DropSynth 2.0: high-fidelity multiplexed gene synthesis in emulsions

Background:

In the last two decades, the ability to rapidly sequence DNA with ever-decreasing costs has revolutionized biology, generating vast amounts of genomic data and unlocking new areas of biology. A variety of highly multiplexed assays taking advantage of high-throughput DNA synthesis with next-generation DNA sequencing to interrogate function of DNA variants, generalized as Multiplexed Assays for Variant Effects (MAVEs), have revealed new biological insights at an incredible pace and scale [Gasperini 2016]. However, the ability to generate gene-size (100s-1000s of DNA base pairs) libraries with high fidelity has limited the scale and scope of some functional assays. Previously, the Kosuri group described DropSynth, an inexpensive bead- and emulsion-based method to assemble gene-size DNA fragment from short oligo pools [Plesa and Sidore, 2018]. Here, they improve DropSynth to substantially increase the scale and fidelity, enabling even high-throughput generation of high-quality gene libraries.

 

Key findings:

A major improvement in DropSynth fidelity (the percentage of assemblies that perfectly match the designed sequence) came from optimization using high-fidelity polymerases, resulting in a ~5-fold improvement to a ~20% rate of perfect assemblies across multiple different codon usage libraries. Several other steps were tested for improved fidelity or to support the use of the high-fidelity polymerases, including buffer optimization, enzymatic mismatch correction, and suppression PCR. Finally, the scale of DropSynth was improved by expanding the on-bead barcode repertoire to enable 4-times as many assembly reactions to be carried out at once. Together, these technical optimizations represent a substantial improvement in the rate and cost of gene synthesis using the DropSynth technique.

 

Importance:

Gene synthesis can still be a rate- or cost-limiting step in the design and implementation of high-throughput functional assays. DropSynth initially developed a robust low-cost synthesis platform for variant library production, enabling what the authors described in their first manuscript as “broad mutational scanning”. However, the fidelity of assembly was still too low for some applications. DropSynth 2.0 greatly improved the scale and success of this technique, and illustrates the promise for further refinement to enable ever-greater assembly of gene libraries. One exciting area this promises to open is the functional interrogation of the rapidly-expanding gene databases generated by large-scale genome and metagenome sequencing efforts. For example, the ability to rapidly generate libraries of all predicted polyketide synthase and non-ribosomal peptide synthase domains (as just one example) from microbial metagenomes will be important to enable functional interrogation of the full gene content of often uncultured microbes.

 

Moving Forward / Questions for Authors:

• What and where are the major types of errors in gene synthesis with DropSynth? One might expect that errors will be more enriched at the overlap sequences than in the intervening sequence (where it should be limited to the rate of polymerase error), but the next-generation sequencing data should clarify that assumption. Additionally, what percentage of erroneous sequences have indels that result in early stop codons? If a substantial fraction of errors result in truncated proteins, functional selection (e.g. cloning in-frame with GFP to enable sorting of full-length variants) may be sufficient for downstream applications rather than requiring dial-out PCR or similar techniques when a high fraction of perfect assemblies is required.
• Is DropSynth primarily limited by number of oligos per gene, or by the length of the overall assembly? As private companies increase the length of oligo pools offered, what do the authors expect to be the limiting step in determining the size of genes that may be produced by DropSynth?
• There seem to be substantial differences between the two different codon libraries prepared. Does this reflect differences in assembly fidelity / efficiency, PCR bias during sequencing library preparation, or simply the variability between library assembly attempts?
• The example libraries are of near-uniform size, which enables size selection and efficient bulk suppression PCR. In a situation where many different genes are assembled of varying lengths (such as the domain-scale metagenome libraries described above), how efficient do the authors expect the bulk suppression PCR to be, or what other methods of removing low molecular weight and improperly assembled products might be used?

 

References:

• Gasperini M., Starita L., Shendure J. “The power of multiplexed functional analysis of genetic variants” 2016. Nature Protocols 11, 1782-1787
• Plesa C.*, Sidore A.M.*, Lubock N.B., Zhang D., Kosuri S. “Multiplexed gene synthesis in emulsions for exploring protein functional landscapes” 2018. Science 359(6373), 343-347

 

doi: https://doi.org/10.1242/prelights.13560

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