) that allowed a reliable prediction of ArcA-P binding sites not only in Shewanella but also in related bacterial genomes.

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.

Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays

Microarrays traditionally have been used to analyze the expression behavior of large numbers of coding transcripts. Here we present a comprehensive approach for high-throughput transcript discovery in Escherichia coli focused mainly on intergenic regions which, together with analysis of coding transcripts, provides us with a more complete insight into the organism's transcriptome. Using a whole genome array, we detected expression for 4052 coding transcripts and identified 1102 additional transcripts in the intergenic regions of the E.coli genome. Further classification reveals 317 novel transcripts with unknown function. Our results show that, despite sophisticated approaches to genome annotation, many cellular transcripts remain unidentified. Through the experimental identification of all RNAs expressed under a specific condition, we gain a more thorough understanding of all cellular processes.

Statistical intelligence: effective analysis of high-density microarray data

Microarrays enable researchers to interrogate thousands of genes simultaneously. A crucial step in data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing genes expressed in a specific condition to a reference condition, the genes of interest are those which are differentially regulated. This review focuses on the methods currently available for the selection of such genes. Fold change, unusual ratio, univariate testing with correction for multiple experiments, ANOVA and noise sampling methods are reviewed and compared.

The overlap of Inr and TATA elements sets the use of alternative transcriptional start sites in the mouse galectin-1 gene promoter

In the mouse gene encoding the protein galectin-1, transcription initiation at the +1 site is directed by a TATA box. Here we show that a consensus Inr element (TCCAGTT), which spans residues -34 to -28 and overlaps the TATA box, directs RNA initiation also from a previously uncharacterized site located at position -31. Upstream transcripts are polyadenylated and contribute to more than half of the galectin-1 mRNA population in all tissues analyzed. The promoter architecture is evolutionarily conserved to man, and galectin-1 mRNA size variants accumulate also in human HeLa cells. The 5' end terminus of the transcripts initiated at residue -31 is extremely GC-rich, and may fold into a relative stable hairpin which could influence translation and thus modulate the intracellular levels of galectin-1. The interval -63/+45 contains sufficient information to ensure RNA initiation from both -31 and +1 sites, and a Sp1 site spanning residues -57 to -48 is crucial for promoter functioning. The unusual overlap of core promoter elements suggests that RNA initiation from the -31 and the +1 sites may take place in a sequential manner.

Discovery and modeling of transcriptional regulatory regions

A complex network of regulatory controls governs the patterns of gene expression. Enabled by the tools of molecular cloning, initial experimental queries into the gene regulatory network elucidated a wide array of transcription factors and their cognate binding sites from hundreds of genes. The recent fusion of genome-scale experimental tools, a more comprehensive gene catalog, and concomitant advances in computational methodology, has extended the range of questions being posed. The potential to further our understanding of the biochemical mechanisms of transcriptional regulation and to accelerate the delineation of regulatory control regions in the human genome is enormous.

In silico detection of control signals: mRNA 3'-end-processing sequences in diverse species

We have investigated mRNA 3'-end-processing signals in each of six eukaryotic species (yeast, rice, arabidopsis, fruitfly, mouse, and human) through the analysis of more than 20,000 3'-expressed sequence tags. The use and conservation of the canonical AAUAAA element vary widely among the six species and are especially weak in plants and yeast. Even in the animal species, the AAUAAA signal does not appear to be as universal as indicated by previous studies. The abundance of single-base variants of AAUAAA correlates with their measured processing efficiencies. As found previously, the plant polyadenylation signals are more similar to those of yeast than to those of animals, with both common content and arrangement of the signal elements. In all species examined, the complete polyadenylation signal appears to consist of an aggregate of multiple elements. In light of these and previous results, we present a broadened concept of 3'-end-processing signals in which no single exact sequence element is universally required for processing. Rather, the total efficiency is a function of all elements and, importantly, an inefficient word in one element can be compensated for by strong words in other elements. These complex patterns indicate that effective tools to identify 3'-end-processing signals will require more than consensus sequence identification.

The biology of eukaryotic promoter prediction--a review

Computational prediction of eukaryotic promoters from the nucleotide sequence is one of the most attractive problems in sequence analysis today, but it is also a very difficult one. Thus, current methods predict in the order of one promoter per kilobase in human DNA, while the average distance between functional promoters has been estimated to be in the range of 30-40 kilobases. Although it is conceivable that some of these predicted promoters correspond to cryptic initiation sites that are used in vivo, it is likely that most are false positives. This suggests that it is important to carefully reconsider the biological data that forms the basis of current algorithms, and we here present a review of data that may be useful in this regard. The review covers the following topics: (1) basal transcription and core promoters, (2) activated transcription and transcription factor binding sites, (3) CpG islands and DNA methylation, (4) chromosomal structure and nucleosome modification, and (5) chromosomal domains and domain boundaries. We discuss the possible lessons that may be learned, especially with respect to the wealth of information about epigenetic regulation of transcription that has been appearing in recent years.

Regulatory elements and expression profiles

There has been steady progress in the computational analysis of transcription control regions, but current methods of predicting the gene regulatory features of noncoding sequences are still not accurate enough to be useful in automatic genome annotation. Therefore, detailed information on the expression patterns of newly sequenced genes is more likely to come from microarray-based high-throughput mRNA quantitation technologies, which have made revolutionary progress over the past few years and are now ready for genome-wide application. Future solutions to the regulatory element prediction problem may be found by the combined analysis of genome sequence and expression data.

Genomic detection of new yeast pre-mRNA 3'-end-processing signals

To investigate Saccharomyces cerevisiae 3'-end-processing signals, a set of 1352 unique pre-mRNA 3'-end-processing sites, corresponding to 861 different genes, was identified by alignment of expressed sequence tag sequences with the complete yeast genome. Nucleotide word frequencies in the vicinity of the cleavage sites were analyzed to reveal the signal element features. In addition to previously recognized processing signals, two previously uncharacterized components of the 3'-end-processing signal sequence were discovered, specifically a predominance of U-rich sequences located on either side of the cleavage site. One of these, the downstream U-rich signal, provides a further link between the 3'-end-processing mechanisms of yeast and higher eukaryotes. Analysis of the complete set of 3'-end-processing sites by means of a discrimination function supports a 'contextual' model in which the sum total effectiveness of the signals in all four elements determines whether or not processing occurs.

Analysis of the human GDNF gene reveals an inducible promoter, three exons, a triplet repeat within the 3'-UTR and alternative splice products

Glial cell line-derived neurotrophic factor (GDNF), a distant member of the TGF-beta superfamily, is a survival factor for various neurons, making it a potential therapeutic agent for neurodegenerative disorders. Here we present the genomic structure and characterization of the promoter of the human GDNF (hGDNF) gene. It contains three exons coding for a cDNA of 4.6 kb including large 5'- and 3'-untranslated regions (UTRs). The 3'-UTR contains a polymorphic AGG repeat that appears not to be expanded in patients suffering from different neurodegenerative disorders. RT-PCR results in at least three different hGDNF transcripts including one that lacks exon 2. Transient expression experiments reveal that exon 2 is essential for proper cellular processing to yield a secreted form of hGDNF, whereas expression of exon 3 alone is sufficient to code for a mature form of hGDNF retained within the cell. Our data show that the hGDNF gene is driven by a TATA-containing promoter preceding exon 1. A second promoter element has been mapped to a region 5' of exon 2. Both promoters are in close proximity to CpG islands covering exons 1 and 2. Using luciferase as a reporter gene, the TATA-containing hGDNF promoter facilitates a 20- to 40-fold increase in transcription when compared with a corresponding promoterless construct, whereas the second promoter confers only weak activity. Furthermore, fibroblast growth factor 2, tetradecanoyl 12-phorbol acetate, an inflammatory agent, and cAMP increase promoter activity, suggesting that GDNF transcriptional regulation is a target of exogenous signals.