Advances and perspectives in aptamer arrays

Aptamers are oligonucleotides (typically 10–60 bases in length) capable of binding target ligands with affinities similar to antibodies. The generation of high density multiplexed aptamer arrays for molecular diagnostics was first proposed nearly ten years ago for the quantification of the thousands of proteins within biological samples, including blood and urine. The tagless aptameric detection of small molecular compounds extends the application of such arrays to bioanalyses at the metabolite level. We present here a minireview on some existing technologies and highlight recent innovations that are being applied to this field, which may facilitate the vision of highly multi-parallelized arrays for the quantitative analysis of biological systems

Analysis of aptamer sequence activity relationships

DNA sequences that can bind selectively and specifically to target molecules are known as aptamers. Normally such binding analyses are performed using soluble aptamers. However, there is much to be gained by using an on-chip or microarray format, where a large number of aptameric DNA sequences can be interrogated simultaneously. To calibrate the system, known thrombin binding aptamers (TBAs) have been mutated systematically, producing large populations that allow exploration of key structural aspects of the overall binding motif. The ability to discriminate between background noise and low affinity binding aptamers can be problematic on arrays, and we use the mutated sequences to establish appropriate experimental conditions and their limitations for two commonly used fluorescence-based detection methods. Having optimized experimental conditions, high-density oligonucleotide microarrays were used to explore the entire loop–sequence–functionality relationship creating a detailed model based on over 40 000 analyses, describing key features for quadruplex-forming sequences

Predictive models for population performance on real biological fitness landscapes

Motivation: Directed evolution, in addition to its principal application of obtaining novel biomolecules, offers significant potential as a vehicle for obtaining useful information about the topologies of biomolecular fitness landscapes. In this article, we make use of a special type of model of fitness landscapes—based on finite state machines—which can be inferred from directed evolution experiments. Importantly, the model is constructed only from the fitness data and phylogeny, not sequence or structural information, which is often absent. The model, called a landscape state machine (LSM), has already been used successfully in the evolutionary computation literature to model the landscapes of artificial optimization problems. Here, we use the method for the first time to simulate a biological fitness landscape based on experimental evaluation. Results: We demonstrate in this study that LSMs are capable not only of representing the structure of model fitness landscapes such as NK-landscapes, but also the fitness landscape of real DNA oligomers binding to a protein (allophycocyanin), data we derived from experimental evaluations on microarrays. The LSMs prove adept at modelling the progress of evolution as a function of various controlling parameters, as validated by evaluations on the real landscapes. Specifically, the ability of the model to ‘predict’ optimal mutation rates and other parameters of the evolution is demonstrated. A modification to the standard LSM also proves accurate at predicting the effects of recombination on the evolution

Convergent evolution to an aptamer observed in small populations on DNA microarrays

The development of aptamers on custom synthesized DNA microarrays, which has been demonstrated in recent publications, can facilitate detailed analyses of sequence and fitness relationships. Here we use the technique to observe the paths taken through sequence-fitness space by three different evolutionary regimes: asexual reproduction, recombination and model-based evolution. The different evolutionary runs are made on the same array chip in triplicate, each one starting from a small population initialized independently at random. When evolving to a common target protein, glucose-6-phosphate dehydrogenase (G6PD), these nine distinct evolutionary runs are observed to develop aptamers with high affinity and to converge on the same motif not present in any of the starting populations. Regime specific differences in the evolutions, such as speed of convergence, could also be observed

Analysis of a complete DNA-protein affinity landscape

Properties of biological fitness landscapes are of interest to a wide sector of the life sciences, from ecology to genetics to synthetic biology. For biomolecular fitness landscapes, the information we currently possess comes primarily from two sources: sparse samples obtained from directed evolution experiments; and more fine-grained but less authentic information from ‘in silico’ models (such as NK-landscapes). Here we present the entire protein-binding profile of all variants of a nucleic acid oligomer 10 bases in length, which we have obtained experimentally by a series of highly parallel on-chip assays. The resulting complete landscape of sequence-binding pairs, comprising more than one million binding measurements in duplicate, has been analysed statistically using a number of metrics commonly applied to synthetic landscapes. These metrics show that the landscape is rugged, with many local optima, and that this arises from a combination of experimental variation and the natural structural properties of the oligonucleotides

Aptamer evolution for array-based diagnostics

Aptamer evolution for array-based diagnostic

Gene-specific linear trends constrain transcriptional variability of the toll-like receptor signaling

Single-cell gene expression is inherently variable, but how this variability is controlled in response to stimulation remains unclear. Here, we use single-cell RNA-seq and single-molecule mRNA counting (smFISH) to study inducible gene expression in the immune toll-like receptor system. We show that mRNA counts of tumor necrosis factor a conform to a standard stochastic switch model, while transcription of interleukin-1b involves an additional regulatory step resulting in increased heterogeneity. Despite different modes of regulation, systematic analysis of single-cell data for a range of genes demonstrates that the variability in transcript count is linearly constrained by the mean response over a range of conditions. Mathematical modeling of smFISH counts and experimental perturbation of chromatin state demonstrates that linear constraints emerge through modulation of transcriptional bursting along with gene-specific relationships. Overall, our analyses demonstrate that the variability of the inducible single-cell mRNA response is constrained by transcriptional bursting