SEWAL: an open-source platform for next-generation sequence analysis and visualization

Implementation of High-Throughput Sequencing (HTS) in Aptamer Selection Technology

Aptamers are nucleic acid ligands that bind specifically to a target of interest. Aptamers have gained in popularity due to their high potential for different applications in analysis, diagnostics, and therapeutics. The procedure called systematic evolution of ligands by exponential enrichment (SELEX) is used for aptamer isolation from large nucleic acid combinatorial libraries. The huge number of unique sequences implemented in the in vitro evolution in the SELEX process imposes the necessity of performing extensive sequencing of the selected nucleic acid pools. High-throughput sequencing (HTS) meets this demand of SELEX. Analysis of the data obtained from sequencing of the libraries produced during and after aptamer isolation provides an informative basis for precise aptamer identification and for examining the structure and function of nucleic acid ligands. This review discusses the technical aspects and the potential of the integration of HTS with SELEX.

High-throughput methods in aptamer discovery and analysis

Aptamers are small, functional nucleic acids that bind a variety of targets, often with high specificity and affinity. Genomic aptamers constitute the ligand-binding domains of riboswitches, whereas synthetic aptamers find applications as diagnostic and therapeutic tools, and as ligand-binding domains of regulatory RNAs in synthetic biology. Discovery and characterization of aptamers has been limited by a lack of high-throughput approaches that uncover the target-binding domains and the biochemical properties of individual sequences. With the advent of high-throughput sequencing, large-scale analysis of in vitro selected populations of aptamers (and catalytic nucleic acids, such as ribozymes and DNAzmes) became possible. In recent years the development of new experimental approaches and software tools has led to significant streamlining of the selection-pool analysis. This article provides an overview of post-selection data analysis and describes high-throughput methods that facilitate rapid discovery and biochemical characterization of aptamers.

Time-lapse imaging of molecular evolution by high-throughput sequencing

High-throughput sequencing of in vitro selection could artificially provide large quantities of relic sequences from known times of molecular evolution. Here, we demonstrate how it can be used to reconstruct an empirical genealogical evolutionary (EGE) tree of an aptamer family. In contrast to classical phylogenetic trees, this tree-diagram represents proliferation and extinction of sequences within a population during rounds of selection. Such information, which corresponds to their evolutionary fitness, is used to infer which sequences may have been mutated through the selection process that led to the appearance and spreading of new sequences. This approach was validated by the re-analysis of an in vitro selection that had previously identified an aptamer against Annexin A2. It revealed that this aptamer might be the descendant of a sequence that was more highly amplified in early rounds. It also succeeded in predicting improved variants of this aptamer and providing a means to understand the influence of selection pressure on evolution. This is the first demonstration that HTS can provide time-lapse imaging of the evolutionary pathway that is taken by a macromolecule during in vitro selection to evolve by successive mutations through better fitness.

Applications of High-Throughput Sequencing for In Vitro Selection and Characterization of Aptamers

Aptamers are identified through an iterative process of evolutionary selection starting from a random pool containing billions of sequences. Simultaneously to the amplification of high-affinity candidates, the diversity in the pool is exponentially reduced after several rounds of in vitro selection. Until now, cloning and Sanger sequencing of about 100 sequences was usually used to identify the enriched candidates. However, High-Throughput Sequencing (HTS) is now extensively used to replace such low throughput sequencing approaches. Providing a deeper analysis of the library, HTS is expected to accelerate the identification of aptamers as well as to identify aptamers with higher affinity. It is also expected that it can provide important information on the binding site of the aptamers. Nevertheless, HTS requires handling a large amount of data that is only possible through the development of new in silico methods. Here, this review presents these different strategies that have been recently developed to improve the identification and characterization of aptamers using HTS.

