Biology subjects

Hayden, E. J.

Publications and source records attributed to Hayden, E. J..

7 recordsLinked to original sources

bioRxiv2025

High-throughput experimental validation of novel hairpin ribozymes

The small self-cleaving hairpin ribozyme has served as a model for RNA structure and function and has been engineered for biotechnology applications. Hairpin ribozymes were thought to be rare with only four known examples, which limited the interpretation of their biological importance and the starting sequences for engineering efforts. Recently, a bioinformatics approach identified hundreds of different RNA sequences in metatranscriptomic data that matched a novel permutation of the hairpin ribozyme. However, the self-cleavage activity of most of these sequences has not been experimentally demonstrated. Here, a high-throughput sequencing-based approach was used to evaluate the co-transcriptional self-cleavage activity of 855 different hairpin ribozymes in parallel. The results showed that nearly all sequences are very efficient self-cleaving ribozymes, and even rare nucleotides at highly conserved positions did not prevent observable ribozyme activity. The distribution of activity observed suggests that the metatranscriptomic sequences could contain random mutations from efficient wild-type ribozymes. The results further validate the bioinformatics approach that was used for ribozyme discovery and opens further questions about the biological roles of these ribozymes in the diverse environments where they were discovered.

biochemistry↗

bioRxiv2024

Expanding the space of self-reproducing RNAs using probabilistic generative models

Estimating the plausibility of RNA self-reproduction is central to origin-of-life scenarios but self-reproduction has been shown in only a handful of systems. Here, we populated a vast sequence space of ribozymes using statistical covariation models and secondary structure prediction. Experimentally assayed sequences were found active as far as 65 mutations from a reference natural sequence. The number of potentially generated sequences together with the experimental success rate indicate that at least [~]1039 such ribozymes may exist. Randomly sampled artificial ribozymes exhibited autocatalytic self-reproduction akin to the reference sequence. The combination of high-throughput screening and probabilistic modeling considerably improves our estimation of the number of self-reproducing systems, paving the way for a statistical approach to the origin of life.

biochemistry↗

bioRxiv2022

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

molecular biology↗

bioRxiv2022

RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence to function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heat-map based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

molecular biology↗

bioRxiv2020

A multiplexed, automated evolution pipeline enablesscalable discovery and characterization of biosensors

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

bioengineering↗

bioRxiv2019

Universal assay for measuring vertebrate telomeres by real-time quantitative PCR

Telomere length dynamics are an established biomarker of health and aging in animals. The study of telomeres in numerous species has been facilitated by methods to measure telomere length by real-time quantitative PCR (qPCR). In this method, telomere length is determined by quantifying the amount of telomeric DNA repeats in a sample and normalizing this to the total amount of genomic DNA. This normalization requires the development of genomic reference primers suitable for qPCR, which remains challenging in non-model organism with genomes that have not been sequenced. Here we report reference primers that can be used in qPCR to measure telomere lengths in any vertebrate species. We designed primer pairs to amplify genetic elements that are highly conserved between evolutionarily distant taxa and tested them in species that span the vertebrate tree of life. We report five primer pairs that meet the specificity and reproducibility standards of qPCR. In addition, we demonstrate how to choose the best primers for a given species by testing the primers on multiple individuals within a species and applying an established computational tool. These reference primers can facilitate the application of qPCR-based telomere length measurements in any vertebrate species of ecological or economic interest.

molecular biology↗

bioRxiv2018

Genotype network intersections promote evolutionary innovation

Evolutionary innovations are qualitatively novel traits that emerge through evolution and increase biodiversity. The genetic mechanisms that lead to innovation remain poorly understood. A systems view of innovation requires the analysis of genotype networks - the vast networks of genetic variants that produce the same phenotype. Innovations can occur at the intersection of two different genotype networks. Here, we use high-throughput sequencing to study the fitness landscape at the intersection of the genotype networks of two catalytic RNA molecules (ribozymes). We determined the ability of numerous neighboring RNA sequences to catalyze two different chemical reactions. We find extensive functional overlap, and over half the genotypes can catalyze both functions to some extent. We demonstrate through evolutionary simulations that these numerous points of intersection facilitate the discovery of a new function. Our study reveals the properties of a fitness landscape where genotype networks intersect, and the consequences for evolutionary innovations.

evolutionary biology↗