Rational Approach to Optimizing Conformation-Switching Aptamers for Biosensing Applications.

The utilization of structure-switching aptamers (SSAs) has enabled the development of novel sensing platforms for the sensitive and continuous detection of molecules. De novo development of SSAs, however, is complex and laborious. Here we describe a rational approach to SSA optimization that simultaneously improves aptamer binding affinity and introduces target-dependent conformation-switching for compatibility with real-world biosensor applications. Key structural features identified from NMR and computational modeling were used to optimize conformational switching in the presence of target, while large-scale, microarray-based mutation analysis was used to map regions of the aptamer permissive to mutation and identify combinations of mutations with stronger binding affinity. Optimizations were carried out in a relevant biofluid to ensure a seamless transition of the aptamer to a biosensing platform. Initial proof-of-concept for this approach is demonstrated with a cortisol binding aptamer but can easily be translated to other relevant aptamers. Cortisol is a hormone correlated with the stress response that has been associated with various medical conditions and is present at quantifiable levels in accessible biofluids. The ability to continuously track levels of stress in real-time via cortisol monitoring, which can be enabled by the aptamers reported here, is crucial for assessing human health and performance.

Engineering a Synthetic Dopamine-Responsive Riboswitch for In Vitro Biosensing.

The detection of chemicals using natural allosteric transcription factors is a powerful strategy for point-of-use molecular sensing, particularly using fieldable cell-free gene expression (CFE) systems. However, the reliance of detection schemes on characterized protein-based sensors limits the number of measurable analytes. One alternative solution to this issue is to develop new sensors by generating RNA aptamers against the target analyte and then incorporating them directly into a riboswitch scaffold for ligand-inducible genetic control of a reporter protein. However, this strategy has not generated more than a handful of successful portable cell-free molecular sensors. To address this gap, here we convert dopamine-binding aptamers into functional dopamine-sensing riboswitches that regulate gene expression in a freeze-dried CFE reaction. We then develop an assay for direct detection and semi-quantification of dopamine in human urine. We anticipate that this work will be broadly applicable for converting many in vitro-generated RNA aptamers into fieldable molecular diagnostics.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

A high-throughput pipeline for design and selection of peptides targeting the SARS-Cov-2 Spike protein.

Rapid design, screening, and characterization of biorecognition elements (BREs) is essential for the development of diagnostic tests and antiviral therapeutics needed to combat the spread of viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address this need, we developed a high-throughput pipeline combining in silico design of a peptide library specific for SARS-CoV-2 spike (S) protein and microarray screening to identify binding sequences. Our optimized microarray platform allowed the simultaneous screening of ~ 2.5 k peptides and rapid identification of binding sequences resulting in selection of four peptides with nanomolar affinity to the SARS-CoV-2 S protein. Finally, we demonstrated the successful integration of one of the top peptides into an electrochemical sensor with a clinically relevant limit of detection for S protein in spiked saliva. Our results demonstrate the utility of this novel pipeline for the selection of peptide BREs in response to the SARS-CoV-2 pandemic, and the broader application of such a platform in response to future viral threats.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Characterization of synthetic riboswitch in cell-free protein expression systems.

Riboswitches are RNA-based regulatory elements that utilize ligand-induced structural changes in the 5'-untranslated region of mRNA to regulate the expression of associated genes. The majority of synthetic riboswitches have been selected and tested in cell-based systems. Cell-free protein expression systems (CFPS) have several advantages for the development and testing of synthetic riboswitches, including eliminating interactions with complex cellular networks, and the decoupling of transcription and translation processes. To gain a better understanding of the riboswitch regulatory mechanism, to allow for more efficient riboswitch optimization and use for biosensing applications, we studied the performance of a theophylline-responsive synthetic riboswitch coupled with the superfolder green fluorescent protein (sfGFP) reporter gene in E. coli cellular extract and PURE cell-free systems. To monitor the mRNA dynamics, a malachite green aptamer sequence was added to the 3'-untranslated region of sfGFP mRNA. Performance of the theophylline riboswitch was compared with a constitutively expressed sfGFP (control). Transcription dynamics of the riboswitch mRNA was very similar to the transcription of the control mRNA for all theophylline concentrations tested in both E. coli extract and PURE CFPS. However, sfGFP expression in the riboswitch construct was one order of magnitude lower, even at the highest concentration of theophylline. A mathematical model of riboswitch activation governed by the kinetic trapping mechanism was developed. Two factors - a reduced fraction of mRNA in the 'ON' state and a considerably lower translation initiation rate in the riboswitch - contribute to the much lower level of protein expression in the theophylline riboswitch compared to the control construct.

