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A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors.

ABSTRACT: 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. Here, using aptamer-coupled ribozyme libraries and a ribozyme regeneration method, de novo rapid in vitro evolution of RNA biosensors (DRIVER) enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identify and validate biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors are applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors are 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.

SUBMITTER: Townshend B

PROVIDER: S-EPMC7933316 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Next generation sequencing technologies are becoming more accessible and affordable over the years, with entire genome sequences of several pathogens being deciphered in few hours. However, there is the need to analyze multiple genomes within a short time, in order to provide critical information about a pathogen of interest such as drug resistance, mutations and genetic relationship of isolates in an outbreak setting. Many pipelines that currently do this are stand-alone workflows and require huge computational requirements to analyze multiple genomes. We present an automated and scalable pipeline called BAGEP for monomorphic bacteria that performs quality control on FASTQ paired end files, scan reads for contaminants using a taxonomic classifier, maps reads to a reference genome of choice for variant detection, detects antimicrobial resistant (AMR) genes, constructs a phylogenetic tree from core genome alignments and provide interactive short nucleotide polymorphism (SNP) visualization across core genomes in the data set. The objective of our research was to create an easy-to-use pipeline from existing bioinformatics tools that can be deployed on a personal computer. The pipeline was built on the Snakemake framework and utilizes existing tools for each processing step: fastp for quality trimming, snippy for variant calling, Centrifuge for taxonomic classification, Abricate for AMR gene detection, snippy-core for generating whole and core genome alignments, IQ-TREE for phylogenetic tree construction and vcfR for an interactive heatmap visualization which shows SNPs at specific locations across the genomes. BAGEP was successfully tested and validated with Mycobacterium tuberculosis (n = 20) and Salmonella enterica serovar Typhi (n = 20) genomes which are about 4.4 million and 4.8 million base pairs, respectively. Running these test data on a 8 GB RAM, 2.5 GHz quad core laptop took 122 and 61 minutes on respective data sets to complete the analysis. BAGEP is a fast, calls accurate SNPs and an easy to run pipeline that can be executed on a mid-range laptop; it is freely available on: https://github.com/idolawoye/BAGEP.

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A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors.

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