An overview of the computational analyses and discovery of transcription factor binding sites

Harnessing regulatory networks in Actinobacteria for natural product discovery

Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches.

ONE-SENTENCE SUMMARY

This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.

Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements

Summary: Gene transcription is mostly conducted through interactions of various transcription factors and their binding sites on DNA (regulatory elements, REs). Today, we are still far from understanding the real regulatory content of promoter regions. Computer methods for identification of REs remain a widely used tool for studying and understanding transcriptional regulation mechanisms. The Nsite, NsiteH and NsiteM programs perform searches for statistically significant (non-random) motifs of known human, animal and plant one-box and composite REs in a single genomic sequence, in a pair of aligned homologous sequences and in a set of functionally related sequences, respectively.

Availability and implementation: Pre-compiled executables built under commonly used operating systems are available for download by visiting http://www.molquest.kaust.edu.sa and http://www.softberry.com.

Contact:solovictor@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Bioinformatic analysis of cis-regulatory interactions between progesterone and estrogen receptors in breast cancer

Chromatin factors interact with each other in a cell and sequence-specific manner in order to regulate transcription and a wealth of publically available datasets exists describing the genomic locations of these interactions. Our recently published BiSA (Binding Sites Analyser) database contains transcription factor binding locations and epigenetic modifications collected from published studies and provides tools to analyse stored and imported data. Using BiSA we investigated the overlapping cis-regulatory role of estrogen receptor alpha (ERα) and progesterone receptor (PR) in the T-47D breast cancer cell line. We found that ERα binding sites overlap with a subset of PR binding sites. To investigate further, we re-analysed raw data to remove any biases introduced by the use of distinct tools in the original publications. We identified 22,152 PR and 18,560 ERα binding sites (<5% false discovery rate) with 4,358 overlapping regions among the two datasets. BiSA statistical analysis revealed a non-significant overall overlap correlation between the two factors, suggesting that ERα and PR are not partner factors and do not require each other for binding to occur. However, Monte Carlo simulation by Binary Interval Search (BITS), Relevant Distance, Absolute Distance, Jaccard and Projection tests by Genometricorr revealed a statistically significant spatial correlation of binding regions on chromosome between the two factors. Motif analysis revealed that the shared binding regions were enriched with binding motifs for ERα, PR and a number of other transcription and pioneer factors. Some of these factors are known to co-locate with ERα and PR binding. Therefore spatially close proximity of ERα binding sites with PR binding sites suggests that ERα and PR, in general function independently at the molecular level, but that their activities converge on a specific subset of transcriptional targets.

What mRNA Abundances Can Tell us about Metabolism

Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.

Computational studies of imprinted genes

Computational studies on imprinted genes can have very different purposes: one major aim of these studies is the identification of DNA elements that distinguish imprinted genes from biallelically expressed genes. Comparative studies may help to identify imprinting regulatory elements and to understand common mechanisms of imprinted gene regulation in mammalian species. To date, the continuously growing number of genomic and epigenetic data sets makes detailed, genome-wide analyses on imprinted genes feasible. However, imprinted genes are characterized by genomic features that can influence statistics and can make such studies difficult. Hence, comparative computational studies can get very complex and require a tight interaction between bioinformaticians and biologists. Furthermore, analyses of raw data that are generated by micro-array hybridization and high-throughput sequencing technologies require computational approaches that have been designed especially for the epigenetic field. This chapter gives an overview about databases and software that is suitable for analyses of imprinted genes. Furthermore, possible difficulties that are typical for computational and statistical analyses of imprinted genes are described.

Recent patents on therapeutic applications of the transcription factor decoy approach

INTRODUCTION

Transcription is considered as an important target of drugs employed in biomedicine. Therefore, novel strategies to inhibit the biological effects of transcription factors (TFs) are of interest, such as targeting promoters with triple-helix-forming oligonucleotides and antisense targeting of mRNAs coding for TFs.

AREAS COVERED

The objective of this review is to describe studies considering inhibition of TF functions with molecules mimicking TF binding sites (transcription factor decoy approach, TFD) and to summarize the patents on possible clinical applications of this approach.

EXPERT OPINION

Treatment of cells with TFD molecules leads to inhibition (or activation) of genes regulated by the target transcription factors. The studies and patents on this specific issue have taken in great consideration the delivery strategy, which is a very important parameter. The TFD strategy has been proven effective in vivo. The stability of the TFD molecules in vivo should be carefully considered, as well as the possible toxicity and/or possible effects on innate and adaptive immune response. In order to improve clinical parameters, many patents suggest the use of the TFD molecules in combination with drugs already employed in therapy. We are expecting in the near future relevant clinical trials based on the TFD strategy.