Advancements in Aptamer Discovery Technologies

Affinity reagents that specifically bind to their target molecules are invaluable tools in nearly every field of modern biomedicine. Nucleic acid-based aptamers offer many advantages in this domain, because they are chemically synthesized, stable, and economical. Despite these compelling features, aptamers are currently not widely used in comparison to antibodies. This is primarily because conventional aptamer-discovery techniques such as SELEX are time-consuming and labor-intensive and often fail to produce aptamers with comparable binding performance to antibodies. This Account describes a body of work from our laboratory in developing advanced methods for consistently producing high-performance aptamers with higher efficiency, fewer resources, and, most importantly, a greater probability of success. We describe our efforts in systematically transforming each major step of the aptamer discovery process: selection, analysis, and characterization. To improve selection, we have developed microfluidic devices (M-SELEX) that enable discovery of high-affinity aptamers after a minimal number of selection rounds by precisely controlling the target concentration and washing stringency. In terms of improving aptamer pool analysis, our group was the first to use high-throughput sequencing (HTS) for the discovery of new aptamers. We showed that tracking the enrichment trajectory of individual aptamer sequences enables the identification of high-performing aptamers without requiring full convergence of the selected aptamer pool. HTS is now widely used for aptamer discovery, and open-source software has become available to facilitate analysis. To improve binding characterization, we used HTS data to design custom aptamer arrays to measure the affinity and specificity of up to ∼10(4) DNA aptamers in parallel as a means to rapidly discover high-quality aptamers. Most recently, our efforts have culminated in the invention of the "particle display" (PD) screening system, which transforms solution-phase aptamers into "aptamer particles" that can be individually screened at high-throughput via fluorescence-activated cell sorting. Using PD, we have shown the feasibility of rapidly generating aptamers with exceptional affinities, even for proteins that have previously proven intractable to aptamer discovery. We are confident that these advanced aptamer-discovery methods will accelerate the discovery of aptamer reagents with excellent affinities and specificities, perhaps even exceeding those of the best monoclonal antibodies. Since aptamers are reproducible, renewable, stable, and can be distributed as sequence information, we anticipate that these affinity reagents will become even more valuable tools for both research and clinical applications.

In vitro evolution of coenzyme-independent variants from the glmS ribozyme structural scaffold

Uniquely among known natural ribozymes that cleave RNA sequence-specifically, the glmS ribozyme-riboswitch employs a small molecule, glucosamine-6-phosphate (GlcN6P) as a catalytic cofactor. In vitro selection was employed to search for coenzyme-independent variants of this ribozyme. In addition to shedding light on the catalytic mechanism of the ribozyme, such variants could resemble the evolutionary ancestors of the modern, GlcN6P-regulated ribozyme-riboswitch. A mutant pool was constructed such that the secondary structure elements, which define the triply-pseudoknotted global fold of the ribozyme, was preserved. A stringent selection scheme that relies on thiol-mercury affinity chromatography for separating active and inactive sequences ultimately yielded a triple mutant with a cleavage rate exceeding 3min(-1) that only requires divalent cations for activity. Mutational analysis demonstrated that a point reversion of the variant toward the wild-type sequence was sufficient to partially restore GlcN6P-dependence, suggesting that coenzyme dependence can be readily be acquired by RNAs that adopt the glmS ribozyme fold. The methods employed to perform this selection experiment are described in detail in this review.

Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments

In vitro selection experiments in biochemistry allow for the discovery of novel molecules capable of specific desired biochemical functions. However, this is not the only benefit we can obtain from such selection experiments. Since selection from a random library yields an unprecedented, and sometimes comprehensive, view of how a particular biochemical function is distributed across sequence space, selection experiments also provide data for creating and analyzing molecular fitness landscapes, which directly map function (phenotypes) to sequence information (genotypes). Given the importance of understanding the relationship between sequence and functional activity, reliable methods to build and analyze fitness landscapes are needed. Here, we present some statistical methods to extract this information from pools of RNA molecules. We also provide new computational tools to construct and study molecular fitness landscapes.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

FASTAptamer: A Bioinformatic Toolkit for High-throughput Sequence Analysis of Combinatorial Selections

High-throughput sequence (HTS) analysis of combinatorial selection populations accelerates lead discovery and optimization and offers dynamic insight into selection processes. An underlying principle is that selection enriches high-fitness sequences as a fraction of the population, whereas low-fitness sequences are depleted. HTS analysis readily provides the requisite numerical information by tracking the evolutionary trajectory of individual sequences in response to selection pressures. Unlike genomic data, for which a number of software solutions exist, user-friendly tools are not readily available for the combinatorial selections field, leading many users to create custom software. FASTAptamer was designed to address the sequence-level analysis needs of the field. The open source FASTAptamer toolkit counts, normalizes and ranks read counts in a FASTQ file, compares populations for sequence distribution, generates clusters of sequence families, calculates fold-enrichment of sequences throughout the course of a selection and searches for degenerate sequence motifs. While originally designed for aptamer selections, FASTAptamer can be applied to any selection strategy that can utilize next-generation DNA sequencing, such as ribozyme or deoxyribozyme selections, in vivo mutagenesis and various surface display technologies (peptide, antibody fragment, mRNA, etc.). FASTAptamer software, sample data and a user's guide are available for download at http://burkelab.missouri.edu/fastaptamer.html.

High-throughput sequence analysis reveals structural diversity and improved potency among RNA inhibitors of HIV reverse transcriptase

Systematic evolution of ligands through exponential enrichment (SELEX) is a well-established method for generating nucleic acid populations that are enriched for specified functions. High-throughput sequencing (HTS) enhances the power of comparative sequence analysis to reveal details of how RNAs within these populations recognize their targets. We used HTS analysis to evaluate RNA populations selected to bind type I human immunodeficiency virus reverse transcriptase (RT). The populations are enriched in RNAs of independent lineages that converge on shared motifs and in clusters of RNAs with nearly identical sequences that share common ancestry. Both of these features informed inferences of the secondary structures of enriched RNAs, their minimal structural requirements and their stabilities in RT-aptamer complexes. Monitoring population dynamics in response to increasing selection pressure revealed RNA inhibitors of RT that are more potent than the previously identified pseudoknots. Improved potency was observed for inhibition of both purified RT in enzymatic assays and viral replication in cell-based assays. Structural and functional details of converged motifs that are obscured by simple consensus descriptions are also revealed by the HTS analysis. The approach presented here can readily be generalized for the efficient and systematic post-SELEX development of aptamers for down-stream applications.

Fitness analyses of all possible point mutations for regions of genes in yeast

Deep sequencing can accurately measure the relative abundance of hundreds of mutations in a single bulk competition experiment, which can give a direct readout of the fitness of each mutant. Here we describe a protocol that we previously developed and optimized to measure the fitness effects of all possible individual codon substitutions for 10-aa regions of essential genes in yeast. Starting with a conditional strain (i.e., a temperature-sensitive strain), we describe how to efficiently generate plasmid libraries of point mutants that can then be transformed to generate libraries of yeast. The yeast libraries are competed under conditions that select for mutant function. Deep-sequencing analyses are used to determine the relative fitness of all mutants. This approach is faster and cheaper per mutant compared with analyzing individually isolated mutants. The protocol can be performed in ∼4 weeks and many 10-aa regions can be analyzed in parallel.

Deep mutational scanning: assessing protein function on a massive scale

Analysis of protein mutants is an effective means to understand their function. Protein display is an approach that allows large numbers of mutants of a protein to be selected based on their activity, but only a handful with maximal activity have been traditionally identified for subsequent functional analysis. However, the recent application of high-throughput sequencing (HTS) to protein display and selection has enabled simultaneous assessment of the function of hundreds of thousands of mutants that span the activity range from high to low. Such deep mutational scanning approaches are rapid and inexpensive with the potential for broad utility. In this review, we discuss the emergence of deep mutational scanning, the challenges associated with its use and some of its exciting applications.

Rapid construction of empirical RNA fitness landscapes

Evolution is an adaptive walk through a hypothetical fitness landscape, which depicts the relationship between genotypes and the fitness of each corresponding phenotype. We constructed an empirical fitness landscape for a catalytic RNA by combining next-generation sequencing, computational analysis, and "serial depletion," an in vitro selection protocol. By determining the reaction rate constant for every point mutant of a catalytic RNA, we demonstrated that abundance in serially depleted pools correlates with biochemical activity (correlation coefficient r = 0.67, standard score Z = 7.4). Therefore, enumeration of each genotype by deep sequencing yielded a fitness landscape containing ~10(7) unique sequences, without requiring measurement of the phenotypic fitness for each sequence. High-throughput mapping between genotype and phenotype may apply to artificial selections, host-pathogen interactions, and other biomedically relevant evolutionary phenomena.