Discovering novel cis-regulatory motifs using functional networks

Designer promoter: an artwork of cis engineering

Advances in systematic computational biology and rapid elucidation of synergistic interplay between cis and trans factors governing transcriptional control have facilitated functional annotation of gene networks. The generation of data through deconstructive, reconstructive and database assisted promoter studies, and its integration to principles of synthetic engineering has started an era of designer promoters. Exploration of natural promoter architecture and the concept of cis engineering have not only enabled fine tuning of single or multiple transgene expression in response to perturbations in the chemical, physiological and environmental stimuli but also provided researchers with a unique answer to various problems in crop improvement in the form of bidirectional promoters.

Prediction of cis-regulatory elements: from high-information content analysis to motif identification

One popular approach to prediction of binding motifs of transcription factors is to model the problem as to search for a group of l-mers (motifs), for some l > 0, one from each of the provided promoter regions of a group of co-expressed genes, that exhibit high information content when aligned without gaps. In our current work, we assume that these desired l-mers have evolved from a common ancestor, each of which has mutations in at most k-positions from the common ancestor, where k is substantially smaller than l. This implies that these l-mers should belong to the k-neighborhood of their common ancestor, measured in terms of Hamming distance. If the ancestor is given, then the problem for finding these l-mers becomes trivial. Unfortunately, the problem of identifying the unknown ancestor is probably as hard as the problem of predicting the motifs themselves. Our goal is to identify a set of l-mers that slightly violate the k-neighborhood of a putative ancestor, but capture all the desired motifs, which will lead to an efficient way for identification of the desired motifs. The main contributions of this paper are in four aspects: (a) we have derived nontrivial lower and upper bounds of information content for a set of l-mers that differ from an unknown ancestor in no more than k positions; (b) we have defined a new distance between two sequences and a k-pseudo-neighborhood, based on the new distance, that contains the k-neighborhood, defined by Hamming distance, of the to-be-defined ancestor; (c) we have developed an algorithm to minimize the sum of all the distances between a predicted ancestor motif and a group of l-mers from the provided promoter regions, using the new distance; and (d) we have tested PROMOCO and compared its prediction results performance with two other prediction programs. The algorithm, implemented as a computer software program PROMOCO, has been used to find all conserved motifs in a set of provided promoter sequences. Our preliminary application of PROMOCO shows that it achieves better or comparable prediction results, when compared to popular programs for identification of cis regulatory binding motifs. A limitation of the algorithm is that it does not work well when the size of the set of provided promoter sequences is too small or when desired motifs appear in only small portion of the given sequences.

Local Renyi entropic profiles of DNA sequences

Background

In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs.

Results

The new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at .

Conclusion

The ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures.

Synthetic promoters: genetic control through cis engineering

Technological advances in plant genetics integrated with systems biology and bioinformatics has yielded a myriad of novel biological data and insights into plant metabolism. This unprecedented advance has provided a platform for targeted manipulation of transcriptional activity through synthetic promoter engineering, and holds great promise as a way to further our understanding of regulatory complexity. The challenge and strategy for predictive experimental gene expression is the accurate design and use of molecular 'switches' and modules that will regulate single or multiple plant transgenes in direct response to specific environmental, physiological and chemical cues. In particular, focusing on cis-motif rearrangement, future plant biotechnology applications and the elucidation of cis- and trans-regulatory mechanisms could greatly benefit from using plant synthetic promoters.

A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes

We present rna22, a method for identifying microRNA binding sites and their corresponding heteroduplexes. Rna22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. Computationally, we show that rna22 identifies most of the currently known heteroduplexes. Experimentally, with luciferase assays, we demonstrate average repressions of 30% or more for 168 of 226 tested targets. The analysis suggests that some microRNAs may have as many as a few thousand targets, and that between 74% and 92% of the gene transcripts in four model genomes are likely under microRNA control through their untranslated and amino acid coding regions. We also extended the method's key idea to a low-error microRNA-precursor-discovery scheme; our studies suggest that the number of microRNA precursors in mammalian genomes likely ranges in the tens of thousands.

Short blocks from the noncoding parts of the human genome have instances within nearly all known genes and relate to biological processes

Using an unsupervised pattern-discovery method, we processed the human intergenic and intronic regions and catalogued all variable-length patterns with identically conserved copies and multiplicities above what is expected by chance. Among the millions of discovered patterns, we found a subset of 127,998 patterns, termed pyknons, which have additional nonoverlapping instances in the untranslated and protein-coding regions of 30,675 transcripts from 20,059 human genes. The pyknons arrange combinatorially in the untranslated and coding regions of numerous human genes where they form mosaics. Consecutive instances of pyknons in these regions show a strong bias in their relative placement, favoring distances of approximately 22 nucleotides. We also found pyknons to be enriched in a statistically significant manner in genes involved in specific processes, e.g., cell communication, transcription, regulation of transcription, signaling, transport, etc. For approximately 1/3 of the pyknons, the intergenic/intronic instances of their reverse complement lie within 380,084 nonoverlapping regions, typically 60-80 nucleotides long, which are predicted to form double-stranded, energetically stable, hairpin-shaped RNA secondary structures; additionally, the pyknons subsume approximately 40% of the known microRNA sequences, thus suggesting a possible link with posttranscriptional gene silencing and RNA interference. Cross-genome comparisons reveal that many of the pyknons have instances in the 3' UTRs of genes from other vertebrates and invertebrates where they are overrepresented in similar biological processes, as in the human genome. These unexpected findings suggest potential unique functional connections between the coding and noncoding parts of the human genome.

