The Bioinformatics of Aptamers: HT-SELEX Analysis with AptaSUITE

Next-generation systematic evolution of ligands by exponential enrichment approaches consistently combine the experimental protocol with high-throughput sequencing after each round of selection. This extension, known as HT-SELEX, results in a vast amount of raw sequencing data that requires tailored bioinformatics approaches to efficiently process and analyze these reads. Here, we present a step-by-step walkthrough of AptaSUITE, a previously published, open-source, and platform-independent software collection of peer-reviewed bioinformatics tools for HT-SELEX data, integrated into a convenient and efficient graphical user interface. We highlight AptaSUITE's main components and illustrate their utility for a variety of usage scenarios. These include, but are not limited to, computational solutions for data preprocessing such as demultiplexing and quality control, aptamer candidate identification, as well as binding motif elucidation. Taken together, AptaSUITE comprises a complete bioinformatics solution for HT-SELEX data analysis while providing real-time, intuitive, and graphical access to the aptamer information.

HT-SELEX-based identification of binding pre-miRNA hairpin-motif for small molecules

Selective targeting of biologically relevant RNAs with small molecules is a long-standing challenge due to the lack of clear understanding of the binding RNA motifs for small molecules. The standard SELEX procedure allows the identification of specific RNA binders (aptamers) for the target of interest. However, more effort is needed to identify and characterize the sequence-structure motifs in the aptamers important for binding to the target. Herein, we described a strategy integrating high-throughput (HT) sequencing with conventional SELEX followed by bioinformatic analysis to identify aptamers with high binding affinity and target specificity to unravel the sequence-structure motifs of pre-miRNA, which is essential for binding to the recently developed new water-soluble small-molecule CMBL3aL. To confirm the fidelity of this approach, we investigated the binding of CMBL3aL to the identified motifs by surface plasmon resonance (SPR) spectroscopy and its potential regulatory activity on dicer-mediated cleavage of the obtained aptamers and endogenous pre-miRNAs comprising the identified motif in its hairpin loop. This new approach would significantly accelerate the identification process of binding sequence-structure motifs of pre-miRNA for the compound of interest and would contribute to increase the spectrum of biomedical application.

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study

BACKGROUND

Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.

RESULTS

Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity.

CONCLUSIONS

In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.

High-Throughput Protein Production Combined with High- Throughput SELEX Identifies an Extensive Atlas of Ciona robusta Transcription Factor DNA-Binding Specificities

Transcription factors (TFs) control gene transcription, binding to specific DNA motifs located in cis-regulatory elements across the genome. The identification of TF-binding motifs is thus an important aspect to understand the role of TFs in gene regulation. SELEX, Systematic Evolution of Ligands by EXponential enrichment, is an efficient in vitro method, which can be used to determine the DNA-binding specificity of TFs. Thanks to the development of high-throughput (HT) DNA cloning system and protein production technology, the classical SELEX assay has be extended to high-throughput scale (HT-SELEX).We report here the detailed protocol for the cloning, production, and purification of 420 Ciona robusta DNA BD. 263 Ciona robusta TF DNA-binding domain proteins were purified in milligram quantities and analyzed by HT-SELEX. The identification of 139 recognition sequences generates an atlas of protein-DNA-binding specificities that is crucial for the understanding of the gene regulatory network (GRN) of Ciona robusta. Overall, our analysis suggests that the Ciona robusta repertoire of sequence-specific transcription factors comprises less than 500 genes. The protocols for high-throughput protein production and HT-SELEX described in this article for the study of Ciona robusta TF DNA-binding specificity are generic and have been successfully applied to a wide range of TFs from other species, including human, mouse, and Drosophila.

A De Novo Shape Motif Discovery Algorithm Reveals Preferences of Transcription Factors for DNA Shape Beyond Sequence Motifs

DNA shape adds specificity to sequence motifs but has not been explored systematically outside this context. We hypothesized that DNA-binding proteins (DBPs) preferentially occupy DNA with specific structures ("shape motifs") regardless of whether or not these correspond to high information content sequence motifs. We present ShapeMF, a Gibbs sampling algorithm that identifies de novo shape motifs. Using binding data from hundreds of in vivo and in vitro experiments, we show that most DBPs have shape motifs and can occupy these in the absence of sequence motifs. This "shape-only binding" is common for many DBPs and in regions co-bound by multiple DBPs. When shape and sequence motifs co-occur, they can be overlapping, flanking, or separated by consistent spacing. Finally, DBPs within the same protein family have different shape motifs, explaining their distinct genome-wide occupancy despite having similar sequence motifs. These results suggest that shape motifs not only complement sequence motifs but also facilitate recognition of DNA beyond conventionally defined sequence motifs.

In silico approaches to RNA aptamer design

RNA aptamers are ribonucleic acids that bind to specific target molecules. An RNA aptamer for a disease-related protein has great potential for development into a new drug. However, huge time and cost investments are required to develop an RNA aptamer into a pharmaceutical. Recently, SELEX combined with high-throughput sequencers (i.e., HT-SELEX) has been widely used to select candidate RNA aptamers that bind to a target protein with high affinity and specificity. After candidate selection, further optimizations such as shortening and modifying candidate sequences are performed. In these steps, in silico approaches are expected to reduce the time and cost associated with aptamer drug development. In this article, we review existing in silico approaches to RNA aptamer development, including a method for ranking the candidates of RNA aptamers from HT-SELEX data, clustering a huge number of aptamer sequences, and finding motifs amidst a set of significant RNA aptamers. It is expected that further studies in addition to these methods will be utilized for in silico RNA aptamer design, permitting a minimal number of experiments to be performed through the utilization of sophisticated computational methods.

Dry and wet approaches for genome-wide functional annotation of conventional and unconventional transcriptional activators

Transcription factors (TFs) are master gene products that regulate gene expression in response to a variety of stimuli. They interact with DNA in a sequence-specific manner using a variety of DNA-binding domain (DBD) modules. This allows to properly position their second domain, called "effector domain", to directly or indirectly recruit positively or negatively acting co-regulators including chromatin modifiers, thus modulating preinitiation complex formation as well as transcription elongation. At variance with the DBDs, which are comprised of well-defined and easily recognizable DNA binding motifs, effector domains are usually much less conserved and thus considerably more difficult to predict. Also not so easy to identify are the DNA-binding sites of TFs, especially on a genome-wide basis and in the case of overlapping binding regions. Another emerging issue, with many potential regulatory implications, is that of so-called "moonlighting" transcription factors, i.e., proteins with an annotated function unrelated to transcription and lacking any recognizable DBD or effector domain, that play a role in gene regulation as their second job. Starting from bioinformatic and experimental high-throughput tools for an unbiased, genome-wide identification and functional characterization of TFs (especially transcriptional activators), we describe both established (and usually well affordable) as well as newly developed platforms for DNA-binding site identification. Selected combinations of these search tools, some of which rely on next-generation sequencing approaches, allow delineating the entire repertoire of TFs and unconventional regulators encoded by the any sequenced genome.

AptaPLEX - A dedicated, multithreaded demultiplexer for HT-SELEX data

Aptamers, short and synthetic RNA/DNA molecules binding distinct targets with high affinity and specificity, are identified via Systematic Evolution of Ligands by Exponential Enrichment (SELEX), an in vitro procedure that, starting from a pool of random ssDNA/RNA sequences, selects sequences by amplifying target-affine species through a series of selection cycles. This versatile protocol has recently been combined with high throughput sequencing, allowing arbitrary stages of the selection to be sequenced and analyzed in silico. As a prerequisite, these data require extensive preprocessing by means of quality controls, error correction and demultiplexing, all while taking into account the specific design of aptamers. Existing solutions addressing this task are currently present only as integrated components in larger pipelines, limiting their applicability in independent software solutions. Here we present AptaPLEX, a standalone and platform independent demultiplexer specifically designed for HT-SELEX data. Given the multiplexed data from one or multiple HT-SELEX experiments, AptaPLEX extracts and restores aptamers into the original selection cycles by identifying the barcode and primer regions in each read. AptaPLEX is capable of fuzzy matching for both the barcode and primers, and automatically corrects mismatches between forward and reverse reads for paired-end data. Our software provides a rich set of additional features and can easily be integrated into existing analysis automation pipelines on multiple platforms ranging from desktop machines to cloud based solutions.

Conservation of transcription factor binding specificities across 600 million years of bilateria evolution

Divergent morphology of species has largely been ascribed to genetic differences in the tissue-specific expression of proteins, which could be achieved by divergence in cis-regulatory elements or by altering the binding specificity of transcription factors (TFs). The relative importance of the latter has been difficult to assess, as previous systematic analyses of TF binding specificity have been performed using different methods in different species. To address this, we determined the binding specificities of 242 Drosophila TFs, and compared them to human and mouse data. This analysis revealed that TF binding specificities are highly conserved between Drosophila and mammals, and that for orthologous TFs, the similarity extends even to the level of very subtle dinucleotide binding preferences. The few human TFs with divergent specificities function in cell types not found in fruit flies, suggesting that evolution of TF specificities contributes to emergence of novel types of differentiated cells.