Lubock, N. B. | bioRxiv Science

bioRxiv2018

A Scalable, Multiplexed Assay for Decoding Receptor-Ligand Interactions

Chemicals such as drugs, hormones, and odorants can have many potential interactions with endogenous targets, and uncovering these relationships is critical for understanding and modulating function. Mammalian olfactory receptors (ORs), a large family of G protein-coupled receptors, mediate olfaction through activation by small molecules. Each OR can respond to many odorants, and vice versa, making exploring this space one interaction at a time difficult. We developed a high-throughput receptor screening platform in human cell lines to screen libraries of chemicals against a multiplexed library of receptors using next-generation sequencing of barcoded genetic reporters. We screened three concentrations of 181 odorants, where in each well we record the activity of 39 ORs simultaneously, and identified 79 novel associations, including ligands for 15 orphan receptors. This platform allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale.

biochemistry↗

bioRxiv2017

A systematic comparison of error correction enzymes by next-generation sequencing

Gene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality, and cost of gene synthesis is limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment and throughput. Here we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in a model gene assembly and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G [->] G/C transversions whereas T7 Endonuclease I preferentially corrects A/T [->] T/A transversions. More generally, this experimental and computational pipeline is a fast, scalable, and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.

synthetic biology↗