The discovery, positioning and verification of a set of transcription-associated motifs in vertebrates

We have developed several new methods to investigate transcriptional motifs in vertebrates. We developed a specific alignment tool appropriate for regions involved in transcription control, and exhaustively enumerated all possible 12-mers for involvement in transcription by virtue of their mammalian conservation. We then used deeper comparative analysis across vertebrates to identify the active instances of these motifs. We have shown experimentally in Medaka fish that a subset of these predictions is involved in transcription.

Systematic discovery of new recognition peptides mediating protein interaction networks

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or “linear motif” (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a K_D of 22 μM and a VxxxRxYS motif that binds Translin with a K_D of 43 μM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.

Many protein interactions are mediated by short amino acid motifs. The authors describe a new approach to identify these interaction motifs and experimentally validate some of their binding predictions.

Linear motifs: evolutionary interaction switches

Linear motifs are short sequence patterns associated with a particular function. They differ fundamentally from longer, globular protein domains in terms of their binding affinities, evolution and in how they are found experimentally or computationally. In this Minireview, we discuss various aspects of these critically important functional regions.

Data integration: challenges for drug discovery

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Computational discovery of DNA motifs associated with cell type-specific gene expression in Ciona

Temporally and spatially co-expressed genes are expected to be regulated by common transcription factors and therefore to share cis-regulatory elements. In the ascidian Ciona intestinalis, the whole-genome sequences and genome-scale gene expression profiles allow the use of computational techniques to investigate cis-elements that control transcription. We collected 5' flanking sequences of 50 tissue-specific genes from genome databases of C. intestinalis and a closely related species Ciona savignyi. We searched for DNA motifs over-represented in upstream regions of a group of co-expressed genes. Several motifs were distributed predominantly in upstream regions of photoreceptor, pan-neuronal, or muscle-specific gene groups. One muscle-specific motif, M2, was distributed preferentially in regions from -200 to -100 bp relative to the translational start sites. Promoters of muscle-specific genes of C. intestinalis were isolated, connected with a green fluorescent protein gene (GFP), and introduced into C. intestinalis embryos. In muscle cells, these promoters specifically drove GFP expression, which mutations of the M2 sites greatly reduced. When M2 sites were located upstream of a basal promoter, the reporter GFP was specifically expressed in muscle cells. These results suggest the validity of our computational prediction of cis-regulatory elements. Thus, bioinformatics can help identify cis-regulatory elements involved in chordate development.

Charting gene regulatory networks: strategies, challenges and perspectives

One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including aspects such as genome annotation, identification of cis-regulatory elements and transcription factors, information on protein-DNA and protein-protein interactions, and data mining and integration. Some of these broad sets of data have already been assembled for building networks of gene regulation. Even though these datasets are still far from comprehensive, and the approach faces many important and difficult challenges, some strategies have begun to make connections between disparate regulatory events and to foster new hypotheses. In this article we review several different genomics and proteomics technologies, and present bioinformatics methods for exploring these data in order to make novel discoveries.

Transcriptional regulation of protein complexes in yeast

BACKGROUND

Multiprotein complexes play an essential role in many cellular processes. But our knowledge of the mechanism of their formation, regulation and lifetimes is very limited. We investigated transcriptional regulation of protein complexes in yeast using two approaches. First, known regulons, manually curated or identified by genome-wide screens, were mapped onto the components of multiprotein complexes. The complexes comprised manually curated ones and those characterized by high-throughput analyses. Second, putative regulatory sequence motifs were identified in the upstream regions of the genes involved in individual complexes and regulons were predicted on the basis of these motifs.

RESULTS

Only a very small fraction of the analyzed complexes (5-6%) have subsets of their components mapping onto known regulons. Likewise, regulatory motifs are detected in only about 8-15% of the complexes, and in those, about half of the components are on average part of predicted regulons. In the manually curated complexes, the so-called 'permanent' assemblies have a larger fraction of their components belonging to putative regulons than 'transient' complexes. For the noisier set of complexes identified by high-throughput screens, valuable insights are obtained into the function and regulation of individual genes.

CONCLUSIONS

A small fraction of the known multiprotein complexes in yeast seems to have at least a subset of their components co-regulated on the transcriptional level. Preliminary analysis of the regulatory motifs for these components suggests that the corresponding genes are likely to be co-regulated either together or in smaller subgroups, indicating that transcriptionally regulated modules might exist within complexes.

Recent advances in computational promoter analysis in understanding the transcriptional regulatory network

The computational approach to the study of transcriptional regulation networks has become more attractive and feasible with the rapid accumulation of complete genome sequences and the advance of high-throughput expression profiling technology. In this review, current computational approaches for understanding the transcriptional regulatory network, including promoter prediction, transcription factor binding site identification, combinatorial regulatory elements prediction, and transcription factor target gene identification, are discussed. The role of comparative genomics in transcription regulatory region analysis is also reviewed.