In Silico Assessment of Acute Oral Toxicity for Mixtures.

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains in vivo acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure-Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.

In Silico Discovery and Validation of Neuropeptide-Y-Binding Peptides for Sensors.

Wearable sensors for human health, performance, and state monitoring, which have a linear response to the binding of biomarkers found in sweat, saliva, or urine, are of current interest for many applications. A critical part of any device is a biological recognition element (BRE) that is able to bind a biomarker at the surface of a sensor with a high affinity and selectivity to produce a measurable signal response. In this study, we discover and compare 12-mer peptides that bind to neuropeptide Y (NPY), a stress and human health biomarker, using independent and complimentary experimental and computational approaches. The affinities of the NPY-binding peptides discovered by both methods are equivalent and below the micromolar level, which makes them suitable for application in sensors. The in silico design protocol for peptide-based BREs is low cost, highly efficient, and simple, suggesting its utility for discovering peptide binders to a variety of biomarker targets.

Microarrays as Model Biosensor Platforms to Investigate the Structure and Affinity of Aptamers.

Immobilization of nucleic acid aptamer recognition elements selected free in solution onto the surface of biosensor platforms has proven challenging. This study investigated the binding of multiple aptamer/target pairs immobilized on a commercially available microarray as a model system mimicking biosensor applications. The results indicate a minimum distance (linker length) from the surface and thymine nucleobase linker provides reproducible binding across varying conditions. An indirect labeling method, where the target was labeled with a biotin followed by a brief Cy3-streptavidin incubation, provided a higher signal-to-noise ratio and over two orders of magnitude improvement in limit of detection, compared to direct Cy3-protein labeling. We also showed that the affinities of the aptamer/target interaction can change between direct and indirect labeling and conditions to optimize for the highest fluorescence intensity will increase the sensitivity of the assay but will not change the overall affinity. Additionally, some sequences which did not initially bind demonstrated binding when conditions were optimized. These results, in combination with studies demonstrating enhanced binding in nonselection buffers, provided insights into the structure and affinity of aptamers critical for biosensor applications and allowed for generalizations in starting conditions for researchers wishing to investigate aptamers on a microarray surface.

Single-Round Patterned DNA Library Microarray Aptamer Lead Identification.

A method for identifying an aptamer in a single round was developed using custom DNA microarrays containing computationally derived patterned libraries incorporating no information on the sequences of previously reported thrombin binding aptamers. The DNA library was specifically designed to increase the probability of binding by enhancing structural complexity in a sequence-space confined environment, much like generating lead compounds in a combinatorial drug screening library. The sequence demonstrating the highest fluorescence intensity upon target addition was confirmed to bind the target molecule thrombin with specificity by surface plasmon resonance, and a novel imino proton NMR/2D NOESY combination was used to screen the structure for G-quartet formation. We propose that the lack of G-quartet structure in microarray-derived aptamers may highlight differences in binding mechanisms between surface-immobilized and solution based strategies. This proof-of-principle study highlights the use of a computational driven methodology to create a DNA library rather than a SELEX based approach. This work is beneficial to the biosensor field where aptamers selected by solution based evolution have proven challenging to retain binding function when immobilized on a surface.

Integrating and amplifying signal from riboswitch biosensors.

Biosensors offer a built-in energy supply and inherent sensing machinery that when exploited correctly may surpass traditional sensors. However, biosensor systems have been hindered by a narrow range of ligand detection capabilities, a relatively low signal output, and their inability to integrate multiple signals. Integration of signals could increase the specificity of the sensor and enable detection of a combination of ligands that may indicate environmental or developmental processes when detected together. Amplifying biosensor signal output will increase detector sensitivity and detection range. Riboswitches offer the potential to widen the diversity of ligands that may be detected, and advances in synthetic biology are illuminating myriad possibilities in signal processing using an orthogonal parts-based engineering approach. In this chapter, we describe the design, building, and testing of a riboswitch-based Boolean logic AND gate in bacteria, where an output requires the activation of two riboswitches, and the biological circuitry required to amplify the output of the AND gate using natural extracellular bacterial communication signals to "wire" cells together.

Tunable stringency aptamer selection and gold nanoparticle assay for detection of cortisol.

The first-known aptamer for the stress biomarker cortisol was selected using a tunable stringency magnetic bead selection strategy. The capture DNA probe immobilized on the beads was systematically lengthened to increase the number of bases bound to the complementary pool primer regions following selection enrichment. This resulted in a single sequence (15-1) dominating the final round 15 pool, where the same sequence was the second-highest copy number candidate in the enriched pool with the shorter capture DNA probe (round 13). A thorough analysis of the next-generation sequencing results showed that a high copy number may only correlate with enhanced affinity under certain stringency and enrichment conditions, in contrast with prior published reports. Aptamer 15-1 demonstrated enhanced binding to cortisol (K(d) = 6.9 ± 2.8 µM by equilibrium dialysis; 16.1 ± 0.6 µM by microscale thermophoresis) when compared with the top sequence from round 13 and the negative control progesterone. Whereas most aptamer selections terminate at the selection round demonstrating the highest enrichment, this work shows that extending the selection with higher stringency conditions leads to lower amounts eluted by the target but higher copy numbers of a sequence with enhanced binding. The structure-switching aptamer was applied to a gold nanoparticle assay in buffer and was shown to discriminate between cortisol and two other stress biomarkers, norepinephrine and epinephrine, and a structurally analogous biomarker of liver dysfunction, cholic acid. We believe this approach enhances aptamer selection and serves as proof-of-principle work toward development of point-of-care diagnostics for medical, combat, or bioterrorism targets.

Computational design of RNA libraries for in vitro selection of aptamers.

Selection of aptamers that bind a specific ligand usually begins with a random library of RNA sequences, and many aptamers selected from such random pools have a simple stem-loop structure. We present here a computational approach for designing a starting library of RNA sequences with increased formation of complex structural motifs and enhanced affinity to a desired target molecule. Our approach consists of two steps: (1) generation of RNA sequences based on customized patterning of nucleotides with increased probability of forming a base pair and (2) a high-throughput virtual screening of the generated library to select aptamers with binding affinity to a small-molecule target. We developed a set of criteria that allows one to select a sequence with potential binding affinity from a pool of random sequences and designed a protocol for RNA 3D structure prediction. The proposed approach significantly reduces the RNA sequence search space, thus accelerating the experimental screening and selection of high-affinity aptamers.

FRET-based optical assay for selection of artificial riboswitches.

Artificial riboswitches are engineered to regulate gene expression in response to a variety of non-endogenous small molecules and, therefore, can be useful tools to reprogram cellular behavior for different applications. A new synthetic riboswitch can be created by linking an in vitro-selected aptamer with a randomized expression platform followed by in vivo selection and screening. Here, we describe an in vivo selection and screening technique to discover artificial riboswitches in E. coli cells that is based on TEV protease-FRET substrate reporter system.

Detection of orexin A neuropeptide in biological fluids using a zinc oxide field effect transistor.

Biomarkers which are indicative of acute physiological and emotional states are studied in a number of different areas in cognitive neuroscience. Currently, many cognitive studies are conducted based on programmed tasks followed by timed biofluid sampling, central laboratory processing, and followed by data analysis. In this work, we present a sensor platform capable of rapid biomarker detection specific for detecting neuropeptide orexin A, found in blood and saliva and known as an indicator of fatigue and cognitive performance. A peptide recognition element that selectively binds to orexin A was designed, characterized, and functionalized onto a zinc oxide field effect transistor to enable rapid detection. The detection limit using the sensor platform was sub-picomolar in water, and picomolar to nanomolar levels in saliva and serum. The transistor and recognition element sensor platform can be easily expanded, allowing for multiple biomarkers to be detected simultaneously, lending itself to complex biomarker analysis applicable to rapid feedback for neuroscience research and physiological monitoring.

Development of a 2,4-dinitrotoluene-responsive synthetic riboswitch in E. coli cells.

Riboswitches are RNA sequences that regulate expression of associated downstream genes in response to the presence or absence of specific small molecules. A novel riboswitch that activates protein translation in E. coli cells in response to 2,4-dinitrotoluene (DNT) has been engineered. A plasmid library was constructed by incorporation of 30 degenerate bases between a previously described trinitrotoluene aptamer and the ribosome binding site. Screening was performed by placing the riboswitch library upstream of the Tobacco Etch Virus (TEV) protease coding sequence in one plasmid; a second plasmid encoded a FRET-based construct linked with a peptide containing the TEV protease cleavage site. Addition of DNT to bacterial culture activated the riboswitch, initiating translation of TEV protease. In turn, the protease cleaved the linker in the FRET-based fusion protein, causing a change in fluorescence. This new riboswitch exhibited a 10-fold increase in fluorescence in the presence of 0.5 mM DNT compared to the system without target.

Stochastic simulation and analysis of biomolecular reaction networks.

BACKGROUND: In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not received much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. RESULTS: Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. CONCLUSION: The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior.